A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy

Conference contribution


Publication Details

Author(s): Fischer P, Pohl T, Köhler GT, Maier A, Hornegger J
Title edited volumes: Information processing in medical imaging : proceedings of the ... conference
Publisher: Springer Verlag
Publishing place: Berlin
Publication year: 2015
Title of series: LNCS
Volume: 9123
Conference Proceedings Title: 24th International Conference on Information Processing in Medical Imaging, IPMI 2015
Pages range: 288-299
ISBN: 978-3-319-19991-7
ISSN: 1011-2499


Abstract


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.



FAU Authors / FAU Editors

Fischer, Peter
Lehrstuhl für Informatik 5 (Mustererkennung)
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Köhler, Gerhard Thomas
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


How to cite

APA:
Fischer, P., Pohl, T., Köhler, G.T., Maier, A., & Hornegger, J. (2015). A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy. In 24th International Conference on Information Processing in Medical Imaging, IPMI 2015 (pp. 288-299). Isle of Skye, Scotland: Berlin: Springer Verlag.

MLA:
Fischer, Peter, et al. "A Robust Probabilistic Model for Motion Layer Separation in X-Ray Fluoroscopy." Proceedings of the Information Processing in Medical Imaging, Isle of Skye, Scotland Berlin: Springer Verlag, 2015. 288-299.

BibTeX: 

Last updated on 2018-19-04 at 03:04