R. Grossman and J. Mcgowan, Perspectives on multiple sclerosis, American journal of neuroradiology, vol.19, issue.7, pp.1251-1265, 1998.

. Polman, Role of magnetic resonance imaging within diagnostic criteria for multiple sclerosis, Annals of Neurology, vol.56, issue.2, pp.273-278, 2004.

D. García-lorenzo, S. Francis, S. Narayanan, D. L. Arnold, and D. L. Collins, Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging, Medical Image Analysis, vol.17, issue.1, pp.1-18, 2013.
DOI : 10.1016/j.media.2012.09.004

X. Lladó, A. Oliver, M. Cabezas, J. Freixenet, J. C. Vilanova et al., 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

M. Elad and M. Aharon, Image Denoising Via Learned Dictionaries and Sparse representation, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.895-900, 2006.
DOI : 10.1109/CVPR.2006.142

J. Mairal, M. Elad, and G. Sapiro, Sparse Representation for Color Image Restoration, IEEE Transactions on Image Processing, vol.17, issue.1, pp.53-69, 2008.
DOI : 10.1109/TIP.2007.911828

M. Elad, M. Figueiredo, and Y. Ma, On the Role of Sparse and Redundant Representations in Image Processing, Proceedings of the IEEE, vol.98, issue.6, pp.972-982, 2010.
DOI : 10.1109/JPROC.2009.2037655
URL : https://hal.archives-ouvertes.fr/inria-00568893

G. Peyré, Sparse Modeling of Textures, Journal of Mathematical Imaging and Vision, vol.27, issue.2, pp.17-31, 2009.
DOI : 10.1007/s10851-008-0120-3

J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2009.
DOI : 10.1109/TPAMI.2008.79

S. Zhang, Y. Zhan, M. Dewan, J. Huang, D. N. Metaxas et al., Towards robust and effective shape modeling: Sparse shape composition, Medical Image Analysis, vol.16, issue.1, pp.265-277, 2012.
DOI : 10.1016/j.media.2011.08.004
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.6912

M. Chung, H. Lee, P. Kim, and J. C. Ye, Sparse topological data recovery in medical images, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1125-1129, 2011.
DOI : 10.1109/ISBI.2011.5872599

B. Kandel, D. Wolk, J. Gee, and B. Avants, Predicting Cognitive Data from Medical Images Using Sparse Linear Regression, Information Processing in Medical Imaging, pp.86-97, 2013.
DOI : 10.1007/978-3-642-38868-2_8

S. Zhang, Y. Zhan, and D. N. Metaxas, Deformable segmentation via sparse representation and dictionary learning, 2012) 1385 ? 1396, special Issue on the 2011 Conference on Medical Image Computing and Computer Assisted Intervention
DOI : 10.1016/j.media.2012.07.007

N. Yu, T. Qiu, W. Liu-wang, J. Li, and P. Fu, Medical image fusion based on sparse representation with ksvd Medical image super-resolution analysis with sparse representation, World Congress on Medical Physics and Biomedical Engineering Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp.550-553, 2012.

R. Rubinstein, M. Zibulevsky, and M. Elad, Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation, IEEE Transactions on Signal Processing, vol.58, issue.3, pp.1553-1564, 2010.
DOI : 10.1109/TSP.2009.2036477

B. Deka and P. Bora, Despeckling of medical ultrasound images using sparse representation, 2010 International Conference on Signal Processing and Communications (SPCOM), pp.1-5, 2010.
DOI : 10.1109/SPCOM.2010.5560519

R. Fang, T. Chen, and P. C. Sanelli, Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), p.114121, 2013.
DOI : 10.1007/978-3-642-40811-3_15

R. Fang, T. Chen, and P. C. Sanelli, Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning, Medical Image Analysis, vol.17, issue.4, pp.417-428, 2013.
DOI : 10.1016/j.media.2013.02.005

R. Fang, K. Karlsson, T. Chen, and P. C. Sanelli, Improving low-dose blood???brain barrier permeability quantification using sparse high-dose induced prior for Patlak model, sparse Methods for Signal Reconstruction and Medical Image Analysis, pp.866-880, 2014.
DOI : 10.1016/j.media.2013.09.008

S. Zhang, J. Huang, D. Metaxas, W. Wang, and X. Huang, Discriminative sparse representations for cervigram image segmentation, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.133-136, 2010.
DOI : 10.1109/ISBI.2010.5490397
URL : http://cbim.rutgers.edu/dmdocuments/Shaoting_ISBI_2010.pdf

T. Tong, R. Wolz, P. Coup, J. V. Hajnal, and D. Rueckert, Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling, NeuroImage, vol.76, issue.0, pp.11-23, 2013.
DOI : 10.1016/j.neuroimage.2013.02.069
URL : https://hal.archives-ouvertes.fr/hal-00806384

H. Deshpande, P. Maurel, and C. Barillot, Detection of multiple sclerosis lesions using sparse representations and dictionary learning, MICCAI workshop on Sparsity Techniques in Medical Imaging, pp.71-79, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01087339

I. Ramirez and G. Sapiro, An MDL Framework for Sparse Coding and Dictionary Learning, IEEE Transactions on Signal Processing, vol.60, issue.6, pp.2913-2927, 2012.
DOI : 10.1109/TSP.2012.2187203

N. Weiss, D. Rueckert, and A. Rao, Multiple Sclerosis Lesion Segmentation Using Dictionary Learning and Sparse Coding, Medical Image Computing and Computer-Assisted Intervention MICCAI 2013, pp.735-742, 2013.
DOI : 10.1007/978-3-642-40811-3_92

A. Zijdenbos, R. Forghani, and A. 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

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

Y. Pati, R. Rezaiifar, and P. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

S. S. Chen, D. L. Donoho, A. Michael, and . Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.7694

M. Aharon, M. Elad, A. Bruckstein, and K. , <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

K. Engan, S. Aase, and J. Husoy, Frame based signal compression using method of optimal directions (MOD), ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), pp.1-4, 1999.
DOI : 10.1109/ISCAS.1999.779928

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online dictionary learning for sparse coding, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 2009.
DOI : 10.1145/1553374.1553463

N. Tustison, B. Avants, P. Cook, Y. Zheng, A. Egan et al., N4ITK: Improved N3 Bias Correction, N4ITK: Improved N3 bias correction, pp.1310-1320, 2010.
DOI : 10.1109/TMI.2010.2046908

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

W. Wells, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, Multi-modal volume registration by maximization of mutual information, Medical Image Analysis, vol.1, issue.1, pp.35-51, 1996.
DOI : 10.1016/S1361-8415(01)80004-9

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

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