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In this paper we propose a constrained version of Mumford-Shah's segmentation with an information-theoretic point of view in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T1-Map and T1-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes...
We present in this paper a solution to the graph isomorphism problem, by a unique decomposition of a graph into a set of atoms via its clique minimal separators. The resulting set of atoms is then used to collapse the original graph into a bipartite attributed relational graph, having a fewer number of vertices. Finally a probabilistic matching algorithm operates on the reduced graphs, simulating...
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