S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*, Journal of Applied Statistics, vol.1, issue.5-6, pp.721-741, 1984.
DOI : 10.1109/TIT.1972.1054786

K. Held, E. R. Kopps, B. J. Krause, W. M. Wells, R. Kikinis et al., Markov random field segmentation of brain MR images, IEEE Transactions on Medical Imaging, vol.16, issue.6, pp.878-886, 1997.
DOI : 10.1109/42.650883

K. Van-leemput, F. Maes, D. Vandermeulen, and P. 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

Y. Zhang, M. Brady, and S. 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.45-47, 2001.
DOI : 10.1109/42.906424

J. L. Marroquin, B. C. Vemuri, S. Botello, F. Calderon, and A. Fernandez-bouzas, An accurate and efficient Bayesian method for automatic segmentation of brain MRI, IEEE Transactions on Medical Imaging, vol.21, issue.8, pp.934-945, 2002.
DOI : 10.1109/TMI.2002.803119

J. L. Marroquin, E. A. Santana, and S. Botello, Hidden Markov measure field models for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.11, pp.1380-1387, 2003.
DOI : 10.1109/TPAMI.2003.1240112

F. Yang and T. Jiang, Pixon-based image segmentation with markov random fields, IEEE Transactions on Image Processing, vol.12, issue.12, pp.1552-1559, 2003.
DOI : 10.1109/TIP.2003.817242

M. Rivera, O. Ocegueda, and J. L. Marroquin, Entropy-Controlled Quadratic Markov Measure Field Models for Efficient Image Segmentation, IEEE Transactions on Image Processing, vol.16, issue.12, pp.3047-3057, 2007.
DOI : 10.1109/TIP.2007.909384

W. D. Rooney, G. Johnson, X. Li, E. R. Cohen, S. G. Kim et al., Magnetic field and tissue dependencies of human brain longitudinal1H2O relaxation in vivo, Magnetic Resonance in Medicine, vol.181, issue.2, pp.308-318, 2007.
DOI : 10.1002/mrm.21122

J. P. Wansapura, S. K. Holland, R. S. Dunn, and W. S. Ball, NMR relaxation times in the human brain at 3.0 tesla, Journal of Magnetic Resonance Imaging, vol.38, issue.4, pp.531-538, 1999.
DOI : 10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L

S. Cho, D. Jones, W. E. Reddick, R. J. Ogg, and R. G. Steen, Establishing norms for age-related changes in proton T1 of human brain tissue in vivo, Magnetic Resonance Imaging, vol.15, issue.10, pp.1133-1143, 1997.
DOI : 10.1016/S0730-725X(97)00202-6

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

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

R. Guillemaud and M. Brady, Estimating the bias field of MR images, IEEE Transactions on Medical Imaging, vol.16, issue.3, pp.238-251, 1997.
DOI : 10.1109/42.585758

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

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

J. C. Rajapakse, J. N. Giedd, and J. L. Rapoport, Statistical approach to segmentation of single-channel cerebral MR images, IEEE Transactions on Medical Imaging, vol.16, issue.2, pp.176-186, 1997.
DOI : 10.1109/42.563663

C. Zhu and T. Jiang, Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images, NeuroImage, vol.18, issue.3, pp.685-96, 2003.
DOI : 10.1016/S1053-8119(03)00006-5

K. M. Pohl, J. Fisher, E. Grimson, R. Kikinis, and W. M. Wells, A Bayesian model for joint segmentation and registration, NeuroImage, vol.31, issue.1, pp.228-239, 2006.
DOI : 10.1016/j.neuroimage.2005.11.044

B. Fischl, Whole Brain Segmentation, Neuron, vol.33, issue.3, pp.341-55, 2002.
DOI : 10.1016/S0896-6273(02)00569-X

URL : http://doi.org/10.1016/s0896-6273(02)00569-x

K. M. Pohl, J. Fisher, M. Shenton, R. W. Mccarley, W. E. Grimson et al., Logarithm Odds Maps for Shape Representation, MICCAI, pp.955-963, 2006.
DOI : 10.1007/11866763_117

