An Automatic Version of the Post-Laryngectomy Telephone Test - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Toy, Hikmet; Rosanowski, Frank (Proc. Text, Speech and Dialogue; 10th International Conference, 2007)

A Neural Network Simulation Environment with Components for Genetic Algorithms and Fuzzy Logic - Wilke, Peter (1994)

An Evaluation of Popular Copy-Move Forgery Detection Approaches - Christlein, Vincent; Riess, Christian; Jordan, Johannes Michael; Riess, Corinna; Angelopoulou, Elli (IEEE Transactions on Information Forensics and Security, 2012)
A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and ZERNIKE features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.
Keywords: Benchmark dataset; copy-move forgery; comparative study; manipulation detection; image forensics

An evaluation system for stereopsis of beach volleyball players measuring perception time as a function of disparity within a virtual environment - Paulus, Jan; Hornegger, Joachim; Schmidt, Michael; Eskofier, Björn; Michelson, Georg (Journal of Vision, 2013)

A new approach to the evaluation of forming limits in sheet metal forming - Merklein, Marion; Maier, Andreas; Kinnstätter, Daniel; Jaremenko, Christian; Affronti, Emanuela (Key Engineering Materials, 2015)
UnivIS-Import:2015-04-14:Pub.2015.tech.IMMD.IMMD5.anewap

A new Method for MRI Intensity Standardization with Application to Lesion Detection in the Brain - Jäger, Florian; Deuerling-Zheng, Yu; Frericks, Bernd; Wacker, Frank; Hornegger, Joachim (Vision Modeling and Visualization 2006, 2006)

A New Scale Space Total Variation Algorithm for Limited Angle Tomography - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography), 2016)
This paper proposes a scale space total variation (ssTV) algorithm to reduce large scale streaks in limited angle tomography. The weighted total variation (wTV) algorithm is able to remove most small scale streaks. However, it fails to reduce larger streaks since total variation (TV) regularization is scale-dependent and may regard them as homogeneous areas. Derived from the wTV algorithm, the proposed ssTV algorithm applies wTV regularization on the image at different scales using down-sampling and up-sampling operations and thus can reduce streaks more effectively. Advantages of the ssTV algorithm are demonstrated on both 2-D numerical data and a 3-D clinical dataset.<br
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.anewsc_3
Keywords: limited angle tomography; total variation; scale space; streak artifacts

A new scheme for view-dependent data differentiation in fan-beam and cone-beam computed tomography - Noo, Frédéric; Hoppe, Stefan; Dennerlein, Frank; Lauritsch, Günter; Hornegger, Joachim (Physics in Medicine and Biology, 2007)
In computed tomography, analytical fan-beam (FB) and cone-beam (CB) image reconstruction often involves a view-dependent data differentiation. The implementation of this differentiation step is critical in terms of resolution and image quality. In this work, we present a new differentiation scheme that is robust to changes in the data acquisition geometry and to coarse view sampling. Our scheme was compared to two previously suggested methods, which we call the direct scheme and the chain-rule scheme. Image reconstructions were performed from computer-simulated data of the Shepp-Logan phantom, the FORBILD thorax phantom and a modified FORBILD head phantom. For FB reconstruction, we investigated three acquisition geometries: a circular, an ellipse-shaped and a square-shaped trajectory. For CB reconstruction, the circle-plus-line trajectory was considered. Image comparison showed that the new scheme performs consistently well when varying the scenario, in both FB and CB geometry, unlike the other two schemes. © 2007 IOP Publishing Ltd.

A NEW WEIGHTED ANISOTROPIC TOTAL VARIATION ALGORITHM FOR LIMITED ANGLE TOMOGRAPHY - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
This paper addresses streak reduction in limited angle tomography. The weighted total variation (wTV) algorithm is able to remove most Poisson noise and small streaks for clinical limited angle data. However, it is still not sufficient to remove larger streaks whose orientations are mostly dependent on the scan trajectory. We propose a new weighted anisotropic total variation (waTV) algorithm, which uses four neighboring pixels to calculate the gradient along the streaks' normal direction and is able to reduce large streaks well.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.anewwe
Keywords: limited angle tomography; streak artifacts; Total variation

An Extension to the Sammon Mapping for the Robust Visualization of Speaker Dependencies - Maier, Andreas; Exner, Julian; Steidl, Stefan; Batliner, Anton; Haderlein, Tino; Nöth, Elmar (Text, Speech and Dialogue, 2008)

ANIMAL‐SPOT enables animal‐independent signal detection and classification using deep learning - Bergler, Christian; Smeele, Simeon Q.; Tyndel, Stephen A.; Barnhill, Alexander; Ortiz, Sara T.; Kalan, Ammie K.; Cheng, Rachael Xi; Brinkløv, Signe; Osieka, Anna N.; Tougaard, Jakob; Jakobsen, Freja; Wahlberg, Magnus; et al. (Scientific Reports, 2022)
Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human- annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience.<br

An Improved Extrapolation Scheme for Truncated CT Data Using 2D Fourier-Based Helgason-Ludwig Consistency Conditions - Xia, Yan; Berger, Martin; Bauer, Sebastian; Hu, Shiyang; Aichert, André; Maier, Andreas (International Journal of Biomedical Imaging, 2017)
We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency conditions that mathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier representation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallel-beam and fan-beam imaging geometry. The derivation result indicates that there is a zero energy region forming a double-wedge shape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature. The major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform (FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the parameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be used to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data. Results show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method.
UnivIS-Import:2018-09-05:Pub.2017.tech.IMMD.IMMD5.animpr_2
Keywords: Truncation; CT; Helgason-Ludwig; Fourier-Consistency; Consistency Conditions

An information theoretic approach for next best view planning in 3-D reconstruction - Wenhardt, Stefan; Deutsch, Benjamin; Hornegger, Joachim; Niemann, Heinrich; Denzler, Joachim (18th International Conference on Pattern Recognition, ICPR 2006, 2006)
We present an algorithm for optimal view point selection for 3-D reconstruction of an object using 2-D image points. Since the image points are noisy, a Kalman filter is used to obtain the best estimate of the object's geometry. This Kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. By choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error. We verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object. © 2006 IEEE.

An Innovative, Safe and Sterile Sigmoid Access for NOTES (ISSA) - Wilhelm, Dirk; Penne, Jochen; Meining, Alexander; Hornegger, Joachim; Feußner, Hubertus (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

An Integrated System for MR-Guided Thermal Ablations: From Planning to Real-Time Temperature Monitoring - Rothgang, Eva; Gilson, Wesley D.; Pan, Li; Roland, Jörg; Kirchberg, Klaus; Wacker, Frank; Hornegger, Joachim; Lorenz, Christine H. (Proceedings of International Society for Magnetic Resonance in Medicine, 2012)

An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process - Bayer, Siming; Spiske, Ute; Geimer, Tobias; Luo, Jie; Wells III, William M.; Ostermeier, Martin; Fahrig, Rebecca; Arya, Nabavi; Bert, Christoph; Eyüpoglu, Ilker Yasin; Maier, Andreas (Algorithmen – Systeme – Anwendungen. Proceedings des Workshops, 2020)
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.<br

Anisotropic hidden Markov random field modeling for unsupervised MRI brain tissue segmentation and brain tumor detection - Wels, Michael; Staatz, Gundula; Rossi, Andrea; Huber, Martin; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery Volume 2 Supplement 1 (Int J CARS (2007) (Suppl 1)) CARS 2007 Computer Assisted Radiology and Surgery Proceedings of the 21st International Congress and Exhibition, 2007)
UnivIS-Import:2015-04-16:Pub.2007.tech.IMMD.IMMD5.anisot

Anisotropic hidden Markov random field modeling for unsupervised MRI brain tissue segmentation and brain tumor detection - Wels, Michael; Staatz, Gundula; Rossi, Andrea; Huber, Martin; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2007)

An iterative approach of local 3d-reconstruction of non-rigid movements of heart region from many angiographics views - Bouattour, Sahla; Heigl, Benno; Paulus, Dietrich; Hornegger, Joachim (Vision, Modeling, and Visualization 2003, 2003)

An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy - Fischer, Peter; Faranesh, Anthony; Pohl, Thomas; Maier, Andreas; Rogers, Toby; Ratnayaka, Kanishka; Lederman, Robert; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2018)
In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.anmrba
Keywords: mri; x-ray imaging; motion compensation; electrocardiography; motion model

ANN-based Alzheimer’s disease classification from bag of words - Klumpp, Philipp; Fritsch, Julian David; Nöth, Elmar (Speech Communication - 13. ITG-Fachtagung Sprachkommunikation, 2018)
Alzheimer’s disease (AD) is the most frequent cause of dementia and the patient numbers are increasing within an aging society. Prior research has shown that AD significantly affects the speech signal, and many approaches were published on how to detect AD from only speech or spoken text information. In an earlier work, we have proven the reliability of language models to statistically evaluate transcriptions from AD and healthy control participants. Based on these results, we propose the approach of counting word occurrences in transcriptions, storing them in a bag of words (BoW) vector, and using this vector as an input into an artificial neural network which classifies between AD and healthy state. It could be shown that the new method reached very similar results compared to the language model classifiers, although information about the word order was omitted.<br

ANN-based Alzheimer's disease classification from bag of words - Klumpp, Philipp; Fritsch, Julian David; Nöth, Elmar (Speech Communication - 13th ITG-Fachtagung Sprachkommunikation, 2020)
Alzheimer's disease (AD) is the most frequent cause of dementia and the patient numbers are increasing within an aging society. Prior research has shown that AD significantly affects the speech signal, and many approaches were published on how to detect AD from only speech or spoken text information. In an earlier work, we have proven the reliability of language models to statistically evaluate transcriptions from AD and healthy control participants. Based on these results, we propose the approach of counting word occurrences in transcriptions, storing them in a bag of words (BoW) vector, and using this vector as an input into an artificial neural network which classifies between AD and healthy state. It could be shown that the new method reached very similar results compared to the language model classifiers, although information about the word order was omitted.
CRIS-Team Scopus Importer:2020-11-27

A Non-linear Dynamics Approach to Classify Gait Signals of Patients with Parkinson's Disease - Pérez Toro, Paula Andrea; Camilo Vasquez-Correa, Juan; Arias-Vergara, Tomas; Garcia-Ospina, Nicanor; Rafael Orozco-Arroyave, Juan; Nöth, Elmar (APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT II, 2018)
CRIS-Team WoS Importer:2019-09-06

A nonlinear projection scheme for fast rigid registration - Daum, Volker; Hahn, Dieter; Hornegger, Joachim (2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC, 2007)
Intensity based rigid registration algorithms commonly employed for medical image fusion are based on the iterative optimization of a pixel-by-pixel distance measure defined on the images. As medical images grow larger in size due to advanced scanner technology, evaluating such similarity measures is no longer computationally efficient. In order to overcome the inherent limitations of the standard approach we propose a new, nonlinear projection scheme that enables a very fast evaluation of the distance between two images. Current state-of-the-art projection schemes decompose a six dimensional search space into three dimensional subspaces. The proposed approach, however, yields a complete decomposition into ID subspaces. The optimization on these subspaces is highly efficient and does not require a reprojection. This scheme is therefore suitable for 2D and 3D registrations, and it is able to cope with subvolume matching problems. Furthermore, the use of modern graphics hardware allows for a highly efficient implementation. Experiments show that computation times can be reduced to less than 10 seconds with the proposed approach for 256 sized volumes. © 2007 IEEE.

A novel approach to 3-D Endoscopy in Minimally Invasive Surgery - Krüger, Sophie; Penne, Jochen; Hornegger, Joachim; Hohenberger, Werner; Horbach, Thomas (Abstractband curac 2006, 2006)

A Novel Filtered Backprojection-Based Algorithm for Sparse View CT Image Reconstruction - Wu, Meng; Maier, Andreas; Yang, Qiao; Fahrig, Rebecca (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.anovel_3

A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data - Jordan, Johannes Michael; Angelopoulou, Elli; Maier, Andreas (Journal of Electrical and Computer Engineering, 2016)
Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.
CRIS-Team Scopus Importer:2022-08-05

A novel image based verification method for respiratory motion management in radiation therapy - Khamene, Ali; Schaller, Christian; Hornegger, Joachim; Celi, Juan Carlos; Ofstad, Barbara; Rietzel, Eike; Li, X. Allen; Tai, An; Bayouth, John (2007 IEEE 11th International Conference on Computer Vision, ICCV, 2007)
Precise localization of moving targets is essential to increase local control of the cancer via dose escalation while reducing the severity of normal tissue complication. Localization of targets in real time with radio-opaque marker is less favorable considering the excess radiation dose to the patient and potential complications of implantation. Various external surrogates could provide indications of the targets' positions during the breathing process. However, there is a great deal of uncertainty in the correlation between external surrogates and internal target positions/trajectory during respiratory cycles. In order to address this problem, we have developed an algorithm that automatically establishes correspondences between the fluoroscopic sequence frames taken from the patient on the day of treatment and the various phases of a 4DCT planning data set. Image based mapping/synchronization procedure is performed using an underlying Markov model established for the breathing process. The mapping procedure is formulated as an optimization process and is solved efficiently using a dynamic programming technique. Results on the phantom, synthetic, and real patient data demonstrate the effectiveness of the proposed method in coping with respiratory correlation variations. The approach could primarily be used for automatic gating interval adaptation in the gated radiotherapy. ©2007 IEEE.

A Novel Lecture Browser Using Key Phrases and Stream Graphs - Riedhammer, Korbinian Thomas; Nöth, Elmar; Gropp, Martin (Proc. ASRU 2011, 2011)

A Novel Lecture Browsing System using Ranked Key Phrases and StreamGraphs - Gropp, Martin; Nöth, Elmar; Riedhammer, Korbinian Thomas (Proceedings of TSD2011, 2011)

A novel probabilistic model for object recognition and pose estimation - Hornegger, Joachim; Niemann, Heinrich (International Journal of Pattern Recognition and Artificial Intelligence, 2001)
In this paper we consider the problem of object recognition and localization in a probabilistic framework. An object is represented by a parametric probability density, and the computation of pose parameters is implemented as a nonlinear parameter estimation problem. The presence of a probabilistic model allows for recognition according to Bayes rule. The introduced probabilistic model requires no prior segmentation but characterizes the statistical properties of observed intensity values in the image plane. A detailed discussion of the applied theoretical framework is followed by a concise experimental evaluation which demonstrates the benefit of the proposed approach.

