D. H. Miller, R. I. Grossman, S. C. Reingold, and H. F. Mcfarland, The role of magnetic resonance techniques in understanding and managing multiple sclerosis, Brain, vol.121, issue.1, pp.3-24, 1998.
DOI : 10.1093/brain/121.1.3

L. S. A¨?ta¨?t-ali, S. Prima, P. Hellier, B. Carsin, G. Edan et al., STREM: a robust multidimensional parametric method to segment MS lesions in MRI, Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, vol.8, pp.409-416, 2005.

M. S. Atkins, M. S. Drew, and Z. Tauber, Towards automatic segmentation of MS lesions in PD/T2 MR images, Proc. SPIE of Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, pp.800-809, 2000.

K. Van-leemput, F. Maes, D. Vandermeulen, A. Colchester, and P. Suetens, Automated segmentation of multiple sclerosis lesions by model outlier detection, IEEE Transactions on Medical Imaging, vol.20, issue.8, pp.677-688, 2001.
DOI : 10.1109/42.938237

G. Dugas-phocion, M. Gonzalez, C. Lebrun, S. Chanalet, C. Bensa et al., Hierarchical segmentation of multiple sclerosis lesions in multisequence MRI, Biomedical Imaging: Macro to Nano, pp.157-160, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00615969

K. Haris, S. Efstratiadis, N. Maglaveras, and A. Katsaggelos, Hybrid image segmentation using watersheds and fast region merging, IEEE Transactions on Image Processing, vol.7, issue.12, pp.1684-1699, 1998.
DOI : 10.1109/83.730380

K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory, IEEE Transactions on, vol.21, issue.1, pp.32-40, 1975.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.24, issue.5, pp.603-619, 2002.

J. Jimenez-alaniz, V. Medina-banuelos, and O. Yanez-suarez, Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information, IEEE Transactions on Medical Imaging, vol.25, issue.1, pp.74-83, 2006.
DOI : 10.1109/TMI.2005.860999

A. Mayer and H. Greenspan, Segmentation of brain mri by adaptive mean shift, Biomedical Imaging: Macro to Nano 3rd IEEE International Symposium on. (6-9, pp.319-322, 2006.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

N. Neykov, P. Filzmoser, R. Dimova, and P. Neytchev, Robust fitting of mixtures using the trimmed likelihood estimator, Computational Statistics & Data Analysis, vol.52, issue.1, pp.299-308, 2007.
DOI : 10.1016/j.csda.2006.12.024

F. Barkhof, M. Filippi, D. H. Miller, P. Scheltens, A. Campi et al., Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis, Brain, vol.120, issue.11, pp.2059-2069, 1997.
DOI : 10.1093/brain/120.11.2059

D. Collins, A. Zijdenbos, V. Kollokian, J. Sled, N. Kabani et al., Design and construction of a realistic digital brain phantom, IEEE Transactions on Medical Imaging, vol.17, issue.3, pp.463-468, 1998.
DOI : 10.1109/42.712135

A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer, Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Transactions on Medical Imaging, vol.13, issue.4, pp.716-724, 1994.
DOI : 10.1109/42.363096

A. P. Zijdenbos, R. Forghani, and A. C. Evans, Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis, IEEE Transactions on Medical Imaging, vol.21, issue.10, pp.1280-1291, 2002.
DOI : 10.1109/TMI.2002.806283

S. M. Smith, Fast robust automated brain extraction, Human Brain Mapping, vol.20, issue.3, pp.143-155, 2002.
DOI : 10.1002/hbm.10062

F. Rousseau, F. Blanc, J. De-seze, L. Rumbach, and J. P. Armspach, An a contrario approach for outliers segmentation: Application to Multiple Sclerosis in MRI, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.14-17, 2008.
DOI : 10.1109/ISBI.2008.4540919

O. Freifeld, H. Greenspan, and J. Goldberger, LESION DETECTION IN NOISY MR BRAIN IMAGES USING CONSTRAINED GMM AND ACTIVE CONTOURS, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.12-15, 2007.
DOI : 10.1109/ISBI.2007.356922