Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
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Deformable surface models are an attractive method for segmenting the three-dimensional shapes of complex anatomic structures in volumetric medical images. Despite the success of this approach, several problems remain. In this paper, we propose a multi-scale deformable surface model with non-intersection constraint forces that successfully addresses three significant problems, sensitivity to model initialization, difficulties in dealing with severe object concavities, and model self-intersection. The first two problems are addressed by the multi-scale scheme, which progressively resamples the trianglulated, deformable surface model both globally and locally, matching its resolution to the levels of a volume image pyramid. We address the third problem by including a non-intersection constraint force among the customary internal and external forces in the physics-based formulation. We apply our new deformable surface model to the challenging task of extracting brain cortical surfaces.