A Novel Trilateral Filter for Digital Subtraction Angiography - Tripathi, Purvi; Obler, Richard; Maier, Andreas; Janssen, Hendrik (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Anterior Segment Angiography with 1050 nm Swept-Source Optical Coherence Tomography - Li, Yan; Lu, Chen D.; Jia, Yali; Lee, Byungkun; Kraus, Martin F.; Hornegger, Joachim; Fujimoto, James G.; Huang, David (Investigative Ophthalmology & Visual Science, 2015)

Aortic and Mitral Valve Modeling from Multi-Modal Image Data - Voigt, Ingmar; et al. (Medical Image Recognition, Segmentation and Parsing, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.aortic

A parallel K-SVD implementation for CT image denoising - Bartuschat, Dominik; Borsdorf, Anja; Köstler, Harald; Rubinstein, R.; Stürmer, Markus (2009)
UnivIS-Import:2016-06-30:Pub.2009.tech.IMMD.lsinfs.aparal

A Passive, Image-Based Navigation Tool for Real-Time MR-Guided Percutaneous Interventionoal Procedures - Strehl, Wilhelm; Rothgang, Eva; Gilson, Wesley D.; Hornegger, Joachim; Lorenz, Christine H. (Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM), 2010)

A phantom study on dose efficiency for orthopedic applications: Comparing slot-scanning radiography using ultra-small-angle tomosynthesis to conventional radiography - Luckner, Christoph; Weber, Thomas; Herbst, Magdalena; Ritschl, Ludwig; Kappler, Steffen; Maier, Andreas (Medical Physics, 2020)
Purpose: This paper studies the abilities of a twin-robotic x-ray slot-scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography. Methods: We investigate the dose saving capabilities, especially in terms of the signal- and the contrast-to-noise ratio, as well as the scatter-to-primary ratio of the proposed slot-scanning method in comparison to the state-of-the-art method for length-extended imaging. As a baseline, we use x-ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose. To obtain an estimate of the photon-related noise directly from an x-ray image, we implement a Poisson-Gaussian noise model. This model is used to compare the dose efficiency of two settings and combined with the well-known (Formula presented.) to determine the transmission parameters. We present a method with an associated measurement protocol, utilizing the robotic capabilities of the used system to automatically obtain quasi-scatter-free ground-truth data with exact geometric correspondence to full-field and slot acquisitions. In total, we investigate two body regions (thoracic spine and lumbar spine) in anterior-posterior view with two patient sizes (BMI = 22 and 30) in two acquisition modes (conventional and slot scan with a flat-panel detector) with and without anti-scatter grid using an anthropomorphic upper-body phantom. Results: We have shown that it is feasible to combine the proposed approach with the (Formula presented.) for the determination of scatter rejection parameters. The use of an anti-scatter grid is indicated for full-field acquisitions allowing for dose savings up to 46% compared to their gridless counterparts. When changing the acquisition mode to the investigated slot scan, the use of an anti-scatter grid has no major impact on the image quality in terms of dose efficiency, in particular for patients with a BMI of 22. However, an increased contrast improvement factor was found. For normal-sized patients, up to 53% of dose can be saved additionally in comparison to full-field acquisitions with grid. Moreover, we could demonstrate that a slot size of 5 cm and air gap of 10 cm is sufficient to achieve scatter-to-primary ratios, which are equal or better compared to those of the full-field acquisitions with a grid. Conclusions: We have shown, that the slot-scanning approach is always superior to the conventional full-field acquisition in terms of signal-to-noise and scatter-to-primary ratios. Compared to the state-of-the-art acquisition protocols with a grid, dose savings up to 53% are possible due to the scatter rejection without compromising the SNR. Hence, the use of the slot-scanning method is indicated, especially when it comes to regularly carried-out follow-up acquisitions, for example, in the case of scoliosis monitoring.
CRIS-Team Scopus Importer:2021-04-19
Keywords: dose; orthopedics; slot-scanning; SNR; tomosynthesis

A Phonetic Similarity Based Noisy Channel Approach to ASR Hypothesis Re-Ranking and Error Detection - Hacker, Martin; Nöth, Elmar (Proceedings of ICASSP 2014, 2014)
We present a new method to augment the correct transcript from automatic speech recognition (ASR) output containing multiple hypotheses. The error-prone ASR process is taken as black box and modeled as a noisy channel on phoneme level. The probabilities of the individual phoneme errors are assigned according to phonetic confusability. We score potential candidate hypotheses by their posterior probability of being the channel input given the competing ASR hypotheses as observed output. The resulting scores provide useful information not included in traditional confidence measures. We investigated the usefulness of the method for rescoring, re-ranking and word error detection. The method alone is not powerful enough to improve the recognition results, but by employing a decision tree classifier it is possible to isolate cases where the method works very well. Our results show that the combination with other knowledge sources and postprocessing techniques can lead to promising improvements. © 2014 IEEE.
UnivIS-Import:2015-04-17:Pub.2014.tech.IMMD.lehstu.agdigi.aphone
Keywords: Automatic speech recognition; confidence; error detection; error modeling; re-ranking

A pilot study of OCT angiography of iris melanomas - Skalet, Alison; Li, Yan; Lu, Chen D.; Jia, Yali; Lee, Byungkun; Hornegger, Joachim; Fujimoto, James G.; Huang, David (Investigative Ophthalmology & Visual Science, 2015)

Apkinson — A Mobile Monitoring Solution for Parkinson's Disease - Klumpp, Philipp; Janu, Thomas; Arias Vergara, Tomás; Vasquez Correa, Juan; Rafael Orozco-Arroyave, Juan; Nöth, Elmar (Interspeech 2017, 2017)
In this paper we want to present our work on a smartphone application which aims to provide a mobile monitoring solution for patients suffering from Parkinson’s disease. By unobtrusively analyzing the speech signal during phone calls and with a dedicated speech test, we want to be able to determine the severity and the progression of Parkinson’s disease for a patient much more frequently than it would be possible with regular check-ups. The application consists of four major parts. There is a phone call detection which triggers the whole processing chain. Secondly, there is the phone call recording which has proven to be more challenging than expected. The signal analysis, another crucial component, is still in development for the phone call analysis. Additionally, the application collects several pieces of meta information about the calls to put the results into deeper context. After describing how the speech signal is affected by Parkinson’s disease, we sketch the overall application architecture and explain the four major parts of the current implementation in further detail. We then present the promising results achieved with the first version of a dedicated speech test. In the end, we outline how the project could receive further improvements in the future.<br
Keywords: speech monitoring; pathological speech; Parkinson’s disease

Apkinson: A mobile solution for multimodal assessment of patients with Parkinson's disease - Vásquez-Correa, J. C.; Klumpp, Philipp; Arias-Vergara, T.; Strauss, Martin; Küderle, Arne; Roth, Nils; Bayerl, S.; García-Ospina, N.; Pérez Toro, Paula Andrea; Parra-Gallego, L. F.; Rios-Urrego, C. D.; Escobar-Grisales, D.; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Parkinson's disease is a neurological disorder that produces different motor impairments in the patients. The longitudinal assessment of the neurological state of patients is important to improve their quality of life. We introduced Apkinson, a smartphone application to evaluate continuously the speech and movement deficits of Parkinson's patients, who receive feedback about their current state after performing different exercises. The speech assessment considers phonation, articulation, and prosody capabilities of the patients. Movement exercises captured with the inertial sensors of the smartphone evaluated symptoms in the upper and lower limbs.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Articulation; Longitudinal Assessment; Parkinson's Disease; Phonation; Prosody

Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement - Orozco-Arroyave, Juan Rafael; Vasquez Correa, Juan; Klumpp, Philipp; Pérez Toro, Paula Andrea; Escobar-Grisales, Daniel; Roth, Nils; Ríos-Urrego, Cristian David; Strauß, Martin; Carvajal-Castaño, Helber Andrés; Bayerl, Sebastian; Castrillón-Osorio, Luis Reinel; Arias-Vergara, Tomas; et al. (Neurodegenerative Disease Management, 2020)
Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson's disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.
CRIS-Team Scopus Importer:2020-07-03
Keywords: automatic monitoring; finger tapping; gait; hand movement; mobile application; motor evaluation; Parkinson’s disease; speech

A Portable Intra-Operative Framework Applied to Distal Radius Fracture Surgery - Magaraggia, Jessica; Wei, Wei; Weiten, Markus; Kleinszig, Gerhard; Vetter, Sven; Franke, Jochen; Barth, Karl; Angelopoulou, Elli; Hornegger, Joachim (MICCAI 2015, 2015)
Fractures of the distal radius account for about 15% of all extremity fractures. To date, open reduction and internal plate fixation is the standard operative treatment. During the procedure, only fluoroscopic images are available for the planning of the screw placement and the monitoring of the instrument trajectory. Complications arising from malpositioned screws can lead to revision surgery. With the aim of improving screw placement accuracy, we present a prototype framework for fully intra-operative guidance that simplifies the planning transfer. Planning is performed directly intra-operatively and expressed in terms of screw configuration w.r.t the used implant plate. Subsequently, guidance is provided solely by a combination of locally positioned markers and a small camera placed on the surgical instrument that allows real-time position feedback. We evaluated our framework on 34 plastic bones and 3 healthy forearm cadaver specimens. In total, 146 screws were placed. On bone phantoms, we achieved an accuracy of 1.02 +/- 0.57mm, 3.68 +/- 4.38 degrees and 1.77 +/- 1.38 degrees in the screw tip position and orientation (azimuth and elevation) respectively. On forearm specimens, we achieved a corresponding accuracy of 1.63 +/- 0.91mm, 5.85 +/- 4.93 degrees and 3.48 +/- 3.07 degrees. Our analysis shows that our framework has the potential for improving the accuracy of the screw placement compared to the state of the art.

Appearance-based Approach to Extract an Age-related Biomarkers from Retinal Images - Bock, Rüdiger; Meier, Jörg; Nyul, Laszlo Gabor; Wärntges, Simone; Michelson, Georg; Hornegger, Joachim (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Appearance-based object recognition using optimal feature transforms - Hornegger, Joachim; Niemann, Heinrich; Risack, Robert (Pattern Recognition, 2000)
In this paper we discuss and compare different approaches to appearance-based object recognition and pose estimation. Images are considered as high-dimensional feature vectors which are transformed in various manners: we use different types of non-linear image-to-image transforms composed with linear mappings to reduce the feature dimensions and to beat the curse of dimensionality. The transforms are selected such that special objective functions are optimized and available image data provide some invariance properties. The paper mainly concentrates on the comparison of preprocessing operations combined with different linear projections in the context of appearance-based object recognition. The experimental evaluation provides recognition rates and pose estimation accuracy. © 1999 Pattern Recognition Society.

Appearance Learning for Image-based Motion Estimation in Tomography - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Hoppe, Elisabeth; Huang, Yixing; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)

Application of automatic speech recognition to quantitative assessment of tracheoesophageal speech with different signal quality - Haderlein, Tino; Riedhammer, Korbinian Thomas; Nöth, Elmar; Toy, Hikmet; Schuster, Maria; Eysholdt, Ulrich; Hornegger, Joachim; Rosanowski, Frank (Folia Phoniatrica Et Logopaedica, 2009)
Tracheoesophageal voice is state-of-the-art in voice rehabilitation after laryngectomy. Intelligibility on a telephone is an important evaluation criterion as it is a crucial part of social life. An objective measure of intelligibility when talking on a telephone is desirable in the field of postlaryngectomy speech therapy and its evaluation. Patients and Methods: Based upon successful earlier studies with broadband speech, an automatic speech recognition (ASR) system was applied to 41 recordings of postlaryngectomy patients. Recordings were available in different signal qualities; quality was the crucial criterion for this study. Results: Compared to the intelligibility rating of 5 human experts, the ASR system had a correlation coefficient of r = -0.87 and Krippendorff's α of 0.65 when broadband speech was processed. The rater group alone achieved α = 0.66. With the test recordings in telephone quality, the system reached r = -0.79 and α = 0.67. Conclusion: For medical purposes, a comprehensive diagnostic approach to (substitute) voice has to cover both subjective and objective tests. An automatic recognition system such as the one proposed in this study can be used for objective intelligibility rating with results comparable to those of human experts. This holds for broadband speech as well as for automatic evaluation via telephone. Copyright © 2008 S. Karger AG, Basel.

Application of deep learning algorithms for Lithographic mask characterization - Woldeamanual, Dereje; Erdmann, Andreas; Maier, Andreas (Proceedings of SPIE - The International Society for Optical Engineering, 2018)
The appearance of defects on the photomask is a key challenge in lithographic printing. Printable defects affect the lithographic process by causing errors in both the phase and magnitude of the light and of the sizes and location of the printed features. Presently 193 nm optical inspection tools are still the main ones for detecting pattern defects on EUV masks.1 However, pattern sizes on EUV masks could not be detected due to the resolution limit of 193 nm inspection tools. We propose and investigate the application of Convolutional Neural Networks (CNNs) to characterize and classify defects on lithographic masks. This paper details the training and evaluation of the CNNs to classify defects in simulated aerial images of an EUV setting. The simulation software Dr.LiTHO is used to simulate aerial images of defect-free masks and of masks with different types and locations of defects. Specifically we compute images of regular arrays of squares to be imaged with typical settings of EUV lithography (λ = 13.5 nm, NA= 0.33). We consider five types of absorber defects (extrusion, intrusion, oversize, undersize and center spot). The architecture of the CNN contains 4 convolutional layers (conv. layers) with a mixed size of filter,(3x3) and (5x5). The convolution stride and the spatial padding is 1 pixel for all conv. layers. Spatial pooling is carried out by 4 max-pooling layers. Two separate networks are trained for detection of the defect types and location, whereas a third algorithm combines the results. When an image is presented to the implemented algorithm and trained networks, it will return the defect type with its location. An accuracy of 99.9% on the training set and 99.3% on the test set is achieved for detection of the defect type. The network trained for location detection results in 98.7% training accuracy and 98.0% for the test set. Having a sufficient amount of training images the trained CNNs classify the types of defects and their location in the aerial image with high accuracy. The proposed method can be also applied to other defect types and simulation settings.
Created from Fastlane, Scopus look-up
Keywords: aerial images; Convolutional neural network; deep learning; EUV; Lithographic Mask; mask defects

Application of morphological operators for optic nerve head segmentation in optical coherence tomography images - Moupagiatzis, Ioannis; Mayer, Markus Anton; Hornegger, Joachim; Tornow, Ralf-Peter; Mardin, Christian Y. (2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012, 2012)
Optical Coherence Tomography (OCT) is an imaging modality that has several advantages (e.g. high resolution and three dimensional imaging) in comparison with other ophthalmic imaging methods. Retinal structures, such as the optic nerve head (ONH), can be examined in detail. The geometry of the ONH can offer valuable information for the detection of glaucoma, because it is affected by this disease. However, manual examinations are highly subjective. In this study we present an approach using morphological operators for the completely automated segmentation of the ONH region, which is crucial for the further examination of the geometrical parameters that indicate the presence of glaucoma. The method was applied to OCT volumes of 10 individuals (7 glaucoma patients, 3 normal subjects). The automatically extracted results were evaluated both qualitatively and quantitatively, and yielded a sensitivity of 0.89±0.09 and a specificity of 0.98±0.01. © 2012 Institute of Telecommunica.

Applied pattern recognition: A practical introduction to image and speech processing in C++ - Paulus, Dietrich; Hornegger, Joachim (Vieweg, 1998)

Applied pattern recognition: A practical introduction to image and speech processing in C++ - Paulus, Dietrich; Hornegger, Joachim (Vieweg, 2001)

Applied pattern recognition: A practical introduction to image and speech processing in C++ - Paulus, Dietrich; Hornegger, Joachim (Vieweg, 2003)

Applying Deep Learning Algorithms to EL-images for Predicting the Module Power - Buerhop-Lutz, Claudia; Hoffmann, Mathis; Reeb, Luca; Pickel, Tobias; Hauch, Jens; Maier, Andreas (Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, 2019)
EL-images nicely visualize the cell quality of PV-modules in terms of existing failures like cell cracks and cell fractures. Nonetheless, the benefit of EL-images recorded in the field increases if a direct prediction of the module power is enabled. For the first time, deep learning algorithms are applied to predict the module power using EL-images. The dataset consists of more than 600 EL-images from different crystalline PV module manufactures. Regarding the network architecture, ResNet18 was identified to perform best with respect to computational time and accuracy. The predicted powers are quite accurate: the mean absolute error over the entire data set is 4.57 W. In addition, 72% of the module powers are predicted with a high accuracy of less than 2% error. Finally, it should be emphasized that even the EL-images recorded in the field with perspective distortion and motion blur are suitable for excellent predictions
Keywords: solar module; power; deep learning; regression; electroluminescence imaging

APPLYING X-VECTORS ON PATHOLOGICAL SPEECH AFTER LARYNX REMOVAL - Scheuerer, Ralph; Haderlein, Tino; Nöth, Elmar; Bocklet, Tobias (2021 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU), 2021)
Speaker embeddings extracted from time delayed neural networks (TDNNs) contributed to major recent advancements in speaker recognition and verification. We use an X-Vector system trained on augmented VoxCeleb1 and VoxCeleb2 data to obtain embeddings for pathological speech after total or partial larynx removal. We show that our model is able to effectively distinguish and visualize patient groups when generating embeddings. We further compare various regression models on the task of automatically predicting different perceptual ratings by speech therapists (intelligibility, vocal effort, and overall quality) based on the extracted speaker embeddings. For both patient groups we show Pearson correlations in the range of +0.8; we find that Random Forest and Support Vector Regression produce scores that best resemble the experts' assessments.
CRIS-Team WoS Importer:2022-05-27