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994060

O. Colliot, C. O. , and I. Bloch, Integration of fuzzy spatial relations in deformable models???Application to brain MRI segmentation, Pattern Recognition, vol.39, issue.8, pp.1401-1414, 2006.
DOI : 10.1016/j.patcog.2006.02.022

URL : https://hal.archives-ouvertes.fr/hal-00878443

V. Barra and J. Y. Boire, Automatic segmentation of subcortical brain structures in MR images using information fusion, IEEE Transactions on Medical Imaging, vol.20, issue.7, pp.549-558, 1977.
DOI : 10.1109/42.932740

D. A. Wicks, J. G. Barker, and P. S. Tofts, Correction of intensity nonuniformity in MR images of any orientation, Magnetic Resonance Imaging, vol.11, issue.2, pp.183-196, 1993.
DOI : 10.1016/0730-725X(93)90023-7

O. Noterdaeme and M. Brady, A fast method for computing and correcting intensity inhomogeneities in MRI, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1525-1528, 2008.
DOI : 10.1109/ISBI.2008.4541299

J. G. Sled, A. P. Zujdenbos, and A. Evans, A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Transactions on Medical Imaging, vol.17, issue.1, pp.87-97, 1998.
DOI : 10.1109/42.668698

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

M. Styner, C. Brechbuhler, G. Szckely, and G. Gerig, Parametric estimate of intensity inhomogeneities applied to MRI, IEEE Transactions on Medical Imaging, vol.19, issue.3, pp.153-165, 2000.
DOI : 10.1109/42.845174

T. J. Grabowski, R. J. Frank, N. R. Szumski, C. K. Brown, and H. Damasio, Validation of Partial Tissue Segmentation of Single-Channel Magnetic Resonance Images of the Brain, NeuroImage, vol.12, issue.6, pp.640-656, 2000.
DOI : 10.1006/nimg.2000.0649

N. Richard, M. Dojat, and C. Garbay, Distributed Markovian segmentation: Application to MR brain scans, Pattern Recognition, vol.40, issue.12, pp.3467-3480, 2007.
DOI : 10.1016/j.patcog.2007.03.019

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. Smith, J. C. Ho et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.1116-1128, 2006.
DOI : 10.1016/j.neuroimage.2006.01.015

S. C. Deonia, B. K. Rutt, A. G. Parrent, and T. M. Peters, Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T, NeuroImage, vol.34, issue.1, pp.117-126, 2006.
DOI : 10.1016/j.neuroimage.2006.09.016

S. Gouttard, M. Styner, S. Joshi, R. G. Smith, H. C. Hazlett et al., Subcortical structure segmentation using probabilistic atlas priors, Medical Imaging 2007: Image Processing, pp.37-46, 2007.
DOI : 10.1117/12.708626

I. Bloch, Fuzzy relative position between objects in image processing: a morphological approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.7, pp.657-664, 1999.
DOI : 10.1109/34.777378

. 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

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

B. Scherrer, M. Dojat, F. Forbes, and C. Garbay, Agentification of Markov model based segmentation: Application to MRI brain scans, Artificial Intelligence in Medicine, 2008.

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

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

S. K. Warfield, K. H. Zou, and W. M. Wells, Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation, IEEE Transactions on Medical Imaging, vol.23, issue.7, pp.903-921, 2004.
DOI : 10.1109/TMI.2004.828354

P. Santago and H. D. Gage, Quantification of MR brain images by mixture density and partial volume modeling, IEEE Transactions on Medical Imaging, vol.12, issue.3, pp.566-574, 1993.
DOI : 10.1109/42.241885

K. Van-leemput, F. Maes, D. Vandermeulen, and P. Suetens, A unifying framework for partial volume segmentation of brain MR images, IEEE Transactions on Medical Imaging, vol.22, issue.1, pp.897-908, 2003.
DOI : 10.1109/TMI.2002.806587

J. Atif, H. Khotanlou, E. Angelini, H. Duffau, and I. Bloch, Segmentation of internal brain structures in the presence of a tumor, MICCAI -Oncology Workshop, pp.61-68, 2006.

F. Forbes and N. Peyrard, Hidden markov random field model selection criteria based on mean field-like approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.9, pp.1089-1101, 2003.
DOI : 10.1109/TPAMI.2003.1227985