Unsupervised classification of skeletal fibers using diffusion maps

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 cluste...

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Bibliographic Details
Main Authors: Neji, R., Langs, G., Deux, J.-F., Maatouk, M., Rahmouni, A., Bassez, G., Fleury, G., Paragios, N.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Diffusion Maps
Diffusion tensor imaging
Diseases
DTI
Fiber Metrics
Fibers
Human Skeletal Muscle
Humans
Image segmentation
Kernel
Muscles
Radiology
Spatial coherence
Tensile stress
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Description
Summary: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.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2009.5193071