Approaching the accuracy-cost conflict in embedded classification system design - Jensen, Ulf; Kugler, Patrick; Ring, Matthias; Eskofier, Björn (Pattern Analysis and Applications, 2016)
Smart embedded systems often run sophisticated pattern recognition algorithms and are found in many areas like automotive, sports and medicine. The developer of such a system is often confronted with the accuracy–cost conflict as the resulting system should be as accurate as possible while being able to run on resource constraint hardware. This article introduces a method to support the solution of this design conflict with accuracy–cost reports. These reports compare classification systems regarding their classification rate (accuracy) and the mathematical operations and parameters of the working phase (cost). Our method is used to deduce the specific cost of various popular pattern recognition algorithms and to derive the overall cost of a classification system. We also show how our analysis can be used to estimate the computational cost for specific hardware architectures. A software toolbox to create accuracy–cost reports was implemented to facilitate the automatic classification system comparison with the presented methodology. The software is available for download and as supplementary material. We performed different experiments on synthetic and real-world data to underline the value of this analysis. Accurate and computationally cheap classification systems were easily identified. We were even able to find a better implementation candidate in an existing embedded classification problem. This work is the first step towards a comprehensive support tool for the design of embedded classification systems.
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.approa
Keywords: Machine learning; Real-time systems; Cost estimation; Classification system design

Approximate path seeking for statistical iterative reconstruction - Wu, Meng; Yang, Qiao; Maier, Andreas; Fahrig, Rebecca (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2015)
Statistical iterative reconstruction (IR) techniques have demonstrated many advantages in X-ray CT reconstruction. The statistical iterative reconstruction approach is often modeled as an optimization problem including a data fitting function and a penalty function. The tuning parameter values that regulate the strength of the penalty function are critical for achieving good reconstruction results. However, appropriate tuning parameter values that are suitable for the scan protocols and imaging tasks are often difficult to choose. In this work, we propose a path seeking algorithm that is capable of generating a series of IR images with different strengths of the penalty function. The path seeking algorithm uses the ratio of the gradients of the data fitting function and the penalty function to select pixels for small fixed size updates. We describe the path seeking algorithm for penalized weighted least squares (PWLS) with a Huber penalty function in both the directions of increasing and decreasing tuning parameter value. Simulations using the XCAT phantom show the proposed method produces path images that are very similar to the IR images that are computed via direct optimization. The root-mean- squared-error of one path image generated by the proposed method relative to full iterative reconstruction is about 6 HU for the entire image and 10 HU for a small region. Different path seeking directions, increment sizes and updating percentages of the path seeking algorithm are compared in simulations. The proposed method may reduce the dependence on selection of good tuning parameter values by instead generating multiple IR images, without significantly increasing the computational load.
CRIS-Team Scopus Importer:2022-08-05
Keywords: CT; Iterative reconstruction; Path seeking

Approximate truncation robust computed tomography - ATRACT - Dennerlein, Frank; Maier, Andreas (Physics in Medicine and Biology, 2013)

A practical calibration criterion for image-based material decomposition in spectral computed tomography - Yang, Kun; Zhai, Xiaohui; Xie, Zhaoheng; Zhou, Kun; Meng, Xiangxi; Ren, Qiushi; Lu, Yanye (AEJ - Alexandria Engineering Journal, 2020)
This study aimed to provide a calibration criterion based on the condition number (Cond, Q) of the basis material matrix (BMM) to optimize experimental performance of basis of material decomposition (BMD) results. Cond(Q) was first elaborated theoretically and then phantom studies, including the possible influence of a number of experimental parameters, such as X-ray spectrum, the number of energy bins and the width of the energy bins, were performed to validate the criterion. In the BMD phantom study, the quantitative relationship between the proposed criterion and BMD accuracy was explored. Results showed that the proposed criterion was negatively correlated to BMD accuracy, and each of the experimental parameters influenced the criterion and BMD accuracy in a similar pattern. In conclusion Cond(Q) can be used to optimize experimental configurations of BMD.
CRIS-Team Scopus Importer:2020-03-31
Keywords: Basis material decomposition; Condition number; Image-based; Photon counting; Spectral weighting

A practical salient region feature based 3d multimodality registration method for medical images - Hahn, Dieter; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Wolz, Gabriele; Kuwert, Torsten; Xu, Chenyang (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2006)

A practical salient region feature based 3D multi-modality registration method for medical images - Hahn, Dieter; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang (Medical Imaging 2006: Image Processing, 2006)
We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

A practical statistical polychromatic image reconstruction for computed tomography using spectrum binning - Wu, Meng; Yang, Qiao; Maier, Andreas; Fahrig, Rebecca (Proc. SPIE Medical Imaging 2014, 2014)
Polychromatic statistical reconstruction algorithms have very high computational demands due To The difficulty of The optimization problems and The large number of spectrum bins. We want To develop a more practical algorithm That has a simpler optimization problem, a faster numerical solver, and requires only a small amount of prior knowledge. In This paper, a modified optimization problem for polychromatic statistical reconstruction algorithms is proposed. The modified optimization problem utilizes The idea of determining scanned materials based on a first pass FBP reconstruction To fix The ratios between photoelectric and Compton scattering components of all image pixels. The reconstruction of a density image is easy To solve by a separable quadratic surrogate algorithm That is also applicable To The multi-material case. In addition, a spectrum binning method is introduced so That The full spectrum information is not required. The energy bins sizes and attenuations are optimized based on The True spectrum and object. With These approximations, The expected line integral values using only a few energy bins are very closed To The True polychromatic values. Thus both The problem size and computational demand caused by The large number of energy bins That are Typically used To model a full spectrum are reduced. Simulation showed That Three energy bins using The generalized spectrum binning method could provide an accurate approximation of The polychromatic X-ray signals. The average absolute error of The logarithmic detector signal is less Than 0.003 for a 120 kVp spectrum. The proposed modified optimization problem and spectrum binning approach can effectively suppress beam hardening artifacts while providing low noise images. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.apract
Keywords: Beam hardening artifact; CT; Iterative reconstruction; Polychromatic X-ray; Spectrum binning

A principled representation of elongated structures using heatmaps - Kordon, Florian Johannes; Stiglmayr, Michael; Maier, Andreas; Martín Vicario, Celia; Pertlwieser, Tobias; Kunze, Holger (Scientific Reports, 2023)
The detection of elongated structures like lines or edges is an essential component in semantic image analysis. Classical approaches that rely on significant image gradients quickly reach their limits when the structure is context-dependent, amorphous, or not directly visible. This study introduces a principled mathematical description of elongated structures with various origins and shapes. Among others, it serves as an expressive operational description of target functions that can be well approximated by Convolutional Neural Networks. The nominal position of a curve and its positional uncertainty are encoded as a heatmap by convolving the curve distribution with a filter function. We propose a low-error approximation to the expensive numerical integration by evaluating a distance-dependent function, enabling a lightweight implementation with linear time complexity. We analyze the method’s numerical approximation error and behavior for different curve types and signal-to-noise levels. Application to surgical 2D and 3D data, semantic boundary detection, skeletonization, and other related tasks demonstrate the method’s versatility at low errors.<br

A Probabilistic Approach for the Semantic Analysis - Haas, Jürgen; Hornegger, Joachim; Nöth, Elmar; Niemann, Heinrich (Proc. of the AIII Workshop on Artificial Intelligence in Industry, 1998)

A probabilistic model for automatic segmentation of the esophagus in 3-D CT scans - Feulner, Johannes; Zhou, S. Kevin; Hammon, Matthias; Seifert, Sascha; Huber, Martin; Comaniciu, Dorin; Hornegger, Joachim; Cavallaro, Alexander Josef (IEEE Transactions on Medical Imaging, 2011)
Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a detect and connect approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods. © 2011 IEEE.

A Realistic Digital Phantom for Perfusion C-Arm CT Based on MRI Data - Aichert, André; Manhart, Michael; Navalpakkam, Bharath; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
CTP is an important imaging modality for diagnosis of ischemic stroke, which is computed from of a series of consecutive CT-scans during the injection of contrast agent. Contrast flow at any point in space can be tracked as minor changes in intensity over a period of about 40 seconds to one minute, represented as a time-attenuation curve (TAC) for every voxel. This work presents an isotropic, dense, physiologically realistic and dynamic brain phantom for CT perfusion. The phantom is based on MRI scans of a volunteer and is freely available for download. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.areali_2

Are fast labeling methods reliable? a case study of computer-aided expert annotations on microscopy slides - Marzahl, Christian; Bertram, Christof A.; Aubreville, Marc; Petrick, Anne; Weiler, Kristina; Gläsel, Agnes C.; Fragoso, Marco; Merz, Sophie; Bartenschlager, Florian; Hoppe, Judith; Langenhagen, Alina; Jasensky, Anne Katherine; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Deep-learning-based pipelines have shown the potential to revolutionalize microscopy image diagnostics by providing visual augmentations and evaluations to a trained pathology expert. However, to match human performance, the methods rely on the availability of vast amounts of high-quality labeled data, which poses a significant challenge. To circumvent this, augmented labeling methods, also known as expert-algorithm-collaboration, have recently become popular. However, potential biases introduced by this operation mode and their effects for training deep neuronal networks are not entirely understood. This work aims to shed light on some of the effects by providing a case study for three pathologically relevant diagnostic settings. Ten trained pathology experts performed a labeling tasks first without and later with computer-generated augmentation. To investigate different biasing effects, we intentionally introduced errors to the augmentation. In total, the pathology experts annotated 26,015 cells on 1,200 images in this novel annotation study. Backed by this extensive data set, we found that the concordance of multiple experts was significantly increased in the computer-aided setting, versus the unaided annotation. However, a significant percentage of the deliberately introduced false labels was not identified by the experts.
CRIS-Team Scopus Importer:2020-10-30
Keywords: Computer-aided labelling; Expert-algorithm collaboration; Microscopy; Pathology

A Reliability Measure for Merging Data from Multiple Cameras in Optical Motion Correction - Ehrl, Jakob; Maclaren, Julian; Aksoy, Murat; Maier, Andreas; Bammer, Roland (Proc. ISMRM SCIENTIFIC WORKSHOP - Motion Correction in MRI 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.arelia
Keywords: motion correction; MRI; reliability

Are men more sleepy than women or does it only look like - Automatic analysis of sleepy speech - Hönig, Florian Thomas; Batliner, Anton; Bocklet, Tobias; Stemmer, Georg; Nöth, Elmar; Schnieder, Sebastian; Krajewski, Jarek (ICASSP 2014, Proceedings, 2014)
The degree of sleepiness in the Sleepy Language Corpus from the Interspeech 2011 Speaker State Challenge is predicted with regression and a very large feature vector. Most notable is the great gender difference which can mainly be attributed to females showing their sleepiness less than males do. © 2014 IEEE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.aremen
Keywords: brute forcing; gender differences; paralinguistics; regression; Sleepiness

Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels - Bertram, Christof A.; Veta, Mitko; Marzahl, Christian; Stathonikos, Nikolas; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge. Additional to manual mitotic figure screening, we used a novel, algorithm-aided labeling process, that allowed to minimize the risk of missing rare mitotic figures in the images. All potential mitotic figures were independently assessed by two pathologists. The novel, publicly available set of labels contains 1,999 mitotic figures (+28.80%) and additionally includes 10,483 labels of cells with high similarities to mitotic figures (hard examples). We found significant difference comparing scores between the original label set (0.549) and the new alternative label set (0.735) using a standard deep learning object detection architecture. The models trained on the alternative set showed higher overall confidence values, suggesting a higher overall label consistency. Findings of the present study show that pathologists-defined labels may vary significantly resulting in notable difference in the model performance. Comparison of deep learning-based algorithms between independent datasets with different labeling methods should be done with caution.
CRIS-Team Scopus Importer:2020-10-30
Keywords: Breast cancer; Computer-aided annotation; Deep learning; Mitotic figures

Are you looking at me, are you talking with me: Multimodal classification of the focus of attention - Batliner, Anton; Nöth, Elmar; Hacker, Christian (Text, Speech and Dialogue. 9th International Conference, TSD 2006, Brno, Czech Republic, September 2006, Proceedings, 2006)
Automatic dialogue systems get easily confused if speech is recognized which is not directed to the system. Besides noise or other people's conversation, even the user's utterance can cause difficulties when he is talking to someone else or to himself ("Off-Talk"). In this paper the automatic classification of the user's focus of attention is investigated. In the German SmartWeb project, a mobile device is used to get access to the semantic web. In this scenario, two modalities are provided speech and video signal. This makes it possible to classify whether a spoken request is addressed to the system or not: with the camera of the mobile device, the user's gaze direction is detected; in the speech signal, prosodic features are analyzed. Encouraging recognition rates of up to 93% are achieved in the speech-only condition. Further improvement is expected from the fusion of the two information sources.

Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set - Timotius, Ivanna; Miaou, Shaou-Gang (International Conference on Audio, Language and Image Processing (ICALIP 2010), 2010)
Classifier performance measurement is essential in the development and analysis of classification algorithms. This paper proposes a new measurement approach that can be used generally for the balanced and imbalanced data set, can reflect the random guessing behavior perfectly, and can be used easily in cost-sensitive classification and multiple-class classification.

A Robust Deep-Learning-based Automated Cardiac Resting Phase Detection: Validation in a Prospective Study - Yoon, Seung Su; Hoppe, Elisabeth; Schmidt, Michaela; Forman, Christoph; Chitiboi, Teodora; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Köhler, Thomas; Maier, Andreas; Hornegger, Joachim (24th International Conference on Information Processing in Medical Imaging, IPMI 2015, 2015)
Fluoroscopic images are characterized by a transparent projection of 3-D structures from all depths to 2-D. Differently moving structures, for example due to breathing and heartbeat, can be described approximately using independently moving 2-D layers. Separating the fluoroscopic images into the motion layers is desirable to facilitate interpretation and diagnosis. Given the motion of each layer, it is state of the art to compute the layer separation by minimizing a least-squares objective function. However, due to high noise levels and inaccurate motion estimates, the results are not satisfactory in X-ray images. In this work, we propose a probabilistic model for motion layer separation. In this model, we analyze various data terms and regularization terms theoretically and experimentally. We show that a robust penalty function is required in the data term to deal with noise and shortcomings of the image formation model. For the regularization term, we propose to enforce smoothness of the layers using bilateral total variation. On synthetic data, the mean squared error between the estimated layers and the ground truth is improved by 18% compared to the state of the art. In addition, we show qualitative improvements on real X-ray data.
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.arobus_6

Articulation and Empirical Mode Decomposition Features in Diadochokinetic Exercises for the Speech Assessment of Parkinson’s Disease Patients - Vasquez Correa, Juan; Rios-Urrego, Cristian D.; Rueda, Alice; Orozco Arroyave, Juan Rafael; Krishnan, Sri; Nöth, Elmar (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2019)

Articulatory Features for Expressive Speech Synthesis - Black, Alan W.; Bunnell, H. Timothy; Dou, Ying; Muthukumar, Prasanna Kumar; Metze, Florian; Perry, Daniel; Polzehl, Tim; Prahallad, Kishore S.; Steidl, Stefan; Vaughn, Callie (Proc. ICASSP 2012, 2012)
This paper describes some of the results from the project entitled “New Parameterization for Emotional Speech Synthesis” held at the Summer 2011 JHU CLSP workshop. We describe experiments on how to use articulatory features as a meaningful intermediate representation for speech synthesis. This parameterization not only allows us to reproduce natural sounding speech but also allows us to generate stylistically varying speech. We show methods for deriving articulatory features from speech, predicting articulatory features from text and reconstructing natural sounding speech from the predicted articulatory features. The methods were tested on clean speech databases in English and German, as well as databases of emotionally and personality varying speech. The resulting speech was evaluated both objectively, using techniques normally used for emotion identification, and subjectively, using crowd-sourcing.
Keywords: speech synthesis; articulatory features; emotional speech; meta-data extraction; evaluation

