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)

Cell Culture Monitoring with Novel Bright Field Miniature Microscopy Prototypes - Schöll, Simon (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
Cell cultures are monitored to develop new drugs, to find efficient ways to produce vaccines and to perform toxicity tests. The cells are cultivated in an incubator and the monitoring steps such as the acquisition of images and the counting of cells are often done outside. As part of a research project, novel bright field miniature microscopy prototypes were developed. These prototypes were designed to work inside the incubator, and hence, they need to be very small. In this thesis, image processing methods for these systems (at different development stages) are presented. These methods made the systems usable for cell monitoring in an incubator. This is a main contribution of the thesis. Our analyses of the system and its components helped to improve the development of the systems. A calibration procedure and algorithms for adjusting the illumination and the focus position of these systems are introduced. Moreover, the proposed preprocessing steps such as illumination correction and contrast enhancement improved the image quality. An image processing library and a cell monitoring software using the library were developed. An algorithm for counting cells in images of the prototype system was included in the image processing library. Features for viability determination were investigated and also included in the library. Another main contribution is related to all bright field microscopes. They have the following effect in common: Focusing of very thin (phase) objects differs from focusing of objects that are thicker and less transparent for light. This effect is investigated in detail, explained, and the calculation of different useful focus positions for phase objects is derived. The optical focus position can be used for applications such as phase retrieval. Slightly defocused cell images with a maximum in contrast at small details can be useful for applications such as cell segmentation or cell analysis. Strongly defocused cell images with a maximum in contrast for the cell borders can be used for applications such as cell detection.<br
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.cellcu
Keywords: cell culture

Epithelial Cell Detection in Endomicroscopy Images of the Vocal Folds - Mualla, Firas; Schöll, Simon; Bohr, Christopher; Neumann, Helmut; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.epithe

Image Preprocessing Pipeline for Bright-Field Miniature Live Cell Microscopy Prototypes - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas (Springer Proceedings in Physics 154, Proceedings of InterM, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.imagep

Improving Joint Learning of Suspended and Adherent Cell Detection Using Low-Pass Monogenic Phase and Transport of Intensity Equation - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.improv_8

Influence of the phase effect on gradient-based and statistics-based focus measures in bright field microscopy - Schöll, Simon; Mualla, Firas; Sommerfeldt, Björn; Steidl, Stefan; Maier, Andreas; Buchholz, Rainer; Hornegger, Joachim (Journal of Microscopy, 2014)
Autofocusing is essential to high throughput microscopy and live cell imaging and requires reliable focus measures. Phase objects such as separated single Chinese hamster ovary cells are almost invisible at the optical focus position in bright field microscopy images. Because of the phase effect, defocused images of phase objects have more contrast. In this paper, we show that widely used focus measures exhibit an untypical behaviour for such images. In the case of homogeneous cells, that is, when most cells tend to lie in the same focal plane, both gradient-based and statistics-based focus measures tend to have a local minimum instead of a global maximum at the optical focus position. On the other hand, if images show inhomogeneous cells, gradient-based focus measures tend to yield typical focus curves, whereas statistics-based focus measures deliver curves similar to the case of homogeneous cells. These results were interpreted using the equation describing the phase effect and patch-wise analysis of the focus curves. Bioprocess engineering experts are also influenced by the phase effect. Forty-four focus positions selected by them led to the conclusion that they prefer to look at defocused images instead of those at the optical focus. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.influe
Keywords: Bright field microscopy; Focus measures; Nontypical focus curves; Phase effect

STXM goes 3D: Digital reconstruction of focal stacks as novel approach towards confocal soft x-ray microscopy - Späth, Andreas; Schöll, Simon; Riess, Christian; Schmidtel, Daniel; Paradossi, Gaio; Raabe, Jörg; Hornegger, Joachim; Fink, Rainer (Ultramicroscopy, 2014)
Fresnel zone plate based soft x-ray transmission microspectroscopy has developed into a routine technique for high-resolution elemental or chemical 2D imaging of thin film specimens. The availability of high resolution Fresnel lenses with short depth of focus offers the possibility of optical slicing (in the third dimension) by focus series with resolutions in the submicron regime. We introduce a 3D reconstruction algorithm that uses a variance-based metric to assign a focus measure as basis for volume rendering. The algorithm is applied to simulated geometries and opaque soft matter specimens thus enabling 3D visualization. These studies with z-resolution of few 100. nm serve as important step towards the vision of a confocal transmission x-ray microscope. © 2014 Elsevier B.

Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (Lecture Notes in Computer Science, Volume 8675, MICCAI 2014 Proceedings, Part III, 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.unsupe_6

Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm. - Muallal, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
CRIS-Team Scopus Importer:2022-08-05

Using the Low-Pass Monogenic Signal Framework for Cell/Background Classification on Multiple Cell Lines in Bright-Field Microscope Images - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose: Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal framework. The purpose of this paper is to show that using the local phase of the aforementioned monogenic signal instead of the defocused image improves the cell/background classification accuracy. Materials and methods: The paper statement was tested on an image database composed of three cell lines: adherent CHO, adherent L929, and Sf21 in suspension. Local phase and local energy images were generated using the low-pass monogenic signal framework with axial derivative images as input. Machine learning was then employed to investigate the discriminative power of the local phase. Three classifier models were utilized: random forest (RF), support vector machine (SVM) with a linear kernel, and SVM with a radial basis function (RBF) kernel. Results: The improvement, averaged over cell lines, of classifying 5 x 5 sized patches extracted from the local phase image instead of the defocused image was 7.3 % using the RF, 11.6 % using the linear SVM, and 10.2 % when a RBF kernel was employed instead of the linear one. Furthermore, the feature images can be sorted by increasing discriminative power as follows: at-focus signal, local energy, defocused signal, local phase. The only exception to this order was the superiority of local energy over defocused signal for suspended cells. Conclusions: Local phase computed using the low-pass monogenic signal framework considerably outperforms the defocused image for the purpose of pixel-patch cell/background classification in bright-field microscopy. © 2013 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.usingt
Keywords: Bright-field microscopy; Cell detection; Local phase; Machine learning; Monogenic signal; Transport of intensity equation

Using the Monogenic Signal for Cell-Background Classification in Bright-Field Microscope Images - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Hornegger, Joachim (Proceedings des Workshops Bildverarbeitung für die Medizin 2013, 2013)