D. Le-bihan, J. Mangin, C. Poupon, C. A. Clark, S. Pappata et al., Diffusion tensor imaging: Concepts and applications, Journal of Magnetic Resonance Imaging, vol.44, issue.4, pp.534-546, 2001.
DOI : 10.1002/jmri.1076

URL : https://hal.archives-ouvertes.fr/hal-00349820

C. J. Galban, S. Maderwald, K. Uffmann, M. E. Armin-de-greiff, and . Ladd, Diffusive sensitivity to muscle architecture: a magnetic resonance diffusion tensor imaging study of the human calf, European Journal of Applied Physiology, vol.227, issue.3, pp.253-262, 2004.
DOI : 10.1007/s00421-004-1186-2

D. C. Martin, M. K. Medri, R. S. Chow, V. Oxorn, R. N. Leekam et al., Comparing human skeletal muscle architectural parameters of cadavers with in vivo ultrasonographic measurements, Journal of Anatomy, vol.199, issue.4, pp.429-463, 2001.
DOI : 10.1046/j.1469-7580.2001.19940429.x

A. Tsai, C. Westin, A. O. Hero, and A. S. Willsky, Fiber tract clustering on manifolds with dual rootedgraphs, CVPR, 2007.

. Westin, Clustering fiber tracts using normalized cuts, MICCAI, 2004.

L. O. Donnell and C. Westin, White matter tract clustering and correspondence in populations, MICCAI, 2005.

T. Yonas, G. Weldeselassie, and . Hamarneh, DT-MRI segmentation using graph cuts, p.65121, 2007.

Z. Wang and B. C. Vemuri, DTI segmentation using an information theoretic tensor dissimilarity measure, IEEE Transactions on Medical Imaging, vol.24, issue.10, pp.1267-1277, 2005.
DOI : 10.1109/TMI.2005.854516

L. Jonasson, P. Hagmann, C. Pollo, X. Bresson, C. Wilson et al., A level set method for segmentation of the thalamus and its nuclei in DT-MRI, Signal Processing, vol.87, issue.2, pp.309-321, 2007.
DOI : 10.1016/j.sigpro.2005.12.017

V. Vapnik, Statistical Learning Theory, 1998.

T. Jaakkola and D. Haussler, Exploiting generative models in discriminative classifiers, Tech. Rep, 1998.

X. Pennec, P. Fillard, and N. Ayache, A Riemannian Framework for Tensor Computing, International Journal of Computer Vision, vol.6, issue.2, pp.41-66, 2006.
DOI : 10.1007/s11263-005-3222-z

URL : https://hal.archives-ouvertes.fr/inria-00070743

J. Lafferty and G. Lebanon, Diffusion kernels on statistical manifolds, Journal of Machine Learning Research, vol.6, pp.129-163, 2005.

K. Tsuda, G. Rtsch, and M. Warmuth, Matrix exponentiated gradient updates for on-line learning and Bregman projection, Journal of Machine Learning Research, vol.6, pp.995-1018, 2005.

T. Jebara, R. Kondor, and A. Howard, Probability product kernels, Journal of Machine Learning Research, vol.5, pp.819-844, 2004.

S. Boughorbel, J. Tarel, and F. Fleuret, Non-Mercer Kernels for SVM Object Recognition, Procedings of the British Machine Vision Conference 2004, 2004.
DOI : 10.5244/C.18.16

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

N. Komodakis, G. Tziritas, and N. Paragios, Fast, Approximately Optimal Solutions for Single and Dynamic MRFs, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383095