C. Carbonnel, V. Seux, V. Pauly, C. Oddoze, C. Roubicek et al., Quelle m??thode d?????valuation de la fonction r??nale utiliser chez le sujet ??g?? hospitalis?? en unit?? de court s??jour g??riatrique??? Comparaison de quatre m??thodes, La Revue de M??decine Interne, vol.29, issue.5, pp.364-369, 2008.
DOI : 10.1016/j.revmed.2007.12.008

D. W. Cockcroft and M. H. Gault, Prediction of Creatinine Clearance from Serum Creatinine, Nephron, vol.16, issue.1, pp.31-41, 1976.
DOI : 10.1159/000180580

A. S. Levey, J. P. Bosch, J. B. Lewis, T. Greene, N. Rogers et al., A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation, Annals of Internal Medicine, vol.130, issue.6, pp.461-470, 1999.
DOI : 10.7326/0003-4819-130-6-199903160-00002

A. Taylor and J. V. Nally, Clinical applications of renal scintigraphy., American Journal of Roentgenology, vol.164, issue.1, pp.31-41, 1995.
DOI : 10.2214/ajr.164.1.7998566

F. P. Esteves, A. Taylor, A. Manatunga, R. D. Folks, M. Krishnan et al., Tc-MAG3 Renography: Normal Values for MAG3 Clearance and Curve Parameters, Excretory Parameters, and Residual Urine Volume, American Journal of Roentgenology, vol.187, issue.6, pp.610-617, 2006.
DOI : 10.2214/AJR.05.1550

C. D. Russell, A. T. Taylor, and E. V. Dubovsky, Measurement of renal function with Technetium-99m-MAG3 in children and adults, Journal of Nuclear Medicine, vol.37, issue.4, pp.588-593, 1996.

C. D. Russell and E. V. Dubovsky, Measurement of renal function with radionuclides, Journal of Nuclear Medicine, vol.30, pp.2053-2057, 1989.

Y. Inoue, K. Yoshikawa, T. Suzuki, N. Katayama, I. Yokoyama et al., Attenuation correction in evaluating renal function in children and adults by a camera-based method, Journal of Nuclear Medicine, vol.41, issue.5, pp.823-829, 2000.

N. Grenier, I. Mendichovszky, B. D. De-senneville, S. Roujol, P. Desbarats et al., Measurement of Glomerular Filtration Rate With Magnetic Resonance Imaging: Principles, Limitations, and Expectations, Seminars in Nuclear Medicine, vol.38, issue.1, pp.47-55, 2008.
DOI : 10.1053/j.semnuclmed.2007.09.004

URL : https://hal.archives-ouvertes.fr/hal-00353113

C. Brochot, B. Bessoud, D. Balvay, C. Cuenod, N. Siauve et al., Evaluation of antiangiogenic treatment effects on tumors' microcirculation by Bayesian physiological pharmacokinetic modeling and magnetic resonance imaging, Magnetic Resonance Imaging, vol.24, issue.8, pp.1059-1067, 2006.
DOI : 10.1016/j.mri.2006.04.002

URL : https://hal.archives-ouvertes.fr/inserm-00140800

I. A. Mendichovszky, M. Cutajar, and I. Gordon, Reproducibility of the aortic input function (AIF) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the kidneys in a volunteer study, Press, Corrected Proof, 2008.
DOI : 10.1016/j.ejrad.2008.09.025

H. S. Teh, E. S. Ang, W. Wong, S. B. Tan, A. G. Tan et al., MR Renography Using a Dynamic Gradient-Echo Sequence and Low-Dose Gadopentetate Dimeglumine as an Alternative to Radionuclide Renography, American Journal of Roentgenology, vol.181, issue.2, pp.441-450, 2003.
DOI : 10.2214/ajr.181.2.1810441

J. Taylor, P. E. Summers, S. F. Keevil, A. M. Saks, J. Diskin et al., Magnetic resonance renography: Optimisation of pulse sequence parameters and Gd-DTPA dose, and comparison with radionuclide renography, Magnetic Resonance Imaging, vol.15, issue.6, pp.637-649, 1997.
DOI : 10.1016/S0730-725X(97)00034-9

H. Uematsu, T. Matsuda, T. Tsuchida, H. Inoue, K. Hayashi et al., Semi-quantitative approach to estimating GFR by magnetic resonance imaging, Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine, vol.2, issue.3, pp.171-176, 2000.
DOI : 10.1148/radiology.175.3.2343131

V. S. Lee, H. Rusinek, L. Bokacheva, A. J. Huang, N. Oesingmann et al., Renal function measurements from MR renography and a simplified multicompartmental model, American Journal of Physiology-Renal Physiology, vol.292, issue.5, pp.1548-1559, 2007.
DOI : 10.1161/01.RES.10.3.393

