. Lladó, Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches, Information Sciences, vol.186, issue.1, pp.164-185, 2012.
DOI : 10.1016/j.ins.2011.10.011

K. Van-leemput, 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

D. García-lorenzo, Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis, IEEE Transactions on Medical Imaging, vol.30, issue.8, pp.1455-1467, 2011.
DOI : 10.1109/TMI.2011.2114671

E. Geremia, Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images, NeuroImage, vol.57, issue.2, pp.378-390, 2011.
DOI : 10.1016/j.neuroimage.2011.03.080
URL : https://hal.archives-ouvertes.fr/inria-00616194

Z. Karimaghaloo, Automatic detection of gadolinium-enhancing ms lesions in brain MRI using crf, IEEE TMI, vol.31, issue.6, pp.1181-1194, 2012.

S. Kadoury, Manifold-constrained embeddings for the detection of white matter lesions in brain MRI, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.562-565, 2012.
DOI : 10.1109/ISBI.2012.6235610
URL : https://hal.archives-ouvertes.fr/hal-00856297

K. Batmanghelich, Multiparametric tissue abnormality characterization using manifold regularization, Medical Imaging 2008: Computer-Aided Diagnosis, 2008.
DOI : 10.1117/12.770837

M. Sugiyama, Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting, IEICE Transactions on Information and Systems, vol.93, issue.10, pp.2690-2701, 2010.
DOI : 10.1587/transinf.E93.D.2690

J. Quinn, A least-squares approach to anomaly detection in static and sequential data, Pattern Recognition Letters, vol.40, pp.36-40, 2014.
DOI : 10.1016/j.patrec.2013.12.016

K. Reddy, Confidence guided enhancing brain tumor segmentation in multi-parametric MRI, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.366-369, 2012.
DOI : 10.1109/ISBI.2012.6235560

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-66, 1979.
DOI : 10.1109/TSMC.1979.4310076

. Ng, Automatic thresholding for defect detection, Pattern Recognition Letters, vol.27, issue.14, pp.1644-1649, 2006.
DOI : 10.1016/j.patrec.2006.03.009

P. Coupé, 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

J. Nicholas, N4ITK: improved N3 bias correction, IEEE TMI, vol.29, issue.6, pp.1310-1330, 2010.

A. Guimond, Average Brain Models: A Convergence Study, Computer Vision and Image Understanding, vol.77, issue.2, pp.192-210, 1999.
DOI : 10.1006/cviu.1999.0815
URL : https://hal.archives-ouvertes.fr/inria-00615030

O. Commowick, Automated Diffeomorphic Registration of Anatomical Structures with Rigid Parts: Application to Dynamic Cervical MRI, MICCAI'12, 2012.
DOI : 10.1007/978-3-642-33418-4_21
URL : https://hal.archives-ouvertes.fr/inserm-00716106

Y. Karpate, O. Commowick, C. Barillot, and G. Edan, Longitudinal intensity normalization in multiple sclerosis patients, " in Clinical Image-Based Procedures. Translational Research in Medical Imaging -Third International Workshop, CLIP 2014, Held in Conjunction with MIC- CAI 2014, pp.118-125, 2014.

B. Schölkopf, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

P. Rousseeuw, A Fast Algorithm for the Minimum Covariance Determinant Estimator, Technometrics, vol.35, issue.3, pp.212-223, 1998.
DOI : 10.1080/01621459.1994.10476821