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

J. Grimaud, M. Lai, J. Thorpe, P. Adeleine, L. Wang et al., Quantification of MRI lesion load in multiple sclerosis: A comparison of three computer-assisted techniques, Magnetic Resonance Imaging, vol.14, issue.5, pp.495-505, 1996.
DOI : 10.1016/0730-725X(96)00018-5

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

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.

W. M. Wells, I. Grimson, W. Kikinis, R. Jolesz, and F. , Adaptive segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.15, issue.4, pp.429-442, 1996.
DOI : 10.1109/42.511747

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

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

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

J. F. Mangin, Entropy minimization for automatic correction of intensity nonuniformity, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737), pp.162-169, 2000.
DOI : 10.1109/MMBIA.2000.852374

P. Coupé, P. Yger, C. Barillot, and . Mic-cai-', Fast Non Local Means Denoising for 3D MR Images, 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.33-40, 2006.
DOI : 10.1007/11866763_5

N. Wiest-daesslé, S. Prima, S. Morrissey, and C. Barillot, VALIDATION OF A NEW OPTIMISATION ALGORITHM FOR REGISTRATION TASKS IN MEDICAL IMAGING, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.41-44, 2007.
DOI : 10.1109/ISBI.2007.356783

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

C. Fennema-notestine, I. B. Ozyurt, C. P. Clark, S. Morris, A. Bischoff-grethe et al., Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location, Human Brain Mapping, vol.23, issue.2, pp.99-113, 2006.
DOI : 10.1002/hbm.20161

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. Montillo, J. K. Udupa, L. Axel, and D. N. Metaxas, Interaction between noise suppression and inhomogeneity correction in MRI, Medical Imaging 2003: Image Processing, pp.1025-1036, 2003.
DOI : 10.1117/12.483555