Artificial Intelligence based Methods for Atrial Fibrillation Ablation Procedures - Koch, Martin (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
As one of the most common types of heart arrhythmias, atrial fibrillation is a severe disorder of the heart rhythm affecting the left atrium. Among the most serious complications count stroke and tachycardia mediated cardiomyopathy. Moreover, recurrent symptoms impair patients’ quality of life and functional status. Common treatment options for rhythm control are antiarrhythmic drug therapy and catheter ablation procedures. Cardiac ablation procedures are usually performed minimally invasive in electrophysiology labs. During these procedures, ablation catheters are navigated into the heart chamber via the venous system to ablate specific areas involved in conduction of irregular impulses. For treatment of paroxysmal atrial fibrillation, ipsilateral pulmonary vein isolation is a common ablation pattern. Interventional X-ray imaging is commonly employed for catheter guidance and control. Furthermore, electroanatomic mapping systems can be used for treatment of complex arrhythmia. Ablation planning data can be used during X-ray guided procedures as well as included in mapping systems to support the physician by supplying further context information. In this thesis, artificial intelligence based methods for interventional treatment of atrial fibrillation ablation procedures are presented. We developed an algorithm for automatic lesion planning targeted at pulmonary vein isolation procedures for treatment of atrial fibrillation. This method facilitates a landmark-constrained non-rigid registration algorithm for accurate alignment of left atrium heart models. Procedure planning data is generated for the individual patient anatomy to be superimposed during the ablation procedure. A quantitative and qualitative evaluation of the algorithm was performed on clinical datasets. The accuracy of the automatically generated ablation planning lines fulfilled clinical requirements. The mean error of 2.7 mm achieved implies a 29 % improvement compared to the state of the art algorithm. The qualitative evaluation showed full acceptance of the automatically generated planning lines. Another aspect investigated in this thesis is the optimization of individual fluoroscopic projection angles for X-ray guided cardiac procedures. We developed an algorithm to estimate individual X-ray C-arm angulations based on pre-procedure planning information, taking individual patient anatomy into consideration. The C-arm angulations are optimized in respect to the orientation of the planning structure to minimize the foreshortening in the projection image. The mathematical framework can be applied for monoplane and biplane C-arm imaging systems. Limitations of C-arm imaging systems in terms of feasible rotation angles are also taken into account during optimization. The algorithm was evaluated on clinical data for ipsilateral pulmonary vein isolation. Patient-specific C-arm angulations were computed and compared against commonly used standard angulations in terms of foreshortening of planning structures in projection images. By applying individually optimized X-ray angulations, 28 % less foreshortening could be achieved on average for biplane systems.<br
Keywords: Bildverarbeitung; Image Processing

Artificial Intelligence for Medical Image Understanding - Ghesu, Florin-Cristian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)
Robust and fast detection and segmentation of anatomical structures in medical image data represents an important component of medical image analysis technologies. Current solutions for this problem are nowadays based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations. This includes the use of suboptimal image feature engineering methods and most importantly the use of computationally suboptimal search-schemes for anatomy parsing, e.g., exhaustive hypotheses scanning. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. To address these challenges, we introduce in this thesis marginal space deep learning, a framework for medical image parsing which exploits the automated feature design of deep learning models and an efficient object parametrization scheme in hierarchical marginal spaces. To support the efficient evaluation of solution hypotheses under complex transformations, such as rotation and anisotropic scaling, we propose a novel cascaded network architecture, called sparse adaptive neural network. Experiments on detecting and segmenting the aortic root in 2891 3D ultrasound volumes from 869 patients, demonstrate a high level of robustness with an accuracy increase of 30-50% against the state-of-the-art. Despite these facts, using a scanning routine to explore large parameter subspaces results in a high computational complexity, with false-positive predictions and limited scalability to high resolution volumetric data. To deal with these limitations, we propose a novel paradigm for medical image parsing, based on principles of cognitive modeling and behavior learning. The anatomy detection problem is reformulated as a behavior learning task for an intelligent artificial agent. Using deep reinforcement learning, agents are taught how to search for an anatomical structure. This resumes to learning to navigate optimal search trajectories through the image space that converge to the locations of the sought anatomical structures. To support the effective parsing of high resolution volumetric data, we apply elements from scale-space theory and enhance our framework to support the learning of multi-scale search strategies through the scale-space representation of medical images. Finally, to enable the accurate recognition whether certain anatomical landmarks are missing from the field-of-view, we exploit prior knowledge about the anatomy and ensure the spatial coherence of the agents by using statistical shape modeling and robust estimation theory. Comprehensive experiments demonstrate a high level of accuracy, compared to state-of-the-art solutions, without failures of clinical significance. In particular, our method achieves 0% false positive and 0% false negative rates at detecting whether anatomical structures are captured in the field-of-view (excluding border cases). The dataset contains 5043 3D computed tomography volumes from over 2000 patients, totaling over 2,500,000 image slices. A significant increase in accuracy compared to reference solutions is achieved on additional 2D ultrasound and 2D/3D magnetic resonance datasets, containing up to 1000 images. Most importantly, this paradigm improves the detection-speed of the previous solutions by 2-3 orders of magnitude, achieving unmatched real-time performance on high resolution volumetric scans.<br
Keywords: Artificial Intelligence; Deep Learning; Medical Image Analysis; Medical Image Parsing; Reinforcement Learning

A scalable architecture for multilingual speech recognition on embedded devices - Raab, Martin; Gruhn, Rainer; Nöth, Elmar (Speech Communication, 2011)
In-car infotainment and navigation devices are typical examples where speech based interfaces are successfully applied. While classical applications are monolingual, such as voice commands or monolingual destination input, the trend goes towards multilingual applications. Examples are music player control or multilingual destination input. As soon as more languages are considered the training and decoding complexity of the speech recognizer increases. For large multilingual systems, some kind of parameter tying is needed to keep the decoding task feasible on embedded systems with limited resources. A traditional technique for this is to use a semi-continuous Hidden Markov Model as the acoustic model. The monolingual codebook on which such a system relies is not appropriate for multilingual recognition. We introduce Multilingual Weighted Codebooks that give good results with low decoding complexity. These codebooks depend on the actual language combination and increase the training complexity. Therefore an algorithm is needed that can reduce the training complexity. Our first proposal are mathematically motivated projections between Hidden Markov Models defined in Gaussian spaces. Although theoretically optimal, these projections were difficult to employ directly in speech decoders. We found approximated projections to be most effective for practical application, giving good performance without requiring major modifications to the common speech recognizer architecture. With a combination of the Multilingual Weighted Codebooks and Gaussian Mixture Model projections we create an efficient and scalable architecture for non-native speech recognition. Our new architecture offers a solution to the combinatoric problems of training and decoding for multiple languages. It builds new multilingual systems in only 0.002% of the time of a traditional HMM training, and achieves comparable performance on foreign languages. (C) 2010 Elsevier B.V. All rights reserved.
Keywords: Multilingual speech recognition; Non-native speech; Projections between Gaussian spaces; Gaussian Mixture Model distances

A Self-Taught Artificial Agent for Multi-Physics Computational Model Personalization - Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; et al. (Medical Image Analysis, 2016)
UnivIS-Import:2016-06-27:Pub.2016.tech.IMMD.IMMD5.aselft
Keywords: Artificial intelligence; Computational modeling; Model personalization; Reinforcement learning

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems - Amrehn, Mario; Steidl, Stefan; Kortekaas, Reinier; Strumia, Maddalena; Weingarten, Markus; Kowarschik, Markus; Maier, Andreas (International Journal of Biomedical Imaging, 2019)
For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype’s user interface (UI) features and segmentation methodologies.<br
Keywords: usability; interactive segmentation; prototype evaluation; user simulation; robot user

A Simulation Study and Experimental Verification of Hand-Eye-Calibration using Monocular X-Ray - Dorn, Petra; Fischer, Peter; Mönnich, Holger; Mewes, Philip; Khalil, Asim; Gulhar, Abhinav; Maier, Andreas (Informatik aktuell, 2018)
In this paper, the simultaneous hand-eye/robot-world problem AX = ZB is performed using a single X-ray image instead of a stereo camera in order to avoid the additional tracking device. Our setup consists of a special X-ray marker, several image preprocessing steps, and a monocular pose estimation algorithm, for extracting the 6- D pose of the marker with respect to the X-ray source. Simulations are performed to investigate the behavior of the proposed hand-eye method when including inaccuracies of the robot and the non-isotropic errors of monocular pose estimation. The simulations were evaluated in an experimental setup, reaching an accuracy of 0.06° and 0.77mm.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.asimul

A Software Kit for Automatic Voice Descrambling - Riedhammer, Korbinian Thomas; Ring, Matthias; Nöth, Elmar; Kolb, Dirk (Proceedings of the IEEE International Workshop on Security and Forensics in Communication Systems (SFCS), 2012)

A Spin-Glass Markov Random Field for 3-D Object Recognition - Caputo, Barbara; Hornegger, Joachim; Paulus, Dietrich; Niemann, Heinrich (Proc. International Conference of Computer Vision (ICCV), 2001)
UnivIS-Import:2015-04-16:Pub.2001.forsch.gradui.gradui_6.asping_80

A Spin Glass Model of a Markov Random Field - Caputo, Barbara; Hornegger, Joachim; Paulus, Dietrich; Niemann, Heinrich (Proceedings of the NIPS 2000 Workshop on New Perspective in Kernel Based Learning Methods, 2000)

Assessing cardiac function from total-variation-regularized 4-D C-arm CT in the presence of angular undersampling - Taubmann, Oliver; Haase, V.; Lauritsch, G.; Zheng, Y.; Krings, G.; Hornegger, Joachim; Maier, Andreas (Physics in Medicine and Biology, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.assess
Keywords: C-arm Computed Tomography; Angular Undersampling; Cardiac Function; 4-D Imaging; Total Variation; Temporal Regularization

Assessing Parkinson's disease from speech using fisher vectors - Egas López, José Vicente; Orozco Arroyave, Juan Rafael; Gosztolya, Gábor (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
Parkinson's Disease (PD) is a neuro-degenerative disorder that affects primarily the motor system of the body. Besides other functions, the subject's speech also deteriorates during the disease, which allows for a non-invasive way of automatic screening. In this study, we represent the utterances of subjects having PD and those of healthy controls by means of the Fisher Vector approach. This technique is very common in the area of image recognition, where it provides a representation of the local image descriptors via frequency and high order statistics. In the present work, we used four frame-level feature sets as the input of the FV method, and applied (linear) Support Vector Machines (SVM) for classifying the speech of subjects. We found that our approach offers superior performance compared to classification based on the i-vector and cosine distance approach, and it also provides an efficient combination of machine learning models trained on different feature sets or on different speaker tasks.
CRIS-Team Scopus Importer:2019-11-19
Keywords: Automatic screening; Fisher Vector encoding; Parkinson's Disease; Speech analysis

Assessing the dysarthria level of parkinson’s disease patients with gmm-ubm supervectors using phonological posteriors and diadochokinetic exercises - Miller, Gabriel Figueiredo; Vasquez Correa, Juan; Nöth, Elmar (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Parkinson’s disease (PD) is a neuro-degenerative disorder that produces symptoms such as tremor, slowed movement, and a lack of coordination. One of the earliest indicators is a combination of different speech impairments called hypokinetic dysarthria. Some indicators that are prevalent in the speech of Parkinson’s patients include, imprecise production of stop consonants, vowel articulation impairment and reduced loudness. In this paper, we examine those features using phonological posterior probabilities obtained via parallel bidirectional recurrent neural networks. We also utilize information such as the velocity and acceleration curve of the signal envelope, and the peak amplitude slope and variance to model the quality of pronunciation for a given speaker. With our feature set, we train Gaussian Mixture Model based Universal Background Models for a set of training speakers and adapt a model for each individual speaker using a form of Bayesian adaptation. With the parameters describing each speaker model, we train SVM and Random Forest classifiers to discriminate PD patients and Healthy Controls (HC), and to determine the severity of dysarthria for each speaker compared with ratings assessed by expert phoneticians.
Created from Fastlane, Scopus look-up

Assessment of Handwriting in Patients With Parkinson's Disease Using Non-Intrusive Tasks - Gallo-Aristizabal, J. D.; Escobar-Grisales, D.; Rios-Urrego, C. D.; Pérez Toro, Paula Andrea; Nöth, Elmar; Maier, Andreas; Orozco Arroyave, Juan Rafael (Proceedings - International Symposium on Biomedical Imaging, 2023)
This study presents two approaches for modeling the handwriting of Parkinson's Disease (PD) patients and Healthy Control (HC) subjects. One approach is based on digit-embeddings generated from a CNN architecture pre-trained with information from the MNIST corpus. The second approach consists of the computation of statistical functionals of dynamics signal collected with the digital tablet, namely azimuth, pressure, altitude, and vertical distance. The experiments are based on writing the ten digits (from 0 to 9), which is a task commonly performed in daily life activities, making this approach closer to a non-intrusive evaluation. According to the results, the accuracy of the classification between PD patients vs. HC improved from 71.8% to 74.5% when information from images is combined with the functionals of the vertical distance signal.
CRIS-Team Scopus Importer:2023-10-06
Keywords: CNN; digital tablet; Handwriting; Parkinson's disease

Assessment of measurement deviations: length-extended x-ray imaging for orthopedic applications - Luckner, Christoph; Magdalena, Herbst; Ritschl, Ludwig; Maier, Andreas; Kappler, Steffen (Medical Imaging 2019: Physics of Medical Imaging, 2019)

Assessment of Non-Native Children's Pronunciation: Human Marking and Automatic Scoring - Hacker, Christian; Batliner, Anton; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich; Cincarek, Tobias (SPEECOM 2005 Proceedings, 10th International Conference on SPEECH and COMPUTER, 2005)

Assessment of segmentation dependence in macroscopic lung cavity extraction - Khdeir, Asmaa; Geimer, Tobias; Chen, Shuqing; Goppert, Eric; Dankbar, Maximilian; Bert, Christoph; Maier, Andreas (Informatik aktuell, 2018)
Training of respiratory motion models and population-based patient phantoms of the lung often requires the definition of the entire lung cavity region in the 4D-CT. To ease the workload of clinical experts, automatic selection is highly desirable. Many lung cavity extraction methods rely on a pre-segmented lung volume.
CRIS-Team Scopus Importer:2022-06-05

Assessment of Time-of-Flight Cameras for Short Camera-Object Distances - Stürmer, Michael; Becker, Guido; Hornegger, Joachim (Vision, Modeling, and Visualization (2011), 2011)

Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval - Bootwala, Azeem; Breininger, Katharina; Maier, Andreas; Christlein, Vincent (Bildverarbeitung für die Medizin 2020, 2020)
Image-based diagnosis of the human eye is crucial for the early detection of several diseases in ophthalmology. In this work, we investigate the possibility to use image retrieval to support the diagnosis of diabetic retinopathy. To this end, we evaluate different feature learning techniques. In particular, we evaluate the performance of cost functions specialized for metric learning, namely, contrastive loss, triplet loss and histogram loss, and compare them with the classification crossentropy loss. Additionally, we train the network on images graded by diabetic retinopathy severity and transfer the knowledge learned, to retrieve images that are graded by diabetic macular edema severity and evaluate our algorithm on three different datasets. For the task of detecting referable/non-referable diabetic retinopathy, we achieve a sensitivity of 0.84 and specificity of 0.88 on the Kaggle dataset using histogram loss. On the Messidor dataset, we achieve a sensitivity and specificity score of 0.79 and 0.84, respectively.<br

Associating Children's Non-Verbal and Verbal Behaviour: Body Movements, Emotions, and Laughter in a Human-Robot Interaction - Batliner, Anton; Steidl, Stefan; Nöth, Elmar (Proceedings of ICASSP, 2011)

(untitled) - Christlein, Vincent; Riess, Christian; Angelopoulou, Elli (Sicherheit 2010: Sicherheit, Schutz und Zuverlässigkeit, Beiträge der 5. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft fü)

A study on the impact of statistical weights on lesion detection performance in iterative CT reconstruction - Haase, Viktor; Griffith, April; Guo, Zijia; Oktay, Mehmet; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Noo, Frederic (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.astudy

