Unsupervised classification of skeletal fibers using diffusion maps

Radhouène Neji 1, 2, 3, * Georg Langs 4 Jean-François Deux 5 Mezri Maatoouk 5 Alain Rahmouni 5 Guillaume Bassez 5 Gilles Fleury 1 Nikolaos Paragios 2, 3
* Corresponding author
1 E3S - Supélec Sciences des Systèmes [Gif-sur-Yvette]
2 MAS - Mathématiques Appliquées aux Systèmes - EA 4037
3 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
Inria Saclay - Ile de France, Ecole Centrale Paris
4 CIR lab - Computational Image Analysis and Radiology
5 Hôpital Henri Mondor
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.
Keywords : DTI Fibers Diffusion Maps Fiber Metrics Human Skeletal Muscle
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Conference papers
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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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