Unsupervised classification of skeletal fibers using diffusion maps

  • Authors:

  • R. Neji;G. Langs;J-F. Deux;M. Maatouk;A. Rahmouni;G. Bassez;G. Fleury;N. Paragios

  • Affiliations:

  • Laboratoire MAS, Ecole Centrale Paris, Châtenay-Malabry and Equipe GALEN, INRIA Saclay, Orsay and Département SSE, Ecole Supérieure d'Electricité, Gif-sur-Yvette, France;CIR lab, Department of Radiology, Medical University of Vienna, Vienna, Austria;Centre Hospitalier Universitaire Henri Mondor, Créteil, France;Centre Hospitalier Universitaire Henri Mondor, Créteil, France;Centre Hospitalier Universitaire Henri Mondor, Créteil, France;Centre Hospitalier Universitaire Henri Mondor, Créteil, France;Département SSE, Ecole Supérieure d'Electricité, Gif-sur-Yvette, France;Laboratoire MAS, Ecole Centrale Paris, Châtenay-Malabry and Equipe GALEN, INRIA Saclay, Orsay, France

  • Venue:
  • ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
  • Year:
  • 2009

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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.