A Survey of Sensors in Healthcare Workflow Monitoring - Stoffel Antunes, Rodolfo; Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Facco Rodrigues, Vinicius; Andre da Costa, Cristiano; da Silveira Junior, Luiz Gonzaga; da Rosa Righi, Rodrigo; Maier, Andreas; Eskofier, Björn; Ollenschläger, Malte; Naderi Boldaji, Farzad; Fahrig, Rebecca; et al. (ACM Computing Surveys, 2018)
Keywords: Real-time location systems; Workflow monitoring; Computer vision; Healthcare

A survey on perceived speaker traits: Personality, likability, pathology, and the first challenge - Schuller, Björn; Steidl, Stefan; Batliner, Anton; Nöth, Elmar; Vinciarelli, Alessandro; Burkhardt, Felix; van Son, Rob; Weninger, Felix; Eyben, Florian; Bocklet, Tobias; Mohammadi, Gelareh; Weiss, Benjamin (Computer Speech and Language, 2015)
The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits - the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks.
UnivIS-Import:2015-03-11:Pub.2015.tech.IMMD.IMMD5.asurve
Keywords: Challenge; Computational paralinguistics; Likability; Pathology; Personality; Survey

A system for advanced real-time visualization and monitoring of MR-guided thermal ablations - Rothgang, Eva; Gilson, Wesley D.; Hornegger, Joachim; Lorenz, Christine H. (Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 2010)

A Temperature-Based Bioimpedance Correction for Water Loss Estimation During Sports - Ring, Matthias; Lohmüller, Clemens; Rauh, Manfred; Mester, Joachim; Eskofier, Björn (IEEE Journal of Biomedical and Health Informatics, 2016)
The amount of total body water (TBW) can be estimated based on bioimpedance measurements of the human body. In sports, TBW estimations are of importance because mild water losses can impair muscular strength and aerobic endurance. Severe water losses can even be life threatening. TBW estimations based on bioimpedance, however, fail during sports because the increased body temperature corrupts bioimpedance measurements. Therefore, this paper proposes a machine learning method that eliminates the effects of increased temperature on bioimpedance and, consequently, reveals the changes in bioimpedance that are due to TBW loss. This is facilitated by utilizing changes in skin and core temperature. The method was evaluated in a study in which bioimpedance, temperature, and TBW loss were recorded every 15 minutes during a two-hour running workout. The evaluation demonstrated that the proposed method is able to reduce the error of TBW loss estimation by up to 71%, compared to the state of art. In the future, the proposed method in combination with portable bioimpedance devices might facilitate the development of wearable devices for continuous and noninvasive TBW loss monitoring during sports.
EVALuna2:15227

Atlas-Based Linear Volume-of-Interest (ABL-VOI) Image Correction - Maier, Andreas; Jiang, Zhenzhen; Jordan, Johannes Michael; Riess, Christian; Hofmann, Hannes; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)

Attention-guided Erasing - Bhandary Panambur, Adarsh; Yu, Hui; Bhat, Sheethal; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed attention-guided erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.<br

A Two-Stage Probabilistic Approach For Object Recognition - Li, Stan Z.; Hornegger, Joachim (Computer Vision - ECCV '98, 1998)

A Two-Stage Regression Using Bioimpedance and Temperature for Hydration Assessment During Sports - Ring, Matthias; Lohmüller, Clemens; Rauh, Manfred; Eskofier, Björn (Proceedings of the 2014 22nd International Conference on Pattern Recognition (ICPR), 2014)
Bioimpedance analysis (BIA) estimates the amount of total body water (TBW) in the human body. During sports, however, the increased skin temperature distorts bioimpedance measurements and, thus, prevents the application of BIA. In this paper, we propose a two-stage regression that includes temperature information in order to correct the temperature-distorted bioimpedance. In detail, the first regression stage corrects temperature-distored bioimpedance using information of skin and core temperature. The second regression stage estimates TBW loss on basis of the corrected bioimpedance. The two-stage regression was evaluated using data of an ongoing study. The results showed that estimations of TBW loss during sports can be considerably improved if temperature information is included. However, a remaining error was still observed. Therefore, additional measurements, e.g., skin blood flow, are discussed because they also influence bioimpedance and could further reduce the error.

Atypical Speech - Stemmer, Georg; Nöth, Elmar; Parsa, V. (2010)

Audio-Based Search and Rescue with a Drone: Highlights from the IEEE Signal Processing Cup 2019 Student Competition [SP Competitions] - Deleforge, Antoine; Di Carlo, Diego; Strauß, Martin; Serizel, Romain; Marcenaro, Lucio (IEEE Signal Processing Magazine, 2019)
Increasing interest in unmanned aerial vehicles (UAVs), commonly referred to as drones, has occurred in recent years. Search and rescue scenarios where humans in emergency situations need to be quickly found in difficult-toaccess areas constitute an important field of application for this technology. Drones have already been used by humanitarian organizations in countries such as Haiti and the Philippines to map areas after a natural disaster using high-resolution embedded cameras, as documented in a recent United Nations report [1]. Although research efforts have focused mostly on developing video-based solutions for this task [2], UAV-embedded audio-based localization has received relatively less attention [3]-[7]. However, UAVs equipped with a microphone array could be of critical help to localize people in emergency situations, especially when video sensors are limited by a lack of visual feedback due to bad lighting conditions (such as at night or in fog) or obstacles limiting the field of view (Figure 1).
CRIS-Team Scopus Importer:2019-09-24

Audio-Visual Feedback System for Reward-Based Training Sessions of Elderly People in a Home Environment - Soutschek, Stefan; Maier, Andreas; Hönig, Florian Thomas; Spiegl, Werner; Steidl, Stefan; Hornegger, Joachim; Erzigkeit, Helmut; Kornhuber, Johannes (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Augmented fluoroscopy-based navigation on a biplane angiography system for pulmonary vein isolation - Bourier, Felix; Brost, Alexander; Hornegger, Joachim; Kiraly, Attila P.; Barbot, Julien; Strobel, Norbert; Zorger, Niels; Schneider, Hans-Jürgen; Heißenhuber, Frank; Kurzidim, Klaus (ESC Congress, 2011)

Augmented Fluoroscopy to Guide Transseptal Puncture - Bourier, Felix; Schneider, Hans-Jürgen; Heißenhuber, Frank; Ganslmeier, Patrycja; Brost, Alexander; Koch, Martin; Hornegger, Joachim; Kleinöder, Andreas; Kiraly, Attila P.; Barbot, Julien; Strobel, Norbert; Kurzidim, Klaus (Venice Arrhythmias 2011, 2011)

Augmented Mitotic Cell Count Using Field of Interest Proposal - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Informatik aktuell, 2019)
Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.
CRIS-Team Scopus Importer:2019-05-14

A Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image Upsampling in Multi-Sensor Imaging - Köhler, Thomas; Jordan, Johannes Michael; Maier, Andreas; Hornegger, Joachim (Proceedings of the British Machine Vision Conference (BMVC), 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.aunifi

Autoblog 2021: The Importance of Language Models for Spontaneous Lecture Speech - Hernandez, Abner; Klumpp, Philipp; Das, Badhan Kumar; Maier, Andreas; Yang, Seung Hee (Text, Speech, and Dialogue 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings, 2022)
The demand for both quantity and quality of online educational resources has skyrocketed during the last two years’ pandemic. Entire course series had since been recorded and distributed online. To reach a broader audience, videos could be transcribed, combined with supplementary material (e.g. lecture slides) and published in the style of blog posts. This had been done previously for Autoblog 2020, a corpus of lecture recordings that had been converted to blog posts, using automated speech recognition (ASR) for subtitle creation. This work aims to introduce a second series of recorded and manually transcribed lecture videos. The corresponding data includes lecture videos, slides, and blog posts/transcripts with aligned slide images and is published under creative commons license. A state-of-the-art Wav2Vec ASR model was used for automatic transcription of the content, using different n-gram language models (LM). The results were compared to the human ground truth annotation. Findings indicated that the ASR performed well on spontaneous lecture speech. Furthermore, LMs trained on large amounts of data with fewer out-of-vocabulary words were outperformed by much smaller LMs estimated over in-domain language. Annotated lecture recordings were deemed helpful for the creation of task-specific ASR solutions as well as their validation against a human ground truth.<br
Keywords: Lecture video corpus; Speech recognition; Language model; Spontaneous lecture speech

Auto-Gain Approach for Use with Time-of-Flight Examination in Minimally Invasive Surgery - Ritt, Philipp; Höller, Kurt Emmerich; Penne, Jochen; Schneider, Adam; Hornegger, Joachim; Feußner, Hubertus (3rd Russian-Bavarian Conference on Biomedical Engineering, 2007)

Automated Coronary Artery Ostia Detection in Magnetic Resonance Angiography - Stimpel, Bernhard; Forman, Christoph; Wetzl, Jens; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Proceedings of the 25th Annual Meeting of the ISMRM, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_3

Automated Cross-language Intelligibility Analysis of Parkinson's Disease Patients Using Speech Recognition Technologies - Hosseini-Kivanani, Nina; Vasquez Correa, Juan; Stede, Manfred; Nöth, Elmar (57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019:): STUDENT RESEARCH WORKSHOP, 2019)
CRIS-Team WoS Importer:2020-04-10

Automated Curved and Multiplanar Reformation for Screening of the Proximal Coronary Arteries in MR Angiography - Stimpel, Bernhard; Wetzl, Jens; Forman, Christoph; Schmidt, Michaela; Maier, Andreas; Unberath, Mathias (Journal of Imaging, 2018)
Congenital anomalies of the coronary ostia can lead to sudden death. A screening solution would be useful to prevent adverse outcomes for the affected individuals. To be considered for integration into clinical routine, such a procedure must meet strict constraints in terms of invasiveness, time and user interaction. Imaging must be fast and seamlessly integrable into the clinical process. Non-contrast enhanced coronary magnetic resonance angiography (MRA) is well suited for this. Furthermore, planar reformations proved effective to reduce the acquired volumetric datasets to 2D images. These usually require time consuming user interaction, though. To fulfill the aforementioned challenges, we present a fully automated solution for imaging and reformatting of the proximal coronary arteries which enables rapid screening of these. The proposed pipeline consists of: (I) highly accelerated single breath-hold MRA data acquisition, (II) coronary ostia detection and vessel centerline extraction, and (III) curved planar reformation of the proximal coronary arteries, as well as multiplanar reformation of the coronary ostia. The procedure proved robust and effective in ten volunteer data sets. Imaging of the proximal coronary arteries took 24±5 s and was successful within one breath-hold for all patients. The extracted centerlines achieve an overlap of 0.76±0.18 compared to the reference standard and the average distance of the centerline points from the spherical surface for reformation was 1.1±0.51 mm. The promising results encourage further experiments on patient data, particularly in coronary ostia anomaly screening.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.automa_94
Keywords: Computer-aided detection and diagnosis; Coronary artery; Magnetic resonance angiography; Planar reformation

Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests - Lorch, Benedikt; Vaillant, Ghislain; Baumgartner, Christian; Bai, Wenjia; Rueckert, Daniel; Maier, Andreas (Journal of Medical Engineering, 2017)
UnivIS-Import:2017-11-06:Pub.2017.tech.IMMD.IMMD5.automa_8
Keywords: image reconstruction; artifacts; classification

Automated Evaluation of Image Quality in Mammography - Heim, Christoph; Riess, Christian; Borsdorf, Anja; Hofmann, Carola; Hornegger, Joachim (5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Automated Evaluation of Three Dimensional Ultrasonic Datasets - Osman, Ahmad (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2013)
Non-destructive testing has become necessary to ensure the quality of materials and components either in-service or at the production stage. This requires the use of a rapid, robust and reliable testing technique. As a main testing technique, the ultrasound technology has unique abilities to assess the discontinuity location, size and shape. Such information play a vital role in the acceptance criteria which are based on safety and quality requirements of manufactured components. Consequently, an extensive usage of the ultrasound technique is perceived especially in the inspection of large scale composites manufactured in the aerospace industry. Significant technical advances have contributed into optimizing the ultrasound acquisition techniques such as the sampling phased array technique. However, acquisition systems need to be complemented with an automated data analysis procedure to avoid the time consuming manual interpretation of all produced data. Such a complement would accelerate the inspection process and improve its reliability. The objective of this thesis is to propose an analysis chain dedicated to automatically process the 3D ultrasound volumes obtained using the sampling phased array technique. First, a detailed study of the speckle noise affecting the ultrasound data was conducted, as speckle reduces the quality of ultrasound data. Afterward, an analysis chain was developed, composed of a segmentation procedure followed by a classification procedure. The proposed segmentation methodology is adapted for ultrasound 3D data and has the objective to detect all potential defects inside the input volume. While the detection of defects is vital, one main difficulty is the high amount of false alarms which are detected by the segmentation procedure. The correct distinction of false alarms is necessary to reduce the rejection ratio of safe parts. This has to be done without risking missing true defects. Therefore, there is a need for a powerful classifier which can efficiently distinguish true defects from false alarms. This is achieved using a specific classification approach based on data fusion theory. The chain was tested on several ultrasound volumetric measures of Carbon Fiber Reinforced Polymers components. Experimental results of the chain revealed high accuracy, reliability in detecting, characterizing and classifying defects.<br
Keywords: Non-Destructive Testing of composite materials; data fusion; defects recognition; speckle noise; ultrasound volumes analysis

Automated Glaucoma Detection with Optical Coherence Tomography - Mayer, Markus Anton (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2018)
The number of patients suffering from the glaucoma disease will increase in the future. A further automation of parts of the diagnostic routine is inevitable to use limited examination times more efficiently. Optical coherence tomography (OCT) technology has become a widespread tool for glaucoma diagnosis, and data collections in the clinics have been built up in recent years that now allow for data mining and pattern recognition approaches to be applied to the diagnostic challenge. A complete pattern recognition pipeline to automatically discriminate glaucomatous from normal eyes with OCT data is proposed, implemented and evaluated. A data collection of 1024 Spectralis HRA+OCT circular scans around the optic nerve head from 565 subjects build the basis for this work. The data collection is labeled with 4 diagnoses: 453 healthy (H), 179 ocular hypertension (OHT), 168 preperimetric glaucoma (PPG), and 224 perimetric glaucoma (PG) eyes. In a first step, 6 retinal layer boundaries are automatically segmented by edge detection and the minimization of a custom energy functional, which was established in preceeding work by the author. The segmentation algorithm is evaluated on a subset consisting of 120 scans. The automatically segmented layer boundaries are compared to a gold standard (GS) created from manual corrections to the automated results by 5 observers. The mean absolute difference of the automated segmentation to the GS for the outer nerve fiber layer boundary is 2.84μm. The other layers have less or almost no segmentation error. No significant correlation between the segmentation error and scans of bad quality or glaucomatous eyes could be found for any layer boundary. The difference of the automated segmentation to the GS is not much worse than the single observer’s manual correction difference to the GS. In a second step, the thickness profiles generated by the segmentation are used in a classification system: In total, 762 features are generated, including novel ratio and principal component analysis features. “Forward selection and backward elimi- nation” selects the best performing features with respect to the classwise averaged classification rate (CR) on the training data. The segmentations of the complete dataset were manually corrected so that the classification experiments could either be run on manually corrected or purely automated segmentations. Three classifiers were compared. The support vector machine classifier (SVM) performed best in a 10-fold cross-validation and differentiated non-glaucomatous (H and OHT) from glaucomatous (PPG and PG) eyes with a CR of 0.859 on manually corrected data. The classification system adapts to the less reliable purely automated segmentations by choosing features of a more global scale. Training with manually corrected and testing with purely automated data and vice versa shows that it is of advance to use manually corrected data for training, no matter what the type of test data is. The distance of the feature vectors to the SVM decision boundary is used as a basis for a novel glaucoma probability score based on OCT data, the OCT-GPS.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_4
Keywords: Optische Kohärenztomografie; Maschinelles Lernen; Überwachtes Lernen; Bildverarbeitung; Bildsegmentierung; Glaukom

Automated Image Forgery Detection through Classification of JPEG Ghosts - Zach, Fabian; Riess, Christian; Angelopoulou, Elli (Pattern Recognition - Joint 34th DAGM and 36th OAGM Symposium, 2012)

