P. M. Thompson and A. W. Toga, A framework for computational anatomy, Computing and Visualization in Science, vol.5, issue.1, pp.13-34, 2002.
DOI : 10.1007/s00791-002-0084-6

U. Grenander and M. I. Miller, Computational anatomy: an emerging discipline, Quarterly of Applied Mathematics, vol.56, issue.4, pp.617-694, 1998.
DOI : 10.1090/qam/1668732

M. K. Chung, K. J. Worsley, T. Paus, C. Cherif, D. L. Collins et al., A Unified Statistical Approach to Deformation-Based Morphometry, NeuroImage, vol.14, issue.3, pp.595-606, 2001.
DOI : 10.1006/nimg.2001.0862

B. Avants and J. C. Gee, Geodesic estimation for large deformation anatomical shape averaging and interpolation, NeuroImage, vol.23, pp.139-50, 2004.
DOI : 10.1016/j.neuroimage.2004.07.010

D. Shen and C. Davatzikos, Very High-Resolution Morphometry Using Mass-Preserving Deformations and HAMMER Elastic Registration, NeuroImage, vol.18, issue.1, pp.28-41, 2003.
DOI : 10.1006/nimg.2002.1301

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.408.1684

G. E. Christensen, R. D. Rabbitt, and M. I. Miller, Deformable templates using large deformation kinematics, IEEE Transactions on Image Processing, vol.5, issue.10, pp.1435-1447, 1996.
DOI : 10.1109/83.536892

M. I. Miller, Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms, NeuroImage, vol.23, pp.19-33, 2004.
DOI : 10.1016/j.neuroimage.2004.07.021

D. L. Collins, T. M. Peters, and A. C. Evans, <title>Automated 3D nonlinear deformation procedure for determination of gross morphometric variability in human brain</title>, Visualization in Biomedical Computing 1994, pp.180-190, 1994.
DOI : 10.1117/12.185178

C. Studholme, V. Cardenas, N. Schuff, H. Rosen, B. Miller et al., Detecting Spatially Consistent Structural Differences in Alzheimer???s and Fronto Temporal Dementia Using Deformation Morphometry, Proc. MICCAI, pp.41-48, 2001.
DOI : 10.1007/3-540-45468-3_6

P. M. Thompson, Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia, NeuroImage, vol.23, issue.1, pp.2-18, 2004.
DOI : 10.1016/j.neuroimage.2004.07.071

P. M. Thompson, J. N. Giedd, R. P. Woods, D. Macdonald, A. C. Evans et al., Growth patterns in the developing brain detected by using continuum mechanical tensor maps, Nature, vol.56, issue.6774, pp.190-193, 2000.
DOI : 10.1038/35004593

P. Cachier and D. Rey, Symmetrization of the Non-rigid Registration Problem Using Inversion-Invariant Energies: Application to Multiple Sclerosis, MICCAI'00, pp.472-481, 2000.
DOI : 10.1007/978-3-540-40899-4_48

A. D. Leow, A. D. Klunder, C. R. Jack, A. W. Toga, A. M. Dale et al., Longitudinal stability of MRI for mapping brain change using tensor-based morphometry, For the ADNI Preparatory Phase Study, 2005.
DOI : 10.1016/j.neuroimage.2005.12.013

J. Ashburner, J. Anderson, and K. Friston, High-Dimensional Image Registration Using Symmetric Priors, NeuroImage, vol.9, issue.6, pp.619-628, 1999.
DOI : 10.1006/nimg.1999.0437

X. Pennec, R. Stefanescu, V. Arsigny, P. Fillard, and N. Ayache, Riemannian Elasticity: A Statistical Regularization Framework for Non-linear Registration, Proc. MICCAI 2005, pp.943-950, 2005.
DOI : 10.1007/11566489_116

URL : https://hal.archives-ouvertes.fr/inria-00502675

V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, Fast and Simple Calculus on Tensors in the Log-Euclidean Framework, Proc. MICCAI 2005, 2005.
DOI : 10.1007/11566465_15

URL : https://hal.archives-ouvertes.fr/inria-00502669

R. P. Woods, Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation, NeuroImage, vol.18, issue.3, pp.769-88, 2003.
DOI : 10.1016/S1053-8119(03)00019-3

A. Leow, S. Huang, A. Geng, J. Becker, S. Davis et al., Inverse Consistent Mapping in 3D Deformable Image Registration: Its Construction and Statistical Properties, 2005.
DOI : 10.1007/11505730_41

N. N. Cencov, Statistical Decision Rules and Optimal Inference, Translations in Mathematics, vol.14, 1982.

M. Nielsen, P. Johansen, . Jackson, and . Lautrup, Statistical warps, a least committed model, 2001.

A. Bhattacharyya, On a Measure of Divergence Between Two Statistical Populations Defined by their Probability Distributions, Bull. Calcutta Math. Soc, vol.35, p.99, 1943.

T. M. Cover and J. A. Thomas, Elements of Information Theory, 1991.

V. D. Liseikin, Grid Generation Methods, 1999.

E. D. Agostino, F. Maes, D. Vandermeulen, and P. Suetens, A viscous fluid model for multimodal non-rigid image registration using mutual information, Medical Image Analysis, vol.7, issue.4, pp.565-575, 2003.
DOI : 10.1016/S1361-8415(03)00039-2

B. Kim, J. L. Boes, K. A. Frey, and C. R. Meyer, Mutual Information for Automated Unwarping of Rat Brain Autoradiographs, NeuroImage, vol.5, issue.1, pp.31-40, 1997.
DOI : 10.1006/nimg.1996.0251

P. Lorenzen, B. Davis, and S. Joshi, Model based symmetric information theoretic large deformation multi-modal image registration, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.720-723, 2004.
DOI : 10.1109/ISBI.2004.1398639

C. J. Twining, T. Cootes, S. Marsland, V. Petrovic, R. Schestowitz et al., A Unified Information-Theoretic Approach to Groupwise Non-rigid Registration and Model Building, 2005.
DOI : 10.1007/11505730_1

J. P. Pluim, J. B. Antoine-maintz, and M. A. Viergever, <tex>$f$</tex>-Information Measures in Medical Image Registration, IEEE Transactions on Medical Imaging, vol.23, issue.12, pp.1508-1516, 2004.
DOI : 10.1109/TMI.2004.836872

. Fig, Image registration using the unbiased deformation algorithm, with the sum of the squared intensity difference (SSD) as the cost function. 3D scans from a semantic dementia patient imaged at two time points were nonlinearly registered to estimate the profile of volumetric change. The later scan (first column) was chosen to be the source image