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Unsupervised classification of skeletal fibers using diffusion maps

Abstract : In this paper, we propose an application of diffusion maps to fiber tract clustering in the human skeletal muscle. To this end, we define a metric between fiber tracts that encompasses both diffusion and localization information. This metric is incorporated in the diffusion maps framework and clustering is done in the embedding space using k-means. Experimental validation of the method is performed over a dataset of diffusion tensor images of the calf muscle of thirty subjects and comparison is done with respect to ground-truth segmentation provided by an expert.
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Submitted on : Friday, October 16, 2009 - 11:52:03 AM
Last modification on : Thursday, October 21, 2021 - 3:12:25 AM
Long-term archiving on: : Tuesday, June 15, 2010 - 8:49:24 PM


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  • HAL Id : hal-00424543, version 1



Radhouène Neji, Georg Langs, Jean-François Deux, Mezri Maatoouk, Alain Rahmouni, et al.. Unsupervised classification of skeletal fibers using diffusion maps. IEEE International Symposium on Biomedical Imaging : from Nano to Macro, Jun 2009, Boston, United States. pp.410-413. ⟨hal-00424543⟩



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