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Improving Deformable Surface Meshes through Omni-directional Displacements and MRFs

Summary: Improving Deformable Surface Meshes through
Omni-directional Displacements and MRFs
D. Kainmueller, H. Lamecker, H. Seim, S. Zachow, and H.-C. Hege
Zuse Institute Berlin, Germany
Abstract. Deformable surface models are often represented as trian-
gular meshes in image segmentation applications. For a fast and easily
regularized deformation onto the target object boundary, the vertices
of the mesh are commonly moved along line segments (typically surface
normals). However, in case of high mesh curvature, these lines may inter-
sect with the target boundary at "non-corresponding" positions, or even
not at all. Consequently, certain deformations cannot be achieved. We
propose an approach that allows each vertex to move not only along a
line segment, but within a surrounding sphere. We achieve globally regu-
larized deformations via Markov Random Field optimization. We demon-
strate the potential of our approach with experiments on synthetic data,
as well as an evaluation on 2x106 coronoid processes of the mandible in
Cone-Beam CTs, and 56 coccyxes (tailbones) in low-resolution CTs.
1 Introduction
Deformable surface models are widely used for image segmentation [1]. Among


Source: Andrzejak, Artur - Konrad-Zuse-Zentrum für Informationstechnik Berlin


Collections: Computer Technologies and Information Sciences