D. R. Gutierrez, K. Wells, O. Diaz-montesdeoca, A. Moran-santana, I. A. Mendichovszky et al., Partial volume effects in dynamic contrast magnetic resonance renal studies, European Journal of Radiology, vol.75, issue.2, 2009.
DOI : 10.1016/j.ejrad.2009.04.073

H. Gray, Anatomy of the Human Body, Philadelphia : Lea and Febiger, Bartleby.com, 1918.

M. Catala, J. M. Andre, G. Katsanis, and J. Poirier, Histologie : organes, systèmes et appareils, 2007.

R. Martzolff, Le rein et la formation de l'urine, 2007.

H. J. Michaely, S. Sourbron, O. Dietrich, U. Attenberger, M. F. Reiser et al., Functional renal MR imaging: an overview, Abdominal Imaging, vol.14, issue.Suppl 5, pp.758-771, 2007.
DOI : 10.1148/radiology.190.1.8259395

N. Grenier, F. Basseau, M. Ries, B. Tyndal, R. Jones et al., Functional MRI of the kidney, Abdominal Imaging, vol.28, issue.2, pp.164-175, 2003.
DOI : 10.1007/s00261-001-0183-8

M. F. Bellin, MR contrast agents, the old and the new, European Journal of Radiology, vol.60, issue.3, pp.314-323, 2006.
DOI : 10.1016/j.ejrad.2006.06.021

N. Grenier, M. Pedersen, and O. Hauger, Contrast agents for functional and cellular MRI of the kidney, European Journal of Radiology, vol.60, issue.3, pp.341-352, 2006.
DOI : 10.1016/j.ejrad.2006.06.024

N. Michoux, J. P. Vallee, A. Pechere-bertschi, X. Montet, L. Buehler et al., Analysis of contrast-enhanced MR images to assess renal function, Magnetic Resonance Materials in Physics, Biology and Medicine, vol.31, issue.4, pp.167-179, 2006.
DOI : 10.1093/comjnl/7.4.308

N. Michoux, X. Montet, A. Pefchere, M. K. Ivancevic, P. Y. Martin et al., Parametric and quantitative analysis of MR renographic curves for assessing the functional behaviour of the kidney, European Journal of Radiology, vol.54, issue.1, pp.124-135, 2005.
DOI : 10.1016/j.ejrad.2004.04.010

M. Dujardin, R. Luypaert, F. Vandenbroucke, P. Van-der-niepen, S. Sourbron et al., Combined T1-based perfusion MRI and MR angiography in kidney: First experience in normals and pathology, European Journal of Radiology, vol.69, issue.3, pp.542-549, 2009.
DOI : 10.1016/j.ejrad.2007.11.033

W. K. Rohrschneider, S. Haufe, M. Wiesel, B. Toenshoff, R. Wunsch et al., Functional and Morphologic Evaluation of Congenital Urinary Tract Dilatation by Using Combined Static-Dynamic MR Urography: Findings in Kidneys with a Single Collecting System, Radiology, vol.224, issue.3, pp.683-694, 2002.
DOI : 10.1148/radiol.2243011207

S. P. Sourbron, H. J. Michaely, M. F. Reiser, and S. O. Schoenberg, MRI-Measurement of Perfusion and Glomerular Filtration in the Human Kidney With a Separable Compartment Model, Investigative Radiology, vol.43, issue.1, pp.40-48, 2008.
DOI : 10.1097/RLI.0b013e31815597c5

N. Hackstein, C. Wiegand, W. S. Rau, and A. C. Langheinrich, Glomerular Filtration Rate Measured by Using Triphasic Helical CT with a Two-Point Patlak Plot Technique, Radiology, vol.230, issue.1, pp.221-226, 2004.
DOI : 10.1148/radiol.2301021266

L. Bokacheva, H. Rusinek, J. L. Zhang, Q. Chen, and V. S. Lee, Estimates of glomerular filtration rate from MR renography and tracer kinetic models, Journal of Magnetic Resonance Imaging, vol.12, issue.2, pp.371-382, 2009.
DOI : 10.2214/ajr.181.1.1810147

B. D. De-senneville, I. A. Mendichovszky, S. Roujol, G. I. , C. Moonen et al., Improvement of MRI-functional measurement with automatic movement correction in native and transplanted kidneys, Journal of Magnetic Resonance Imaging, vol.15, issue.4, pp.970-978, 2008.
DOI : 10.1038/jcbfm.1983.1

URL : https://hal.archives-ouvertes.fr/hal-01503903

J. B. Maintz and M. A. Viergever, A survey of medical image registration, Medical Image Analysis, vol.2, issue.1, pp.1-36, 1998.
DOI : 10.1016/S1361-8415(01)80026-8

G. E. Christensen, R. D. Rabbit, 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

E. L. Giele, J. A. De-priester, J. A. Blom, J. A. De-boer, J. M. Van-engelshoven et al., Movement correction of the kidney in dynamic MRI scans using FFT phase difference movement detection, Journal of Magnetic Resonance Imaging, vol.2, issue.6, pp.741-749, 2001.
DOI : 10.1016/S1361-8415(98)80024-8

Y. Sun, M. Jolly, and J. M. Moura, Integrated registration of dynamic renal perfusion MR images, Proceedings of the International Conference on Image Processing, pp.1923-1929, 2004.

