Z. Ding, J. Gore, and A. Anderson, Classification and quantification of neuronal fiber pathways using diffusion tensor MRI, Magnetic Resonance in Medicine, vol.12, issue.4, pp.716-721, 2003.
DOI : 10.1002/mrm.10415

I. Corouge, S. Gouttard, and G. Gerig, Towards a shape model of white matter fiber bundles using diffusion tensor MRI, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.344-347, 2004.
DOI : 10.1109/ISBI.2004.1398545
URL : https://hal.archives-ouvertes.fr/inserm-00772619

K. Lim and J. Helpern, Neuropsychiatric applications of DTI - a review, NMR in Biomedicine, vol.46, issue.7-8, pp.587-593, 2002.
DOI : 10.1002/nbm.789

I. Corouge, S. Gouttard, and G. Gerig, A Statistical Shape Model of Individual Fiber Tracts Extracted from Diffusion Tensor MRI, In: MICCAI. LNCS, vol.3217, pp.671-679, 2004.
DOI : 10.1007/978-3-540-30136-3_82
URL : https://hal.archives-ouvertes.fr/inserm-00771247

P. T. Fletcher and S. Joshi, Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors, Workshop CVAMIA LNCS, vol.3117, pp.87-98, 2004.
DOI : 10.1007/978-3-540-27816-0_8

P. T. Fletcher, Statistical variability in nonlinear spaces: Application to shape analysis and DT-MRI, 2004.

P. G. Batchelor, M. Moakher, D. Atkinson, F. Calamante, and A. Connelly, A rigorous framework for diffusion tensor calculus, Magnetic Resonance in Medicine, vol.103, issue.1, pp.221-225, 2005.
DOI : 10.1002/mrm.20334

S. Helgason, Differential Geometry, Lie Groups, and Symmetric Spaces, 1978.
DOI : 10.1090/gsm/034

M. Fréchet, LesélémentsLes´Leséléments aléatoires de nature quelconque dans un espace distancié, Ann. Inst. H. Poincaré, pp.215-310, 1948.

X. Pennec, Probabilities and statistics on Riemannian manifolds: basic tools for geometric measurements, IEEE Workshop on Nonlinear Signal and Image Processing, 1999.
URL : https://hal.archives-ouvertes.fr/inria-00615833

T. Cootes, C. Taylor, D. Cooper, and J. Graham, Active Shape Models-Their Training and Application, Computer Vision and Image Understanding, vol.61, issue.1, pp.38-59, 1995.
DOI : 10.1006/cviu.1995.1004
URL : https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:1d1862&datastreamId=POST-PEER-REVIEW-PUBLISHERS.PDF

C. F. Westin, S. Maier, H. Mamata, A. Nabavi, F. Jolesz et al., Processing and visualization for diffusion tensor MRI, Medical Image Analysis, vol.6, issue.2, pp.93-108, 2002.
DOI : 10.1016/S1361-8415(02)00053-1

P. Fillard and G. Gerig, Analysis Tool for Diffusion Tensor MRI, In: MICCAI. LNCS, vol.2879, pp.967-968, 2003.
DOI : 10.1007/978-3-540-39903-2_126
URL : https://hal.archives-ouvertes.fr/inria-00502755

C. Goodall, Procrustes methods in the statistical analysis of shape, J.R. Statist. Soc. B, vol.53, pp.285-239, 1991.

J. Gilmore, G. Zhai, K. Wilber, J. Smith, W. Lin et al., 3 Tesla magnetic resonance imaging of the brain in newborns, Psychiatry Research: Neuroimaging, vol.132, issue.1, pp.81-85, 2004.
DOI : 10.1016/j.pscychresns.2004.04.004

M. Rutherford, MRI of the Neonatal Brain, WB Saunders Ltd, 2002.

P. Basser and C. Pierpaoli, Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI, J. Magn. Reson. B, vol.3, pp.209-219, 1996.

G. Gerig, I. Corouge, C. Vachet, K. R. Krishnan, and J. R. Macfall, Quantitative analysis of diffusion properties of white matter fiber tracts: A validation study, 2005.