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• W2037116875 abstract "Model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis such as in Statistical Appearance Models, our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted EM classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal Principal Component Analysis approach while relying on fewer profile modes." @default.
• W2037116875 created "2016-06-24" @default.
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• W2037116875 date "2013-06-01" @default.
• W2037116875 modified "2023-09-24" @default.
• W2037116875 title "Regional appearance modeling based on the clustering of intensity profiles" @default.
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• W2037116875 doi "https://doi.org/10.1016/j.cviu.2013.01.011" @default.
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