In this paper, we propose a novel approach for segmenting the skeletal muscles in MRI automatically. In order to deal with the absence of contrast between the different muscle classes, we proposed a principled mathematical formulation that integrates prior knowledge with a random walks graph-based formulation. Prior knowledge is represented using a statistical shape atlas that once coupled with the random walks segmentation leads to an efficient iterative linear optimization system. We reveal the potential of our approach on a challenging set of real clinical data.
CITATION STYLE
Baudin, P. Y., Azzabou, N., Carlier, P. G., & Paragios, N. (2012). Prior knowledge, random walks and human skeletal muscle segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7510 LNCS, pp. 569–576). Springer Verlag. https://doi.org/10.1007/978-3-642-33415-3_70
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