Automatic skeletal muscle segmentation through random walks and graph-based seed placement

Abstract : In this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome of this optimization process is fed to a powerful graphbased diffusion segmentation method (random walker) that is able to produce very promising results through a fully automated approach. Validation on challenging data sets demonstrates the potentials of our method.
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Pierre-Yves Baudin, Noura Azzabou, Pierre Carlier, Nikos Paragios. Automatic skeletal muscle segmentation through random walks and graph-based seed placement. International Symposium Biomedical Imaging (ISBI), May 2012, Barcelone, Spain. pp.1036--1039. ⟨hal-00773616⟩

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