Manifold-enhanced Segmentation through Random Walks on Linear Subspace Priors

Abstract : In this paper we propose a novel method for knowledge-based segmentation. Our contribution lies on the introduction of linear sub-spaces constraints within the random-walk segmentation framework. Prior knowledge is obtained through principal component analysis that is then combined with conventional boundary constraints for image segmentation. The approach is validated on a challenging clinical setting that is multicomponent segmentation of the human upper leg skeletal muscle in Magnetic Resonance Imaging, where there is limited visual differentiation support between muscle classes.
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Pierre-Yves Baudin, Noura Azzabou, Pierre Carlier, Nikos Paragios. Manifold-enhanced Segmentation through Random Walks on Linear Subspace Priors. British Machine Vision Conference, 2012, United Kingdom. pp.51.1-51.10. ⟨hal-00773635⟩

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