G. B. Frisoni, N. C. Fox, C. R. Jack, . Jr, P. Scheltens et al., The clinical use of structural MRI in Alzheimer disease, Nature Reviews Neurology, vol.15, issue.2, pp.67-77, 2010.
DOI : 10.1038/nrneurol.2009.215

P. Coupe, S. F. Eskildsen, J. V. Manjon, V. S. Fonov, and D. L. Collins, Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease, NeuroImage, vol.59, issue.4, pp.3736-3747, 2012.
DOI : 10.1016/j.neuroimage.2011.10.080

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehericy et al., Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, NeuroImage, vol.56, issue.2, pp.766-781, 2011.
DOI : 10.1016/j.neuroimage.2010.06.013

R. Wolz, V. Julkunen, J. Koikkalainen, E. Niskanen, D. P. Zhang et al., Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease, PLoS ONE, vol.54, issue.3, p.25446, 2011.
DOI : 10.1371/journal.pone.0025446.s002

L. G. Apostolova and P. M. Thompson, Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment, Neuropsychologia, vol.46, issue.6, pp.1597-1612, 2008.
DOI : 10.1016/j.neuropsychologia.2007.10.026

P. Vemuri, J. L. Whitwell, K. Kantarci, K. A. Josephs, J. E. Parisi et al., Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage, Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage, pp.559-567, 2008.
DOI : 10.1016/j.neuroimage.2008.05.012

K. Ritchie and S. Lovestone, The dementias, The Lancet, vol.360, issue.9347, pp.1759-1766, 2002.
DOI : 10.1016/S0140-6736(02)11667-9

G. Mckhann, D. Drachman, M. Folstein, R. Katzman, D. Price et al., Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology, vol.34, issue.7, pp.939-944, 1984.
DOI : 10.1212/WNL.34.7.939

Y. Cho, J. K. Seong, Y. Jeong, and S. Y. Shin, Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data, NeuroImage, vol.59, issue.3, pp.2217-2230, 2012.
DOI : 10.1016/j.neuroimage.2011.09.085

M. Chupin, E. Gerardin, R. Cuingnet, C. Boutet, L. Lemieux et al., Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI, Hippocampus, vol.54, issue.6, pp.579-587, 2009.
DOI : 10.1002/hipo.20626

C. Davatzikos, P. Bhatt, L. M. Shaw, K. N. Batmanghelich, and J. Q. Trojanowski, Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification, Neurobiology of Aging, vol.32, issue.12, pp.2322-2319, 2011.
DOI : 10.1016/j.neurobiolaging.2010.05.023

J. Koikkalainen, J. Lotjonen, L. Thurfjell, D. Rueckert, G. Waldemar et al., Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease, NeuroImage, vol.56, issue.3, pp.1134-1144, 2011.
DOI : 10.1016/j.neuroimage.2011.03.029

C. Misra, Y. Fan, and C. Davatzikos, Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI???, NeuroImage, vol.44, issue.4, pp.1415-1422, 2009.
DOI : 10.1016/j.neuroimage.2008.10.031

O. Querbes, F. Aubry, J. Pariente, J. A. Lotterie, J. F. Demonet et al., Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve, Brain, vol.132, issue.8, pp.2036-2047, 2009.
DOI : 10.1093/brain/awp105

E. Westman, A. Simmons, J. S. Muehlboeck, P. Mecocci, B. Vellas et al., AddNeuroMed and ADNI: Similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America, NeuroImage, vol.58, issue.3, pp.818-828, 2011.
DOI : 10.1016/j.neuroimage.2011.06.065

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

P. Coupe, J. V. Manjon, E. Gedamu, D. Arnold, M. Robles et al., Robust Rician noise estimation for MR images, Medical Image Analysis, vol.14, issue.4, pp.483-493, 2010.
DOI : 10.1016/j.media.2010.03.001
URL : https://hal.archives-ouvertes.fr/inserm-00486495

J. G. Sled, A. P. Zijdenbos, and A. C. 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

D. L. Collins, P. Neelin, T. M. Peters, and A. C. Evans, Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space, Journal of Computer Assisted Tomography, vol.18, issue.2, pp.192-205, 1994.
DOI : 10.1097/00004728-199403000-00005

S. F. Eskildsen, P. Coupe, V. Fonov, J. V. Manjon, K. K. Leung et al., BEaST: Brain extraction based on nonlocal segmentation technique, NeuroImage, vol.59, issue.3, 2011.
DOI : 10.1016/j.neuroimage.2011.09.012
URL : https://hal.archives-ouvertes.fr/inserm-00629187

S. F. Eskildsen and L. R. Ostergaard, Active surface approach for extraction of the human cerebral cortex from MRI. Medical image computing and computer-assisted intervention : MICCAI -International Conference on, Medical Image Computing and Computer-Assisted Intervention, vol.9, pp.823-830, 2006.

V. Fonov, A. C. Evans, K. Botteron, C. R. Almli, R. C. Mckinstry et al., Unbiased average age-appropriate atlases for pediatric studies, NeuroImage, vol.54, issue.1, pp.313-327, 2011.
DOI : 10.1016/j.neuroimage.2010.07.033

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.
DOI : 10.1109/TPAMI.2005.159

J. P. Lerch, J. Pruessner, A. P. Zijdenbos, D. L. Collins, S. J. Teipel et al., Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls, Neurobiology of Aging, vol.29, issue.1, pp.23-30, 2008.
DOI : 10.1016/j.neurobiolaging.2006.09.013

S. F. Eskildsen, V. Fonov, P. Coupé, L. R. Østergaard, and D. L. Collins, Prediction of Alzheimer???s Disease in Subjects with Mild Cognitive Impairment Using Structural Patterns of Cortical Thinning*, 15th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, pp.156-159, 2011.
DOI : 10.1007/978-3-642-21683-1_39

S. F. Eskildsen, P. Coupe, V. Fonov, L. R. Østergaard, and D. L. Collins, Effect of non-local means denoising on cortical segmentation accuracy with FACE, 17th Annual Meeting of the Organization on Human Brain Mapping, p.673
URL : https://hal.archives-ouvertes.fr/hal-00645491

Y. Fan, D. Shen, R. C. Gur, R. E. Gur, and C. Davatzikos, COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements, IEEE Transactions on Medical Imaging, vol.26, issue.1, pp.93-105, 2007.
DOI : 10.1109/TMI.2006.886812