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Proceedings Paper

Coupled level set segmentation using a point-based statistical shape model relying on correspondence probabilities
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Paper Abstract

In this article, we propose a unified statistical framework for image segmentation with shape prior information. The approach combines an explicitely parameterized point-based probabilistic statistical shape model (SSM) with a segmentation contour which is implicitly represented by the zero level set of a higher dimensional surface. These two aspects are unified in a Maximum a Posteriori (MAP) estimation where the level set is evolved to converge towards the boundary of the organ to be segmented based on the image information while taking into account the prior given by the SSM information. The optimization of the energy functional obtained by the MAP formulation leads to an alternate update of the level set and an update of the fitting of the SSM. We then adapt the probabilistic SSM for multi-shape modeling and extend the approach to multiple-structure segmentation by introducing a level set function for each structure. During segmentation, the evolution of the different level set functions is coupled by the multi-shape SSM. First experimental evaluations indicate that our method is well suited for the segmentation of topologically complex, non spheric and multiple-structure shapes. We demonstrate the effectiveness of the method by experiments on kidney segmentation as well as on hip joint segmentation in CT images.

Paper Details

Date Published: 12 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762318 (12 March 2010); doi: 10.1117/12.844289
Show Author Affiliations
Heike Hufnagel, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Jan Ehrhardt, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Xavier Pennec, INRIA Sophia Antipolis (France)
Alexander Schmidt-Richberg, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Heinz Handels, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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