Unsupervised classification of skeletal fibers using diffusion maps

Radhouène Neji; Georg Langs; Jean-François Deux; Mezri Maatoouk; Alain Rahmouni; Guillaume Bassez; Gilles Fleury; Nikolaos Paragios

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Medical Imaging

Computer Science [cs] / Image Processing [eess.IV]

Engineering Sciences [physics] / Signal and Image processing

Life Sciences [q-bio] / Bioengineering

Computer Science [cs] / Signal and Image Processing

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