Topology-preserving discrete deformable model: Application to multi-segmentation of brain MRI

Abstract : Among the numerous 3D medical image segmentation methods proposed in the literature, very few have intended to provide topologically satisfying results, a fortiori for 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.
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Sanae Miri, Nicolas Passat, Jean-Paul Armspach. Topology-preserving discrete deformable model: Application to multi-segmentation of brain MRI. International Conference on Image and Signal Processing (ICISP), 2008, Cherbourg-Octeville, France. pp.67-75, ⟨10.1007/978-3-540-69905-7_8⟩. ⟨hal-01695010⟩

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