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

S. M. Ahmed, N. Yamany, A. A. Mohamed, T. Farag, and . Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Transactions on Medical Imaging, vol.21, issue.3, pp.993-999, 2002.
DOI : 10.1109/42.996338

C. [. Bricq, J. Collet, and . Armspach, Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains, Medical Image Analysis, vol.12, issue.6, pp.639-652, 2008.
DOI : 10.1016/j.media.2008.03.001

B. [. Buades and J. M. Coll, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
DOI : 10.1137/040616024

URL : https://hal.archives-ouvertes.fr/hal-00271141

]. J. Bez84 and . Bezdek, FCM: The Fuzzy C-Means clustering algorithm, Computers and Geosciences, vol.10, issue.5, pp.191-203, 1984.

[. Bazin and D. L. Pham, Topology-Preserving Tissue Classification of Magnetic Resonance Brain Images, IEEE Transactions on Medical Imaging, vol.26, issue.4, pp.487-496, 2007.
DOI : 10.1109/TMI.2007.893283

C. Ciofolo and C. Barillot, Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control, Medical Image Analysis, vol.13, issue.3, pp.456-470, 2009.
DOI : 10.1016/j.media.2009.02.008

URL : https://hal.archives-ouvertes.fr/inserm-00443873

S. [. Cai, D. Chen, and . Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition, vol.40, issue.3, pp.825-838, 2007.
DOI : 10.1016/j.patcog.2006.07.011

V. [. Cocosco, R. K. Kollokian, A. C. Kwan, and . Evans, BrainWeb: Online interface to a 3D MRI simulated brain database, HBM'97, Proceedings, p.425, 1997.

B. Caldairou, F. Rousseau, N. Passat, P. Habas, C. Studholme et al., A Non-Local Fuzzy Segmentation Method: Application to Brain MRI, Computer Analysis of Images and Patterns -CAIP'09, pp.606-613, 2009.
DOI : 10.1007/978-3-642-03767-2_74

URL : https://hal.archives-ouvertes.fr/hal-00476587

. Cyp-+-08-]-p, P. Coupé, S. Yger, P. Prima, C. Hellier 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.

. Dbc-+-03-]-p, I. Dokládal, M. Bloch, D. Couprie, R. Ruijters et al., Topologically controlled segmentation of 3D magnetic resonance images of the head by using morphological operators, Pattern Recognition, issue.10, pp.362463-2478, 2003.

]. A. Fds07, . Ferreira, and . Silva, A Dirichlet process mixture model for brain MRI tissue classication, Medical Image Analysis, vol.11, issue.2, pp.169-182, 2007.

R. [. Goldenberg, E. Kimmel, M. Rivlin, and . Rudzsky, Cortex segmentation: a fast variational geometric approach, IEEE Transactions on Medical Imaging, vol.21, issue.12, pp.1544-1551, 2002.
DOI : 10.1109/TMI.2002.806594

S. [. Gilboa and . Osher, Nonlocal Operators with Applications to Image Processing, Multiscale Modeling & Simulation, vol.7, issue.3, 2007.
DOI : 10.1137/070698592

. A. Hkg-+-09-]-p, K. Habas, O. A. Kim, A. J. Glenn, C. Barkovich et al., A spatiotemporal atlas of the human fetal brain with application to tissue segmentation, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2009, pp.289-296, 2009.

V. [. Jimenez-alaniz, O. Medina-banuelos, and . 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

[. Kwan, A. C. Evans, and G. B. Pike, MRI simulation-based evaluation of image-processing and classification methods, IEEE Transactions on Medical Imaging, vol.18, issue.11, pp.1085-1097, 1999.
DOI : 10.1109/42.816072

V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, From Local Kernel to Nonlocal Multiple-Model Image Denoising, International Journal of Computer Vision, vol.11, issue.1, pp.1-32, 2010.
DOI : 10.1007/s11263-009-0272-7

S. Kinderman, S. Osher, and P. W. Jones, Deblurring and Denoising of Images by Nonlocal Functionals, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1091-1115, 2005.
DOI : 10.1137/050622249

. Lgpc-+-99-]-g, E. Le-goualher, D. L. Procyk, R. Collins, C. Venugopal et al., Automated extraction and variability analysis of sulcal neuroanatomy, IEEE Transactions on Medical Imaging, vol.18, issue.3, pp.206-217, 1999.

