Topology-Preserving Discrete Deformable Model: Application to Multi-segmentation of Brain MRI

Authors:
Sanae Miri;Nicolas Passat;Jean-Paul Armspach
Affiliations:
LSIIT, UMR 7005 CNRS/ULP, Strasbourg 1 University, France and LINC, UMR 7191 CNRS/ULP, Strasbourg 1 University, France;LSIIT, UMR 7005 CNRS/ULP, Strasbourg 1 University, France;LINC, UMR 7191 CNRS/ULP, Strasbourg 1 University, France
Venue:
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Year:
2008

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Abstract

Among the numerous 3D medical image segmentation methods proposed in the literature, very few have intended to provide topologically satisfying results, a fortiorifor multiple object segmentation. In this paper, we present a method devoted to parallel segmentation of the main classes of cerebral tissues from 3D magnetic resonance imaging data. This method is based on a multi-class discrete deformable model strategy, starting from a topologically correct model, and guiding its evolution in a topology-preserving fashion. Validations on a commonly used cerebral image database provide promising results and justify the further development of a general methodological framework based on the concepts exposed in this preliminary work.