Automated lamina cribrosa microstructural segmentation in optical coherence tomography scans of healthy and glaucomatous eyes - Nadler, Zach; Wang, Bo; Wollstein, Gadi; Nevins, Jessica; Ishikawa, Hiroshi; Kagemann, Larry; Sigal, Ian; Ferguson, Daniel; Hammer, Daniel; Grulkowski, Ireneusz; Liu, Jonathan J.; Kraus, Martin; et al. (Biomedical Optics Express, 2013)
We demonstrate an automated segmentation method for in-vivo 3D optical coherence tomography (OCT) imaging of the lamina cribrosa (LC). Manual segmentations of coronal slices of the LC were used as a gold standard in parameter selection and evaluation of the automated technique. The method was validated using two prototype OCT devices; each had a subject cohort including both healthy and glaucomatous eyes. Automated segmentation of in-vivo 3D LC OCT microstructure performed comparably to manual segmentation and is useful for investigative research and in clinical quantification of the LC. © 2013 Optical Society of America.
UnivIS-Import:2015-03-09:Pub.2013.tech.IMMD.IMMD5.automa_57

Automated Left Ventricle Segmentation in 2-D LGE-MRI - Kurzendorfer, Tanja; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_2
Keywords: Heart; Image Segmentation; Magnetic Resonance Imaging (MRI)

Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (STACOM 2019, 2019)
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject to inter- and intra-observer variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multi-sequence CMR segmentation task using transfer learning that combines multi-source image information. We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features. The domain-adapted network was trained with just four LGE-MR training samples and obtained an average Dice score of 85.0% on the test set comprises of 40 LGE-MR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGE-MR training data. <br

Automated postlaryngectomy telephone test - Haderlein, Tino; Riedhammer, Korbinian Thomas; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Rosanowski, Frank (HNO, 2009)
Objective: In this study, an objective version of the postlaryngectomy telephone test (PLTT) for measuring speech intelligibility based on automatic speech recognition is presented. Methods: Thirty-one patients with tracheoesophageal substitute voice (25 men and six women, 63.4±8.7 years) were evaluated by 11 naïve listeners. The automatic measurement of speech intelligibility was expressed by means of word accuracy and word recognition rates, or the percentage of correctly recognized words from a word sequence. These automatic measures were compared with the subjectively obtained PLTT values. Results: The average PLTT intelligibility of the 11 naïve listeners was 47%; the automatically obtained word accuracy and word recognition rates were much lower (approximately 0% and 15%, respectively). The correlation between subjective and automatic evaluation, however, reached more than 0.9 in some of the examined cases. Conclusion: Automatic speech recognition provides an efficient, objective measure that is equivalent to the overall PLTT intelligibility value. © 2009 Springer Medizin Verlag.
Keywords: Automated pattern recognition; Correlation of data; Evaluation methodology; Speech intelligibility; Speech recognition software

Automated quality assessment of retinal fundus photos - Paulus, Jan; Meier, Jörg; Bock, Rüdiger; Hornegger, Joachim; Michelson, Georg (International Journal of Computer Assisted Radiology and Surgery, 2010)
Objective: Automated, objective and fast measurement of the image quality of single retinal fundus photos to allow a stable and reliable medical evaluation. Methods: The proposed technique maps diagnosis-relevant criteria inspired by diagnosis procedures based on the advise of an eye expert to quantitative and objective features related to image quality. Independent from segmentation methods it combines global clustering with local sharpness and texture features for classification. Results: On a test dataset of 301 retinal fundus images we evaluated our method on a given gold standard by human observers and compared it to a state of the art approach. An area under the ROC curve of 95.3% compared to 87.2% outperformed the state of the art approach. A significant p-value of 0.019 emphasizes the statistical difference of both approaches. Conclusions: The combination of local and global image statistics models the defined quality criteria and automatically produces reliable and objective results in determining the image quality of retinal fundus photos. © 2010 CARS.

Automated segmentation of ultrasonic volumetric data of composite materials - Osman, A.; Hassler, U.; Kaftandjian, V.; Hornegger, Joachim (Insight, 2015)
Manual ultrasonic testing is frequently applied to inspect the quality of carbon fibre-reinforced polymer composites (CFRP), especially in the aerospace industry. There is an immediate requirement to replace manual ultrasonic testing and data evaluation with an automated system. A part of the solution is provided via automated acquisition of ultrasound data. The second part to be solved is the analysis of the enormous amount of data generated. The objective of this contribution is to propose a method that can provide an automated interpretation of data. The proposed method is tested on two CFRP samples with artificial defects of different sizes, depths and shapes.
Keywords: sampling phased array; composite materials; ultrasound image analysis; speckle noise reduction

Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data - Kajic, Vedran; Esmaeelpour, Marieh; Glittenberg, Carl; Kraus, Martin; Hornegger, Joachim; Othara, Richu; Binder, Susanne; Fujimoto, James G.; Drexler, Wolfgang (Biomedical Optics Express, 2013)

Automatically evaluated degree of intelligibility of children with different cleft type from preschool and elementary school measured by automatic speech recognition - Schuster, Maria; Maier, Andreas; Bocklet, Tobias; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Stelzle, Florian (International Journal of Pediatric Otorhinolaryngology, 2012)

Automatic Annotation and Classification of Phrase Accents in Spontaneous Speech - Batliner, Anton; Nutt, Matthias; Warnke, Volker; Nöth, Elmar; Buckow, Jan-Constantin; Huber, Richard; Niemann, Heinrich (Proc. European Conf. on Speech Communication and Technology, 1999)

Automatic Assessment of Alzheimer's across Three Languages Using Speech and Language Features - Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Braun, F.; Hönig, F.; Tobón-Quintero, C. A.; Aguillón, D.; Lopera, F.; Hincapié-Henao, L.; Schuster, M.; Riedhammer, K.; Maier, Andreas; Nöth, Elmar; et al. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
With the increasing prevalence of Alzheimer's Disease (AD) worldwide, it is essential to develop non-invasive methods to monitor the progression of the disease. Speech and language analyses are suitable for detecting the cognitive impairment of AD patients; thus, by analyzing changes in speech patterns and language use, researchers can develop methods to monitor AD remotely. In this paper, we investigated several speech and language techniques commonly used for the automatic detection of AD. Furthermore, we considered speech recordings of 448 patients in three different languages: Spanish (57), German (205), and English (186). Cross-lingual analysis was carried out using two classification approaches: (1) training/testing in one or more languages and (2) training in one language and testing in another. We obtained unweighted average recall values of up to 83% to classify AD using the first classification approach and up to 70% with the second.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Alzheimer's Disease; Cross-lingual Analysis; Deep Learning; Dementia; Pathological Speech Processing

Automatic Assessment of Children's Speech with Cleft Lip and Palate - Maier, Andreas; Nöth, Elmar; Nkenke, Emeka; Schuster, Maria (Language Technologies, IS-LTC 2006, 2006)

Automatic Assessment of Non-Native Prosody: Annotation, Modelling and Evaluation - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar (Proceedings of the International Symposium on Automatic Detection of Errors in Pronunciation Training, 2012)

Automatic B0 Drift Correction for MR Thermometry - Rothgang, Eva; Roland, Jörg; Gilson, Wesley D.; Hornegger, Joachim; Lorenz, Christine H. (Proceedings of International Society for Magnetic Resonance in Medicine, 2011)

Automatic Batik Motifs Classification using Various Combinations of SIFT Features Moments and k-Nearest Neighbor - Setyawan, Iwan; Timotius, Ivanna; Kalvin, Marchellius (International Conference on Information Technology and Electrical Engineering, 2015)
Batik cloth is Indonesia's national heritage. Across the archipelago, there are numerous patterns and motifs of batik, each having its own meaning and cultural significance. In this paper, we present the results of our investigation of various combinations of SIFT features moments used in automatic classification of batik motifs. The classification method used in this paper is the k-Nearest Neighbor. Our experiments show that the best performance of the system is obtained using feature vectors of length 7, yielding a classification accuracy rate of 31.43% for 7 classes of batik motifs with no batik motif classes having zero classification accuracy rate. Furthermore, our experiments suggest that the feature moment that seems to be the best for the classification process is the μc, while the feature moment that seems to hinder the classification process is the σc2.
Keywords: SIFT features; feature moments; pattern classification; batik motifs

Automatic Bolus Analysis for DCE-MRI Using Radial Golden-Angle Stack-of-stars GRE Imaging - Grimm, Robert; Li, Feng; Forman, Christoph; Hutter, Jana; Kiefer, Berthold; Hornegger, Joachim; Block, Kai Tobias (International Society for Magnetic Resonance in Medicine, 2013)

Automatic boost articulation therapy in adults with dysarthria: Acceptability, usability and user interaction - Ramos, Viviana Mendoza; Vasquez Correa, Juan; Cremers, Rani; Van Den Steen, Leen; Nöth, Elmar; De Bodt, Marc; Van Nuffelen, Gwen (International Journal of Language & Communication Disorders, 2021)
Background Imprecise articulation has a negative impact on speech intelligibility. Therefore, treatment of articulation is clinically relevant in patients with dysarthria. In order to be effective and according to the principles of motor learning, articulation therapy needs to be intensive, well organized, with adequate feedback and requires frequent practice. Aims The aims of this pilot study are (1) to evaluate the feasibility of a virtual articulation therapy (VAT) to guide patients with dysarthria through a boost articulation therapy (BArT) program; (2) to evaluate the acoustic models' performance used for automatic phonological error detection; and (3) to validate the system by end-users from their perspective. Methods & Procedures The VAT provides an extensive and well-structured package of exercises with visual and auditory modelling and adequate feedback on the utterances. The tool incorporates automated methods to detect phonological errors, which are specifically designed to analyse Dutch speech production. A total of 14 subjects with dysarthria evaluated the acceptability, usability and user interaction with the VAT based on two completed therapy sessions using a self-designed questionnaire. Outcomes & Results In general, participants were positive about the new computer-based therapy approach. The algorithm performance for phonological error detection shows it to be accurate, which contributes to adequate feedback of utterance production. The results of the study indicate that the VAT has a user-friendly interface that can be used independently by patients with dysarthria who have sufficient cognitive, linguistic, motoric and sensory skills to benefit from speech therapy. Recommendations were given by the end-users to further optimize the program and to ensure user engagement. Conclusions & Implications The initial implementation of an automatic BArT shows it to be feasible and well accepted by end-users. The tool is an appropriate solution to increase the frequency and intensity of articulation training that supports traditional methods. What this paper adds What is already known on the subject Behavioural interventions to improve articulation in patients with dysarthria demand intensive treatments, repetitive practice and feedback. However, the current treatments are mainly limited in time to the interactive sessions in the presence of speech-language pathology. Automatic systems addressing the needs of individuals with dysarthria are scarce. This study evaluates the feasibility of a VAT program and investigates its acceptability, usability and user interaction. What this paper adds to existing knowledge The computer-based speech therapy approach developed and applied in this study intends to support intensive articulation training of patients with dysarthria. The virtual speech therapy offers the possibility of an individualized and customized therapy programme, with an extensive database of exercises, visual and auditory models of the target utterances, and providing adequate feedback based on automatic acoustic analysis of speech. What are the potential or actual clinical implications of this work? The automatic BArT overcomes the limitation in time of face-to-face traditional speech therapy. It offers patients the opportunity to have access to speech therapy more intensively and frequently in their home environment.
CRIS-Team WoS Importer:2021-07-16

Automatic CAD-RADS Scoring using Deep Learning - Denzinger, Felix; Wels, Michael; Breininger, Katharina; Gülsün, Mehmet A.; Schöbinger, Max; Andre, Florian; Buss, Sebastian J.; Goerich, Johannes; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design – which allows comprehensive predictions.<br
Keywords: Coronary Artery Disease; Coronary CT Angiography; Deep Learning; Data Representation; CAD-RADS

Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks - Yoon, Seung Su; Hoppe, Elisabeth; Schmidt, Michaela; Forman, Christoph; Sharma, Puneet; Tillmanns, Christoph; Maier, Andreas; Wetzl, Jens (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition), 2019)

Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Automatic Classification and Pathological Staging of Confocal Laser Endomicroscopic Images of the Vocal Cords - Vo, Kim; Jaremenko, Christian; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Bildverarbeitung für die Medizin 2017 Algorithmen Systeme Anwendungen, 2017)
Confocal laser endomicroscopy is a novel imaging technique which provides real-time in vivo examination and histological analysis of tissue during an ongoing endoscopy. We present an automatic classification system that is able to differentiate between healthy and cancerous tissue of the vocal cords. Textural as well as CNN features are encoded using Fisher vectors and vector of locally aggregated descriptors while the classification is performed using random forests and support vector machines. Two experiments are investigated following a leave-one-sequence-out cross-validation and a fixed training and test set approach. Classification rates reach up to 87.6 % and 81.5 %, respectively.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa_7

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning - Aubreville, Marc; Knipfer, Christian; Oetter, Nicolai; Jaremenko, Christian; Rodner, Erik; Denzler, Joachim; Bohr, Christopher; Neumann, Helmut; Stelzle, Florian; Maier, Andreas (Scientific Reports, 2017)
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, su cient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 speci c locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, speci city 90%).
Keywords: Cancer imaging; translational research; deep learning

Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images - Deitsch, Sergiu; Christlein, Vincent; Berger, Stephan; Buerhop-Lutz, Claudia; Maier, Andreas; Gallwitz, Florian; Riess, Christian (Solar Energy, 2019)
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.<br
CRIS-Team Scopus Importer:2019-05-09
Keywords: Deep learning; defect classification; electroluminescence imaging; photovoltaic modules; regression analysis; support vector machines; visual inspection

Automatic Classification of Emotion-Related User States in Spontaneous Children's Speech - Steidl, Stefan (Logos Verlag, 2009)
The recognition of the user’s emotion-related state is one important step in making human-machine communication more natural. In this work, the focus is set on mono-modal systems with speech as only input channel. Current research has to shift from emotion portrayals to those states that actually appear in application-oriented scenarios. These states are mainly weak emotion-related states and mixtures of different states. The presented FAU Aibo Emotion Corpus is a major contribution in this area. It is a corpus of spontaneous, emotionally colored speech of children at the age of 10 to 13 years interacting with the Sony robot Aibo. 11 emotion-related states are labeled on the word level. Experiments are conducted on three subsets of the corpus on the word, the turn, and the intermediate chunk level. Best results have been obtained on the chunk level where a classwise averaged recognition rate of almost 70 % for the 4-class problem Anger, Emphatic, Neutral, and Motherese has been achieved. Applying the proposed entropy based measure for the evaluation of decoders, the performance of the machine classifier on the word level is even slightly better than the one of the average human labeler. The presented set of features covers both acoustic and linguistic features. The linguistic features perform slightly worse than the acoustic features. An improvement can be achieved by combining both knowledge sources. The acoustic features are categorized into prosodic, spectral, and voice quality features. The energy and duration based prosodic features and the spectral MFCC features are the most relevant acoustic features in this scenario. Unigram models and bag-of-words features are the most relevant linguistic features.
Keywords: Emotion; emotionale Benutzerzustände; Automatische Klassifikation; Kindersprache

Automatic Classification of Hypokinetic and Hyperkinetic Dysarthria based on GMM-Supervectors - Rios-Urrego, C. D.; Rusz, J.; Nöth, Elmar; Orozco-Arroyave, J. R. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023)
Hypokinetic and hyperkinetic dysarthria are motor speech disorders that appear in patients with Parkinson's and Huntington's disease, respectively. They are caused due to progressive lesions or alterations in the basal ganglia. In particular, Huntington's disease (HD) is known to be more invasive and difficult to treat than Parkinson's disease (PD), producing more aggressive motor and cognitive alterations. Since speech production requires the movement and control of many different muscles and limbs, it constitutes a highly complex motor activity that may reflect relevant aspects of the patient's health state. This paper proposes the discrimination between patients with PD, HD, and healthy controls (HC) based on different speech dimensions. Speaker models based on Gaussian-mixture model supervectors are created with the features extracted from each speech dimension. The results suggest that it is possible to distinguish between PD and HD patients using the supervectors-based approach.
CRIS-Team Scopus Importer:2023-09-29
Keywords: Articulation; Huntington's disease; Parkinson's disease; Pathological speech; Phonation; Prosody

Automatic Classification of Prosodically Marked Phrase Boundaries in German - Kompe, Ralf; Batliner, Anton; Kießling, Andreas; Kilian, Ute; Niemann, Heinrich; Nöth, Elmar; Regel-Brietzmann, Peter (ICASSP, 1994)

Automatic Classification of Reading Disorders in a Single Word Reading Test - Maier, Andreas; Horndasch, Stefanie; Nöth, Elmar (Workshop on Child, Computer, and Interaction 2009, 2009)

Automatic Classification of Sport Exercises for Training Support - Schuldhaus, Dominik; Leutheuser, Heike; Eskofier, Björn (Sportinformatik 2012, 2012)

Automatic clustering of code changes - Kreutzer, Patrick; Dotzler, Georg; Ring, Matthias; Eskofier, Björn; Philippsen, Michael (Proceedings of the 13th International Conference on Mining Software Repositories (MSR'16), 2016)
Several research tools and projects require groups of similar code changes as input. Examples are recommendation and bug finding tools that can provide valuable information to developers based on such data. With the help of similar code changes they can simplify the application of bug fixes and code changes to multiple locations in a project. But despite their benefit, the practical value of existing tools is limited, as users need to manually specify the input data, i.e., the groups of similar code changes. To overcome this drawback, this paper presents and evaluates two syntactical similarity metrics, one of them is specifically designed to run fast, in combination with two carefully selected and self-tuning clustering algorithms to automatically detect groups of similar code changes. We evaluate the combinations of metrics and clustering algorithms by applying them to several open source projects and also publish the detected groups of similar code changes online as a reference dataset. The automatically detected groups of similar code changes work well when used as input for LASE, a recommendation system for code changes.

