Multimodal Prior Appearance Models based on Regional Clustering of Intensity Profiles

François Chung 1 Hervé Delingette 1, *
* Auteur correspondant
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Model-based image segmentation requires prior information about the appearance of a structure in the image. Instead of relying on Principal Component Analysis such as in Statistical Appearance Models, we propose a method based on a regional clustering of intensity profiles that does not rely on an accurate pointwise registration. Our method is built upon the Expectation-Maximization algorithm with regularized covariance matrices and includes spatial regularization. The number of appearance regions is determined by a novel model order selection criterion. The prior is described on a reference mesh where each vertex has a probability to belong to several intensity profile classes.
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Communication dans un congrès
Yang, Guang-Zhong and Hawkes, David and Rueckert, Daniel and Noble, Alison and Taylor, Chris. Medical Image Computing and Computer-Assisted Intervention (MICCAI'09), 2009, London, United Kingdom. Springer, 5762, pp.1051--1058, 2009, Lecture Notes in Computer Science. 〈10.1007/978-3-642-04271-3_127〉
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François Chung, Hervé Delingette. Multimodal Prior Appearance Models based on Regional Clustering of Intensity Profiles. Yang, Guang-Zhong and Hawkes, David and Rueckert, Daniel and Noble, Alison and Taylor, Chris. Medical Image Computing and Computer-Assisted Intervention (MICCAI'09), 2009, London, United Kingdom. Springer, 5762, pp.1051--1058, 2009, Lecture Notes in Computer Science. 〈10.1007/978-3-642-04271-3_127〉. 〈inria-00616132〉

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