J. Ashburner and K. J. Friston, Unified segmentation, NeuroImage, vol.26, issue.3, pp.839-851, 2005.
DOI : 10.1016/j.neuroimage.2005.02.018

M. Beal and Z. Ghahramani, The variational Bayesian EM Algorithm for incomplete data: with application to scoring graphical model structures. Bayesian Statistics, 2003.

J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, pp.259-302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4
URL : https://hal.archives-ouvertes.fr/inria-00072526

D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. 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

P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann et al., An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images, IEEE Transactions on Medical Imaging, vol.27, issue.4, pp.425-441, 2008.
DOI : 10.1109/TMI.2007.906087
URL : https://hal.archives-ouvertes.fr/inserm-00169658

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, pp.1-38, 1977.

L. R. Dice, Measures of the Amount of Ecologic Association Between Species, Ecology, vol.26, issue.3, pp.297-302, 1945.
DOI : 10.2307/1932409

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.596-599, 2007.
DOI : 10.1109/ISBI.2007.356922

D. Garcia-lorenzo, L. Lecoeur, D. L. Arnold, D. L. Collins, and C. Barillot, Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts, MICCAI, pp.584-591, 2009.
DOI : 10.1007/978-3-642-04271-3_71
URL : https://hal.archives-ouvertes.fr/inria-00423040

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin, C. Genest et al., Bayesian Data Analysis Characterization of externally Bayesian pooling operators, Ann. Statist, vol.14, pp.487-501, 1986.

Z. Ghahramani and M. Beal, Propagation algorithms for variational Bayesian learning, Advances in Neural Information Processing Systems, 2001.

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

G. J. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

R. M. Neal and G. E. Hinton, A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in Graphical Models, pp.355-368, 1998.
DOI : 10.1007/978-94-011-5014-9_12

F. Rousseau, F. Blanc, J. De-seze, L. Rumbac, 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.9-12, 2008.
DOI : 10.1109/ISBI.2008.4540919

B. R. Sajja, S. Datta, R. He, M. Mehta, R. K. Gupta et al., Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI, Annals of Biomedical Engineering, vol.21, issue.1, pp.142-151, 2006.
DOI : 10.1007/s10439-005-9009-0

B. Scherrer, F. Forbes, C. Garbay, and M. Dojat, Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation, MICCAI, pp.1066-1074, 2008.
DOI : 10.1007/978-3-540-85990-1_128
URL : https://hal.archives-ouvertes.fr/inserm-00356883

M. L. Seghier, A. Ramlackhansingh, J. Crinion, A. P. Leff, and C. J. Price, Lesion identification using unified segmentation-normalisation models and fuzzy clustering, NeuroImage, vol.41, issue.4, pp.1253-1266, 2008.
DOI : 10.1016/j.neuroimage.2008.03.028

D. W. Shattuck, S. R. Sandor-leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, Magnetic Resonance Image Tissue Classification Using a Partial Volume Model, NeuroImage, vol.13, issue.5, pp.856-876, 2001.
DOI : 10.1006/nimg.2000.0730
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.1701

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

K. Van-leemput, F. Maes, D. Vandermeulen, and P. Suetens, Automated model-based bias field correction of MR images of the brain, IEEE Transactions on Medical Imaging, vol.18, issue.10, pp.885-896, 1999.
DOI : 10.1109/42.811268