Abstract
For the analysis of shape variations of the heart and the cardiac motion in a clinical environment it is necessary to segment a large amount of data in order to be able to build statistically significant models. Therefore it has been the aim of this project to find and develop methods that allow the creation of a fully automatic segmentation pipeline for the segmentation of endocardium and myocardium in ECG-triggered MRI images. For this purpose a combination of a number of image processing techniques, from the fields of segmentation, modeling and image registration have been used and extended to create a segmentation pipeline that reduces the need for supplementary manual correction of the segmented labels to a minimum.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. International Journal on Computer Vision 1, 321–331 (1987)
Sethian, J.A.: Curvature and evolution of fronts. Commun. Math. Phys. 101 (1985)
Osher, J., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)
Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Material Science, 2nd edn. Cambridge University Press, Cambridge (1999)
Leventon, M.: Statistical Models for Medical Image Analysis. In: Artificial Intelligence Lab. MIT, Cambridge (2000)
Viola, P., Wells, W.M.: Alignment of maximization of mutual information. International Journal on Computer Vision 22, 61–97 (1997)
McInerney, T., Terzopoulos, D.: T-snakes: Topology adaptive snakes. Medical Image Analysis 4, 73–91 (2000)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE TPAMI 17, 158–175 (1995)
Caselles, V., Kimmel, R., Sapiro, G.: A geometric model for active contours. Numerische Mathematik 66 (1993)
Goldenberg, R., Kimmel, R., Rivlin, R., Rudzsky, E.: Fast Geodesic Active Contours. IEEE Transactions Imag. Proc. 10, 1476–1475 (2001)
Zhu, S.C., Yuille, A.: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Transactions on Pattern Analysis and machine Intelligence 18 (1996)
Leventon, M., Grimson, E., Faugeras, O.: Statistical Shape Influence in Geodesic Active Contours. Computer Vision and Pattern Recognition 1, 316–323 (2000)
Fritscher, K.D., Schubert, R.: A software framework for pre-processing and level set segmentation of medical image data. Presented at SPIE Medical Imaging, San Diego (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fritscher, K.D., Pilgram, R., Schubert, R. (2005). Automatic Cardiac 4D Segmentation Using Level Sets. In: Frangi, A.F., Radeva, P.I., Santos, A., Hernandez, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2005. Lecture Notes in Computer Science, vol 3504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494621_12
Download citation
DOI: https://doi.org/10.1007/11494621_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26161-2
Online ISBN: 978-3-540-32081-4
eBook Packages: Computer ScienceComputer Science (R0)