Abstract
Most of the tasks derived from shape analysis rely on the problem of finding meaningful correspondences between two or more shapes. In medical imaging analysis, this problem is a challenging topic due to the need to establish matching features in a given registration process. Besides, a similarity measure between shapes must be computed in order to obtain these correspondences. In this paper, we propose a method for 3D shape correspondences based on groupwise analysis using probabilistic latent variable models. The proposed method finds groupwise correspondences, and can handle multiple shapes with different number of objects (vertex or descriptors for every shape). By assigning a latent vector for each shape descriptor, we can cluster objects in different shapes, and find correspondences between clusters. We use a Dirichlet process prior in order to infer the number of clusters and find groupwise correspondences in an unsupervised manner. The results show that the proposed method can efficiently establish meaningful correspondences without using similarity measures between shapes.
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Notes
- 1.
This database is available on http://www.spl.harvard.edu/publications/item/view/1265.
- 2.
We use the fast-marching toolbox developed by Gabriel Peyre and available on https://github.com/gpeyre/matlab-toolboxes/tree/master/toolbox_fast_marching.
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Acknowledgments
This research is developed under the project: Estimación de los parámetros de neuromodulación con terapia de estimulación cerebral profunda, en pacientes con enfermedad de Parkinson a partir del volumen de tejido activo planeado, financed by Colciencias with code \(1110-657-40687\). H.F. García is funded by Colciencias under the program: formación de alto nivel para la ciencia, la tecnología y la innovación - Convocatoria 617 de 2013.
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García, H.F., Álvarez, M.A., Orozco, Á. (2015). Groupwise Shape Correspondences on 3D Brain Structures Using Probabilistic Latent Variable Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_44
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DOI: https://doi.org/10.1007/978-3-319-27857-5_44
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