Automatic, computer-based analysis of speech intelligibility on edentulous patients with and without complete dentures - preliminary results - Stelzle, Florian; Ugrinovic, Biljana; Knipfer, Christian; Bocklet, Tobias; Nöth, Elmar; Schuster, Maria; Eitner, Stefan; Seiss, Martin; Nkenke, Emeka (Journal of Oral Rehabilitation, 2010)

Automatic, computer-based speech assessment on edentulous patients with and without complete dentures - preliminary results - Stelzle, Florian; Ugrinovic, Biljana; Knipfer, Christian; Bocklet, Tobias; Nöth, Elmar; Schuster, Maria; Eitner, Stefan; Seiss, Martin; Nkenke, Emeka (Journal of Oral Rehabilitation, 2010)
Dental rehabilitation of edentulous patients with complete dentures includes not only aesthetics and mastication of food, but also speech quality. It was the aim of this study to introduce and validate a computer-based speech recognition system (ASR) for automatic speech assessment in edentulous patients after dental rehabilitation with complete dentures. To examine the impact of dentures on speech production, the speech outcome of edentulous patients with and without complete dentures was compared. Twenty-eight patients reading a standardized text were recorded twice - with and without their complete dentures in situ. A control group of 40 healthy subjects with natural dentition was recorded under the same conditions. Speech quality was evaluated by means of a polyphone-based ASR according to the percentage of the word accuracy (WA). Speech acceptability assessment by expert listeners and the automatic rating of the WA by the ASR showed a high correlation (corr = 0.71). Word accuracy was significantly reduced in edentulous speakers (55.42 +/- 13.1) compared to the control group's WA (69.79 +/- 10.6). On the other hand, wearing complete dentures significantly increased the WA of the edentulous patients (60.00 +/- 15.6). Speech production quality is significantly reduced after complete loss of teeth. Reconstitution of speech production quality is an important part of dental rehabilitation and can be improved for edentulous patients by means of complete dentures. The ASR has proven to be a useful and easily applicable tool for automatic speech assessment in a standardized way.
Keywords: speech quality; automatic speech recognition; objective speech assessment; edentulism; complete dentures; oral rehabilitation

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests - Chen, Shuqing; Stromer, Daniel; Alnasser Alabdalrahim, Harb; Schwab, Stefan; Weih, Markus; Maier, Andreas (Scientific Reports, 2020)
Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.<br

Automatic Detection and Analysis of Photovoltaic Modules in Aerial Infrared Imagery - Dotenco, Sergiu; Dalsaß, Manuel; Winkler, Ludwig; Wurzner, Tobias; Brabec, Christoph; Maier, Andreas; Gallwitz, Florian (IEEE Winter Conference on Applications of Computer Vision, 2016)
Drone-based aerial thermography has become a convenient quality assessment tool for the precise localization of defective modules and cells in large photovoltaic-power plants. However, manual evaluation of aerial infrared recordings can be extremely time-consuming. Therefore, we propose an approach for automatic detection and analysis of photovoltaic modules in aerial infrared images. Significant temperature abnormalities such as hot spots and hot areas can be identified using our processing pipeline. To identify such defects, we first detect the individual modules in infrared images, and then use statistical tests to detect the defective modules. A quantitative evaluation of the detection and analysis pipeline on real-world, infrared recordings shows the applicability of our approac

Automatic Detection and Evaluation of Edentulous Speakers with Insufficient Dentures - Bocklet, Tobias; Hönig, Florian Thomas; Haderlein, Tino; Stelzle, Florian; Knipfer, Christian; Nöth, Elmar (Proceedings of the 13th International Conference on Text, Speech and Dialogue, 2010)

Automatic Detection and Quantification of Mitral Regurgitation on TTE with Application to Assist Mitral Clip Planning and Evaluation - Vitanovski, Dime; Voigt, Ingmar; et al. (Proceedings of the MICCAI Workshop on Clinical Image-based Procedures: From Planning to Intervention, 2012)

Automatic detection and segmentation of focal liver lesions in contrast enhanced CT images - Militzer, Arne; Hager, Tobias; Jäger, Florian; Tietjen, Christian; Hornegger, Joachim (2010 20th International Conference on Pattern Recognition, ICPR 2010, 2010)
In this paper a novel system for automatic detection and segmentation of focal liver lesions in CT images is presented. It utilizes a probabilistic boosting tree to classify points in the liver as either lesion or parenchyma, thus providing both detection and segmentation of the lesions at the same time and fully automatically. To make the segmentation more robust, an iterative classification scheme is integrated, that incorporates knowledge gained from earlier iterations into later decisions. Finally, a comprehensive evaluation of both the segmentation and the detection performance for the most common hypodense lesions is given. Detection rates of 77% could be achieved with a sensitivity of 0.95 and a specificity of 0.93 for lesion segmentation at the same settings. © 2010 IEEE.

Automatic detection of articulation disorders in children with cleft lip and palate - Maier, Andreas; Hönig, Florian Thomas; Bocklet, Tobias; Nöth, Elmar; Stelzle, Florian; Nkenke, Emeka; Schuster, Maria (Journal of the Acoustical Society of America, 2009)

Automatic detection of hypernasal speech signals using nonlinear and entropy measurements - Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. E.; Nöth, Elmar (2012)
Automatic hypernasality detection in children with Cleft Lip and Palate is classically performed by means of acoustic analysis; however, recent findings indicate that nonlinear dynamics features could be useful for this task. In order to continue deepening in this issue, in this paper the discriminant capability of 4 different nonlinear dynamics features along with a set of 6 entropy measurements is studied. The whole set of features is optimized using an automatic feature selection technique based on principal component analysis. The decision about the presence or absence of hypemasality is made by employing a support vector machine. The system is tested over two databases, one considers the five Spanish vowels and the words /coco/ and /gato/, and the other one considers different German words. The performance of the system is presented in terms of accuracy, sensitivity, specificity and receiver operating curves. According to the results, the accuracy of system increases when nonlinear and entropy measures are combined.
Keywords: Hypernasality; Cleft Lip and Palate; nonlinear dynamics; entropy measures

Automatic detection of inertial sensor orientation for movement analysis in Parkinsons disease - Maier, Jennifer; Reinfelder, Samuel; Barth, Jens; Klucken, Jochen; Eskofier, Björn (BSA Conference 2013 - Biosignal Analysis, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.automa_27

Automatic Detection of Ostia in the Left Atrium - Hoffmann, Matthias; Koch, Martin; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.automa

Automatic Detection of Parkinson's Disease from Compressed Speech Recordings - Orozco Arroyave, Juan Rafael; Garcia, Nicanor; Vargas-Bonilla, Jesus Francisco; Nöth, Elmar (18th International Conference on Text, Speech and Dialogue, TSD 2015, 2015)
The impact of speech compression in the automatic classification of speakers with Parkinson's disease (PD) and healthy controls (HC) is tested. The set of codecs considered to compress the speech recordings includes G. 722, G. 226, GSM-EFR, AMR-WB, SILK, and Opus. A total of 100 speakers (50 with PD and 50 HC) are asked to read a text with 36 words. The recordings are compressed from bit-rates of 705.6 kbps down to 6.6 kbps. The method addressed to discriminate between speakers with PD and HC consists on the systematic segmentat ion of voiced and unvoiced speech frames. Each kind of frame is characterized independently. For voiced segments noise, perturbation, and cepstral features are considered. The unvoiced segments are characterized with Bark band energies and cepstral features. According to the results the codecs evaluated in this paper do not affect significantly the accuracy of the system, indicating that the addressed methodology could be used for the telemonitoring of PD patients through Internet or through the mobile communications network.
Keywords: Parkinson's disease; Speech compression; Speech codec; Voiced/unvoiced frames; Internet; Telemonitoring; Mobile communications network

Automatic detection of Parkinson's disease from continuous speech recorded in non-controlled noise conditions - Vásquez-Correa, J. C.; Arias-Vergara, T.; Orozco-Arroyave, J. R.; Vargas-Bonilla, J. F.; Arias-Londoño, J. D.; Nöth, Elmar (16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015, 2015)
Automatic classification of Parkinson's disease (PD) speakers and healthy controls (HC) is performed considering speech recordings collected in non-controlled noise conditions. The speech tasks include six sentences and a read text. The recording is performed using an open source portable device and a commercial microphone. A speech enhancement (SE) technique is applied to improve the quality of the signals. Voiced and unvoiced frames are segmented from the speech tasks and characterized separately. The discrimination of speakers with PD and HC is performed using a support vector machine with soft margin. The results indicate that it is possible to discriminate between PD and HC speakers using recordings collected in non-controlled noise conditions. The accuracies obtained with the voiced features range from 64% to 86%. For unvoiced features the accuracies range from 78% to 99%. The SE algorithm improves the accuracies of the unvoiced frames in up to 11 percentage points, while the accuracies decrease in the voiced frames when the SE algorithm is applied. This work is a step forward to the development of portable devices to assess the speech of people with PD.
Keywords: Non-controlled noise conditions; Parkinson's disease; Speech; Speech enhancement; Voiced/unvoiced

Automatic detection of Parkinson's disease fromwords uttered in three different languages - Orozco-Arroyave, J. R.; Hönig, Florian Thomas; Arias-Londoño, J. D.; Vargas-Bonilla, J. F.; Skodda, S.; Rusz, J.; Nöth, Elmar (15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014, 2014)
About 90% of the people with Parkinson's disease (PD) develop speech impairments such as monopitch, monoloudness, imprecise articulation, and other symptoms. There are several studies addressing the problem of the automatic detection of PD from speech signals in order to develop computer aided tools for the assessment and monitoring of the patients. Recent works have shown that it is possible to detect PD from speech with accuracies above 90%; however, it is still unclear whether it is possible to make the detection independent of the spoken language. This paper addresses the automatic detection of PD considering speech recordings of three languages: German, Spanish and Czech. According to the results it is possible to classify between speech of people with PD and healthy controls (HC) with accuracies ranging from 84% to 99%, depending on the utterance.
Keywords: Acoustic measures; Dysarthria; Language; Parkinson's disease; Speech disorder; Voiced/unvoiced segments

Automatic Detection of Parkinson's Disease in Reverberant Environments - Orozco-Arroyave, Juan Rafael; Haderlein, Tino; Nöth, Elmar (18th International Conference on Text, Speech and Dialogue, TSD 2015, 2015)
Automatic classification of speakers with Parkinson's disease (PD) and healthy controls (HC) is performed considering a method for the characterization of the speech signals which is based on the estimation of the energy content of the unvoiced frames. The method is tested with recordings of three languages: Spanish, German, and Czech. Additionally, the signals are affected by two different reverberant scenarios in order to validate the robustness of the proposed method. The obtained results range from 85% to 99% of accuracy depending on the speech task, the spoken language, and the recording scenario. The method shows to be accurate and robust even when the signals are reverberated. This work is a step forward to the development of methods to assess the speech of PD patients without requiring special acoustic conditions.
Keywords: Parkinson's disease; Reverberant evironments; Unvoiced frames; Multi-language

Automatic detection of Parkinson's disease in running speech spoken in three different languages. - Orozco-Arroyave, J. R.; Hönig, Florian Thomas; Arias-Londoño, J. D.; Vargas-Bonilla, J. F.; Daqrouq, K.; Skodda, S.; Rusz, J.; Nöth, Elmar (The Journal of the Acoustical Society of America, 2016)
The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.

Automatic detection of Parkinson's disease using noise measures of speech - Belalcazar-Bolaños, E. A.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Vargas-Bonilla, J. F.; Nöth, Elmar (2013 18th Symposium of Image, Signal Processing, and Artificial Vision, STSIVA 2013, 2013)
Parkinson's disease (PD) is a neurodegenerative disorder that is characterized by the loss of dopaminergic neurons in the mid brain. It is demonstrated that about 90% of the people with PD also develop speech impairments, exhibiting symptoms such as monotonic speech, low pitch intensity, inappropriate pauses, imprecision in consonants and problems in prosody; although they are already identify problems, only 3% to 4% of the patients receive speech therapy. The research community has addressed the problem of the automatic detection of PD by means of noise measures; however, in such works only the phonation of the English vowel /a/ has been considered. In this paper, the five Spanish vowels uttered by 50 people with PD and 50 healthy controls (HC) are evaluated automatically considering a set of four noise measures: Harmonics to Noise Ratio (HNR), Normalized Noise Energy (NNE), Cepstral HNR (CHNR) and Glottal to Noise Excitation Ratio (GNE). The decision on whether a speech recording is from a person with PD or from a HC is taken by a K nearest neighbors (k-NN) classifier, finding an accuracy of 66.57% when only the vowel /i/ is considered. © 2013 IEEE.
Keywords: k-nearest neighbor; Noise measures; Parkinson's disease; Spanish vowels

Automatic Detection of Sigmatism in Children - Botinhao, Cassia; Degenkolb-Weyers, Sabine; Maier, Andreas; Nöth, Elmar; Eysholdt, Ulrich; Bocklet, Tobias (WOCCI 2012 - Workshop on Child, Computer and Interaction, 2012)

Automatic Detection of Stent Graft Markers in 2-D Fluoroscopy Images - Reiml, Sabrina; Pfister, Marcus; Toth, Daniel; Maier, Andreas; Hoffmann, Matthias; Kowarschik, Markus; Hornegger, Joachim (Joint MICCAI workshop on Computing and Visualisation for Intravascular Imaging and Computer-Assisted Stenting, 2015)
UnivIS-Import:2016-06-01:Pub.2015.tech.IMMD.IMMD5.automa_90

Automatic detection of Voice Onset Time in voiceless plosives using gated recurrent units - Arias Vergara, Tomás; Arguello-Velez, P.; Vasquez Correa, Juan; Nöth, Elmar; Schuster, M.; González-Rátiva, M. C.; Orozco Arroyave, Juan Rafael (Digital Signal Processing, 2020)
Voice Onset Time (VOT) has been used by researchers as an acoustic measure in order to gain some understanding about the impact of different motor speech disorders in speech production. However, VOT values are usually obtained manually, which is expensive and time consuming. In this paper we proposed a method for the automatic detection of VOT based on pre-trained Recurrent Neural Networks with Gated Recurrent Units (GRUs). Speech recordings from 50 Spanish native speakers from Colombia (25 male) are considered for the experiments. The recordings include the utterance of the diadochokinesis task /pa-ta-ka/ which is typically used for the evaluation of motor speech disorders like those caused due to Parkinson's disease. Additionally, the diadochokinesis task allows us to train a system to detect the VOT of voiceless plosive sounds in intermediate positions. Acoustic analysis is performed by extracting different temporal and spectral features from the recordings. According to the results, it is possible to detect the VOT with F1-score values of 0.66 for [Formula presented], 0.75 for [Formula presented], and 0.78 for [Formula presented] when the predicted values are compared with respect to the manual VOT labels.
CRIS-Team Scopus Importer:2020-06-09
Keywords: Diadochokinesis; Recurrent neural network; Voice Onset Time; Voiceless stop consonants

Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models - Fritsch, Julian; Wankerl, Sebastian; Nöth, Elmar (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019)
In today's aging society, the number of neurodegenerative diseases such as Alzheimer's disease (AD) increases. Reliable tools for automatic early screening as well as monitoring of AD patients are necessary. For that, semantic deficits have been shown to be useful indicators. We present a way to significantly improve the method introduced by Wankerl et al. [1]. The purely statistical approach of n-gram language models (LMs) is enhanced by using the rwthlm toolkit to create neural network language models (NNLMs) with Long Short Term-Memory (LSTM) cells. The prediction is solely based on evaluating the perplexity of transliterations of descriptions of the Cookie Theft picture from DementiaBank's Pitt Corpus. Each transliteration is evaluated on LMs of both control and Alzheimer speakers in a leave-one-speaker-out cross-validation scheme. The resulting perplexity values reveal enough discrepancy to classify patients on just those two values with an accuracy of 85.6% at equal-error-rate.
CRIS-Team Scopus Importer:2019-07-23
Keywords: Alzheimer's disease; automatic diagnosis; language models; Long-Short Term Memory; neural network language models

Automatic ECG Arrhythmia Detection in Real-Time on Android-based Mobile Devices - Leutheuser, Heike; Gottschalk, Tristan; Anneken, Lars; Struck, Matthias; Heuberger, Albert; Achenbach, Stephan; Eskofier, Björn; Arnold, Martin (Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa_72

Automatic Evaluation of Characteristic Speech Disorders in Children with Cleft Lip and Palate - Maier, Andreas; Hönig, Florian Thomas; Hacker, Christian; Schuster, Maria; Nöth, Elmar (Interspeech 2008, 2008)

Automatic Evaluation of Dysarthric Speech and Telemedical Use in the Therapy - Nöth, Elmar; Maier, Andreas; Gebhard, Arnd; Bocklet, Tobias; Schupp, Wilfried; Schuster, Maria; Haderlein, Tino (The Phonetician, 2011)

Automatic Evaluation of Parkinson's Speech - Acoustic, Prosodic and Voice Related Cues - Bocklet, Tobias; Steidl, Stefan; Nöth, Elmar; Skodda, Sabine (Proceedings of the 14th Annual Conference of the International Speech Communication Association (INTERSPEECH 2013), 2013)

Automatic Evaluation of Pathologic Speech - from Research to Routine Clinical Use - Nöth, Elmar; Maier, Andreas; Haderlein, Tino; Riedhammer, Korbinian Thomas; Rosanowski, Frank; Schuster, Maria (Text, Speech and Dialogue, 2007)

Automatic evaluation of prosodic features of tracheoesophageal substitute voice - Haderlein, Tino; Nöth, Elmar; Toy, Hikmet; Batliner, Anton; Schuster, Maria; Eysholdt, Ulrich; et al. (European Archives of Oto-Rhino-Laryngology, 2007)
In comparison with laryngeal voice, substitute voice after laryngectomy is characterized by restricted aero-acoustic properties. Until now, an objective means of prosodic differences between substitute and normal voices does not exist. In a pilot study, we applied an automatic prosody analysis module to 18 speech samples of laryngectomees (age: 64.2 ± 8.3 years) and 18 recordings of normal speakers of the same age (65.4 ± 7.6 years). Ninety-five different features per word based upon the speech energy, fundamental frequency F and duration measures on words, pauses and voiced/voiceless sections were measured. These reflect aspects of loudness, pitch and articulation rate. Subjective evaluation of the 18 patients' voices was performed by a panel of five experts on the criteria "noise", "speech effort", "roughness", "intelligibility", "match of breath and sense units" and "overall quality". These ratings were compared to the automatically computed features. Several of them could be identified being twice as high for the laryngectomees compared to the normal speakers, and vice versa. Comparing the evaluation data of the human experts and the automatic rating, correlation coefficients of up to 0.84 were measured. The automatic analysis serves as a good means to objectify and quantify the global speech outcome of laryngectomees. Even better results are expected when both the computation of the features and the comparison method to the human ratings will have been revised and adapted to the special properties of the substitute voices. © 2007 Springer-Verlag.

Automatic Evaluation of Tracheoesophageal Substitute Voice: Sustained Vowel versus Standard Text - Bocklet, Tobias; Toy, Hikmet; Nöth, Elmar; Schuster, Maria; Eysholdt, Ulrich; Rosanowski, Frank; Gottwald, Frank; Haderlein, Tino (Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP), 2009)

Automatic Evaluation of Tracheoesophageal Telephone Speech - Riedhammer, Korbinian Thomas; Haderlein, Tino; Schuster, Maria; Rosanowski, Frank; Nöth, Elmar (Proceedings of the 5th Slovenian and 1st International Conference Language Technologies IS-LTC 2006, 2006)
UnivIS-Import:2015-10-26:Pub.2006.tech.IMMD.IMMD5.automa_4

Automatic Evaluation of Voice Quality Using Text-Based Laryngograph Measurements and Prosodic Analysis - Haderlein, Tino; Schwemmle, Cornelia; Döllinger, Michael; Matoušek, Václav; Ptok, Martin; Nöth, Elmar (Computational and Mathematical Methods in Medicine, 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.automa

Automatic Extraction of 3D Dynamic Left Ventricle Model From 2D Rotational Angiocardiogram - Chen, Mingqing; Zheng, Yefeng; Müller, Kerstin; Rohkohl, Christopher; Lauritsch, Günter; Boese, Jan; Funka-Lea, Gareth; Hornegger, Joachim; Comaniciu, Dorin (Springer-verlag, 2011)
In this paper, we propose an automatic method to directly extract 3D dynamic left ventricle (LV) model from sparse 2D rotational angiocardiogram (each cardiac phase contains only five projections). The extracted dynamic model provides quantitative cardiac function for analysis. The overlay of the model onto 2D real-time fluoroscopic images provides valuable visual guidance during cardiac intervention. Though containing severe cardiac motion artifacts, an ungated CT reconstruction is used in our approach to extract a rough static LV model. The initialized LV model is projected onto each 2D projection image. The silhouette of the projected mesh is deformed to match the boundary of LV blood pool. The deformation vectors of the silhouette are back-projected to 3D space and used as anchor points for thin plate spline (TPS) interpolation of other mesh points. The proposed method is validated on 12 synthesized datasets. The extracted 3D LV meshes match the ground truth quite well with a mean point-to-mesh error of 0.51±0.11mm. The preliminary experiments on two real datasets (included a patient and a pig) show promising results too. © 2011 Springer-Verlag.
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD5.automa_56

Automatic Feature Selection in Large-Scale System-Software Product Lines - Ruprecht, Andreas; Heinloth, Bernhard; Lohmann, Daniel (Proceedings of the 2014 International Conference on Generative Programming: Concepts and Experiences, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD4.automa
Keywords: CADOS; VAMOS; Linux; Tailoring; optimiziaton; experimentation

Automatic Finger Joint Detection for Volumetric Hand Imaging - Bopp, Johannes; Unberath, Mathias; Steidl, Stefan; Fahrig, Rebecca; Oliveira, Isabelle; Kleyer, Arnd; Maier, Andreas (Bildverarbeitung für Medizin, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.automa_9

Automatic Image-to-Model Framework for Patient-specific Electromechanical Modeling of the Heart - Neumann, Dominik; Tommaso, Mansi; Grbic, Sasa; Voigt, Ingmar; Georgescu, Bogdan; Kayvanpour, Elham; Amr, Ali; Sedaghat-Hamedani, Farbod; Haas, Jan; Katus, Hugo; Meder, Benjamin; Hornegger, Joachim; et al. (2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.automa_6

Automatic Intelligibility Assessment of Parkinson's Disease with Diadochokinetic Exercises - Felipe Parra-Gallego, L.; Arias-Vergara, Tomas; Camilo Vasquez-Correa, Juan; Garcia-Ospina, Nicanor; Rafael Orozco-Arroyave, Juan; Nöth, Elmar (APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT II, 2018)
CRIS-Team WoS Importer:2019-09-06

Automatic intelligibility assessment of pathologic speech over the telephone - Haderlein, Tino; Nöth, Elmar; Batliner, Anton; Eysholdt, Ulrich; Rosanowski, Frank (Logopedics Phoniatrics Vocology, 2011)

Automatic intelligibility assessment of speakers after laryngeal cancer by means of acoustic modeling - Bocklet, Tobias; Riedhammer, Korbinian Thomas; Nöth, Elmar; Eysholdt, Ulrich; Haderlein, Tino (Journal of Voice, 2012)

Automatic Labeling of Phrase Accents in German - Kießling, Andreas; Kompe, Ralf; Batliner, Anton; Niemann, Heinrich; Nöth, Elmar (Proc. Int. Conf. on Spoken Language Processing, 1994)

Automatic Layer Generation for Scar Transmurality Visualization - Reiml, Sabrina; Kurzendorfer, Tanja; Toth, Daniel; Mountney, Peter; Panayiotou, Maria; Behar, Jonathan M.; Rinaldi, Christopher A.; Rhode, Kawal; Maier, Andreas; Brost, Alexander (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.automa

Automatic malignancy estimation for pulmonary nodules from CT images - Mentl, Katrin; Saffoury, Rimon; Maier, Andreas (Informatik aktuell, 2018)
Early detection of lung cancer is crucial to increase the chance of cure. As lung cancer often manifests itself in the presence of malignant pulmonary nodules, the assessment of such is of high clinical importance. Lung cancer screening is primarily performed using diagnostic imaging modalities such as CT, while invasive methods such as biopsy are used as a last resort to confirm diagnosis.
CRIS-Team Scopus Importer:2022-06-05

Automatic measurement of contrast bolus distribution in carotid arteries using a C-arm angiography system to support interventional perfusion imaging - Fieselmann, Andreas; Ganguly, Arundhuti; Yu Deuerling-Zheng,; Boese, Jan; Hornegger, Joachim; Fahrig, Rebecca (Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 2011)
Brain perfusion CT using a C-arm angiography system capable of CT-like imaging could optimize patient treatment during stroke therapy procedures. For this application, an intra-arterial contrast bolus injection at the aortic arch could be used provided that the location of the injection catheter enables uniform distribution of the bolus into the two common carotid arteries (CCA). In this work, we present a novel method to support optimal injection catheter placement by providing additional quantitative information about the distribution of the contrast bolus into the CCAs. Our fully automatic method uses 2-D digital subtraction angiography (DSA) images following a test bolus injection. It segments both CCAs and computes the relative contrast distribution. We have tested the method in DSA data sets from 5 healthy pigs and our method achieved successful segmentation of both CCAs in all data sets. The results showed that the contrast is uniformly distributed (mean relative difference less or equal than 10%) if the injection location is properly chosen. © 2011 SPIE.

Automatic Modelling of Depressed Speech: Relevant Features and Relevance of Gender - Hönig, Florian Thomas; Batliner, Anton; Nöth, Elmar; Schnieder, Sebastian; Krajewski, Jarek (Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association (INTERSPEECH 2014), 2014)
Depression is an affective disorder characterised by psychomotor retardation; in speech, this shows up in reduction of pitch (variation, range), loudness, and tempo, and in voice qualities different from those of typical modal speech. A similar reduction can be observed in sleepy speech (relaxation). In this paper, we employ a small group of acoustic features modelling prosody and spectrum that have been proven successful in the modelling of sleepy speech, enriched with voice quality features, for the modelling of depressed speech within a regression approach. This knowledge-based approach is complemented by and compared with brute-forcing and automatic feature selection. We further discuss gender differences and the contributions of (groups of) features both for the modelling of depression and across depression and sleepiness.
UnivIS-Import:2015-07-08:Pub.2014.tech.IMMD.IMMD5.automa_55
Keywords: Acoustic features; Brute forcing; Depression; Interpretation; Paralinguistics

Automatic Motion Estimation and Compensation Framework forWeight-bearing C-arm CT scans using Fiducial Markers - Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Maier, Andreas; Fahrig, Rebecca (IFMBE Proceedings, 2015)
UnivIS-Import:2018-09-06:Pub.2015.tech.IMMD.IMMD5.automa_2

Automatic Multi-modal ToF/CT Organ Surface Registration - Müller, Kerstin; Bauer, Sebastian; Wasza, Jakob; Hornegger, Joachim (Bildverarbeitung für die Medizin 2011, 2011)

Automatic Multi-organ Segmentation in Dual Energy CT (DECT) with Dedicated 3D Fully Convolutional DECT Networks - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (Medical Physics, 2020)
Purpose Dual‐energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single‐energy CT (SECT). Recent research shows that automatic multi‐organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. Methods In the scope of this work, we proposed four three‐dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. Results Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual‐source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross‐validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual‐energy spectra and outperform deep learning for SECT. Conclusions The results of the cross‐validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.
Keywords: Multi-organ; DECT; FCN; U-Net; deep learning

Automatic Multi-Organ Segmentation in Dual Energy CT using 3D Fully Convolutional Network - Chen, Shuqing; Zhong, Xia; Hu, Shiyang; Dorn, Sabrina; Kachelriess, Marc; Lell, Michael; Maier, Andreas (MIDL, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.automa_98

Automatic No-Reference Quality Assessment for Retinal Fundus Images Using Vessel Segmentation - Köhler, Thomas; Budai, Attila; Kraus, Martin; Odstrcilik, Jan; Michelson, Georg; Hornegger, Joachim (2013 26th IEEE International Symposium on Computer-Based Medical Systems (CBMS), 2013)

Automatic Objective Analysis of Speech Disorders on Patients with Oral Squamous Cell Carcinoma - Stelzle, Florian; Schuster, Maria; Maier, Andreas; Bocklet, Tobias; Nöth, Elmar; Seiß, Martin; Neukam, Friedrich Wilhelm; Nkenke, Emeka (3rd International IZKF-Symposium, 2009)

Automatic Parameter Selection for Multi-Modal Image Registration - Hahn, Dieter; Daum, Volker; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2010)
Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CTMR, PETMR, and MRMR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion-corrected magnetic resonance images. © 2010 IEEE.
UnivIS-Import:2015-03-09:Pub.2010.tech.IMMD.IMMD5.automa

Automatic Patient Pose Estimation Using Pressure Sensing Mattresses - Grimm, Robert; Sukkau, Johann; Hornegger, Joachim; Greiner, Günther (Informatik aktuell, 2011)

AUTOMATIC PHONEME ANALYSIS IN CHILDREN WITH CLEFT LIP AND PALATE - Bocklet, Tobias; Riedhammer, Korbinian Thomas; Eysholdt, Ulrich; Nöth, Elmar (2013)
Cleft Lip and Palate (CLP) is among the most frequent congenital abnormalities. The impaired facial development affects the articulation, with different phonemes being impacted inhomogeneously among different patients. This work focuses on automatic phoneme analysis of children with CLP for a detailed diagnosis and therapy control. In clinical routine, the state-of-the-art evaluation is based on perceptual evaluations. Perceptual ratings act as ground-truth throughout this work, with the goal to build an automatic system that is as reliable as humans. We propose two different automatic systems focusing on modeling the articulatory space of a speaker: one system models a speaker by a GMM, the other system employs a speech recognition system and estimates fMLLR matrices for each speaker. SVR is then used to predict the perceptual ratings. We show that the fMLLR-based system is able to achieve automatic phoneme evaluation results that are in the same range as perceptual inter-rater-agreements.
Keywords: Pathology; automatic assessment; spectral features; GMM; fMLLR