Abstract
Matching 3D shapes is important in many medical imaging applications. We show that a joint clustering and diffeomorphism estimation strategy is capable of simultaneously estimating correspondences and a diffeomorphism between unlabeled 3D point-sets. Correspondence is established between the cluster centers and this is coupled with a simultaneous estimation of a 3D diffeomorphism of space. The number of clusters can be estimated by minimizing the Jensen-Shannon divergence on the registered data. We apply our algorithm to both synthetically warped 3D hippocampal shapes as well as real 3D hippocampal shapes from different subjects.
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Keywords
- Cluster Center
- Gaussian Radial Basis Function
- Deterministic Annealing
- Medical Imaging Application
- Point Feature Match
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Guo, H., Rangarajan, A., Joshi, S.C. (2005). 3-D Diffeomorphic Shape Registration on Hippocampal Data Sets. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_121
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DOI: https://doi.org/10.1007/11566489_121
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