Improving Deformable Surface Meshes through Omni-Directional Displacements and MRFs
Deformable surface models are often represented as triangular 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 intersect 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 regularized deformations via Markov Random Field optimization. We demonstrate 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.
KeywordsMarkov Random Field Sphere Diameter Coronoid Process Initial Mesh Statistical Shape Model
- 5.Kainmueller, D., Lange, T., Lamecker, H.: Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. In: 3D Segmentation in the Clinic: A Grand Challenge, pp. 109–116 (2007)Google Scholar
- 7.Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. MIA 12(6), 731–741 (2008); Special issue on information processing in medical imaging 2007 Google Scholar
- 8.Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal Surface Segmentation in Volumetric Images - A Graph-Theoretic Approach. IEEE TPAMI 28(1), 119–134 (2006)Google Scholar
- 9.Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.C.: Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model. In: Proc. VCBM, pp. 93–100 (2008)Google Scholar
- 10.Kainmueller, D., Lamecker, H., Seim, H., Zinser, M., Zachow, S.: Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 76–83. Springer, Heidelberg (2009)CrossRefGoogle Scholar