Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics

Résumé

In this paper, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algo-rithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.
Fichier principal
Vignette du fichier
Neji_ipmi09.pdf (566.47 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-00424532 , version 1 (16-10-2009)

Identifiants

  • HAL Id : hal-00424532 , version 1

Citer

Radhouène Neji, Ahmed Besbes, Nikos Komodakis, Mezri Maatoouk, Jean-François Deux, et al.. Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics. 21st International Conference on Information Processing in Medical Imaging, Jul 2009, Williamsburg (Virginia), United States. pp.14-25. ⟨hal-00424532⟩
303 Consultations
552 Téléchargements

Partager

Gmail Facebook X LinkedIn More