S. [. Liang and . Wang, An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis, IEEE Transactions on Medical Imaging, vol.28, issue.2, pp.297-310, 2009.
DOI : 10.1109/TMI.2008.2004670

[. Liew and H. Yan, An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation, IEEE Transactions on Medical Imaging, vol.22, issue.9, pp.1063-1075, 2003.
DOI : 10.1109/TMI.2003.816956

]. J. Mac67 and . Macqueen, Some methods for classification and analysis of multivariate observations, Proc. Fifth Berkeley Sympos, pp.281-297, 1965.

[. Mangin, V. Frouin, I. Bloch, J. Régis, and J. López-krahe, From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations, Journal of Mathematical Imaging and Vision, vol.44, issue.6, pp.297-318, 1995.
DOI : 10.1007/BF01250286

H. [. Mayer and . Greenspan, An Adaptive Mean-Shift Framework for MRI Brain Segmentation, IEEE Transactions on Medical Imaging, vol.28, issue.8, pp.1238-1250, 2008.
DOI : 10.1109/TMI.2009.2013850

]. D. Pha01 and . Pham, Spatial models for fuzzy clustering, Computer Vision and Image Understanding, vol.84, issue.2, pp.285-297, 2001.

J. [. Pham and . Prince, Adaptive fuzzy segmentation of magnetic resonance images, IEEE Transactions on Medical Imaging, vol.18, issue.9, pp.737-752, 1999.
DOI : 10.1109/42.802752

J. [. Pham, X. Prince, A. P. Chenyang, and . Dagher, An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging, International Journal of Pattern Recognition and Artificial Intelligence, vol.11, issue.08, pp.1189-1211, 1997.
DOI : 10.1142/S021800149700055X

J. [. Pham, A. P. Prince, C. Dagher, and . Xu, An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging, International Journal of Pattern Recognition and Artificial Intelligence, vol.11, issue.08, pp.1189-1211, 1996.
DOI : 10.1142/S021800149700055X

D. L. Pham, C. Xu, and J. L. Prince, Current Methods in Medical Image Segmentation, Annual Review of Biomedical Engineering, vol.2, issue.1, pp.315-337, 2000.
DOI : 10.1146/annurev.bioeng.2.1.315

]. F. Rou08 and . Rousseau, Brain hallucination, European Conference on Computer Vision -ECCV'08, pp.497-508, 2008.

F. [. Scherrer, C. Forbes, M. Garbay, and . Dojat, Distributed Local MRF Models for Tissue and Structure Brain Segmentation, IEEE Transactions on Medical Imaging, vol.28, issue.8, pp.1278-1295, 2009.
DOI : 10.1109/TMI.2009.2014459

URL : https://hal.archives-ouvertes.fr/inserm-00402265

G. [. Sled and . Pike, Understanding intensity non-uniformity in MRI, Medical Image Computing and Computer-Assisted Intervention -MICCAI'98, pp.614-622, 1998.
DOI : 10.1097/00004728-199403000-00005

]. A. Tik63 and . Tikhonov, Regularization of incorrectly posed problems, pp.1624-1627, 1963.

F. [. Van-leemput, D. Maes, P. Vandermeulen, and . Suetens, Automated model-based tissue classification of MR images of the brain, IEEE Transactions on Medical Imaging, vol.18, issue.10, pp.897-908, 1999.
DOI : 10.1109/42.811270

]. J. Wkl-+-08, J. Wang, Y. Kong, M. Lu, B. Qi et al., A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints, Computerized Medical Imaging and Graphics, issue.8, pp.32685-698, 2008.

Y. [. Wang, Q. Soh, K. Song, and . Sim, Adaptive spatial information-theoretic clustering for image segmentation, Pattern Recognition, vol.42, issue.9, pp.2029-2044, 2009.
DOI : 10.1016/j.patcog.2009.01.023

F. [. Yang and . Kruggel, Automatic segmentation of human brain sulci, Medical Image Analysis, vol.12, issue.4, pp.442-451, 2008.
DOI : 10.1016/j.media.2008.01.003

]. L. Zad65 and . Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.

M. [. Zhang, S. Brady, and . Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.45-57, 2001.
DOI : 10.1109/42.906424

C. Zhu and T. Jiang, Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images, NeuroImage, vol.18, issue.3, pp.685-696, 2003.
DOI : 10.1016/S1053-8119(03)00006-5