T. Song, V. S. Lee, H. Rusinek, S. Wong, and A. F. Laine, Integrated Four Dimensional Registration and Segmentation of Dynamic Renal MR Images, Proceedings of the 9th Conference on Medical Image Computing and Computer-Assisted Intervention, pp.758-65, 2006.
DOI : 10.1007/11866763_93

T. Song, V. S. Lee, H. Rusinek, S. Wong, and A. F. Laine, Four Dimensional MR Image Analysis of Dynamic Renography, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3134-3137, 2006.
DOI : 10.1109/IEMBS.2006.260178

T. Song, V. S. Lee, H. Rusinek, M. Kaur, and A. F. Laine, Automatic 4-D Registration in Dynamic MR Renography Based on Over-Complete Dyadic Wavelet and Fourier Transforms, Proceedings of the 8th Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2005), pp.205-218, 2005.
DOI : 10.1007/11566489_26

Y. Sun, J. M. Moura, and H. Chien, Subpixel registration in renal perfusion MR image sequence, Proceedings of the IEEE International Symposium on Biomedical Imaging : Macro to Nano, pp.700-703, 2004.

F. G. Zoellner, R. Sance, P. Rogelj, M. J. Ledesma-carbayo, J. Roervik et al., Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses, Computerized Medical Imaging and Graphics, vol.33, issue.3, pp.171-181, 2009.
DOI : 10.1016/j.compmedimag.2008.11.004

Y. Sun, M. Jolly, and J. M. Moura, Contrast-Invariant Registration of Cardiac and Renal MR Perfusion Images, Proceedings of 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2004), pp.903-913, 2004.
DOI : 10.1007/978-3-540-30135-6_110

P. Rogelj, F. G. Zoellner, S. Kova?i?, and A. Lundervold, Motion correction of contrast-enhanced MRI time series of kidney, Proceedings of the 16th International Electrotechnical and Computer Science Conference, pp.191-194, 2007.

J. P. Pluim, J. B. Maintz, and M. A. Viergever, Mutual-information-based registration of medical images: a survey, IEEE Transactions on Medical Imaging, vol.22, issue.8, pp.986-1004, 2003.
DOI : 10.1109/TMI.2003.815867

P. Rogelj, S. Kovacic, and J. C. Gee, Point similarity measures for non-rigid registration of multi-modal data, Computer Vision and Image Understanding, vol.92, issue.1, pp.112-140, 2003.
DOI : 10.1016/S1077-3142(03)00116-4

F. J. Richard, A. M. Samson, and C. A. Cuénod, A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences, Statistics and Computing, vol.8, issue.1, pp.465-478, 2009.
DOI : 10.1161/01.CIR.92.7.1902

URL : https://hal.archives-ouvertes.fr/hal-00187723

V. S. Lee, H. Rusinek, M. E. Noz, P. Lee, M. Raghavan et al., Dynamic Three-dimensional MR Renography for the Measurement of Single Kidney Function: Initial Experience, Radiology, vol.227, issue.1, pp.289-294, 2003.
DOI : 10.1148/radiol.2271020383

J. P. Cocquerez, S. Philipp, . Ph, J. M. Bolon, D. Chassery et al., Analyse d'images : filtrage et segmentation, 1995.
URL : https://hal.archives-ouvertes.fr/hal-00706168

C. H. Coulam, D. M. Bouley, and F. G. Sommer, Measurement of renal volumes with contrast-enhanced MRI, Journal of Magnetic Resonance Imaging, vol.165, issue.2, pp.174-179, 2002.
DOI : 10.2214/ajr.165.6.7484583

D. Lv, J. Zhuang, H. Chen, J. Wang, Y. Xu et al., Dynamic contrast-enhanced magnetic resonance images of the kidney, IEEE Engineering in Medicine and Biology Magazine, vol.27, issue.5, pp.36-41, 2008.

J. A. De-priester, A. G. Kessels, E. L. Giele, J. A. De-boer, M. H. Christiaans et al., MR renography by semiautomated image analysis: Performance in renal transplant recipients, Journal of Magnetic Resonance Imaging, vol.86, issue.2, pp.134-140, 2001.
DOI : 10.1037/0033-2909.86.2.420

Y. Boykov and G. Funka-lea, Graph Cuts and Efficient N-D Image Segmentation, International Journal of Computer Vision, vol.18, issue.9, pp.109-131, 2006.
DOI : 10.1109/TPAMI.2002.1114849

F. G. Zoellner, R. Sance, A. Anderlik, J. Roervik, M. Kocinski et al., Towards quantification of kidney function by clustering volumetric MRI perfusion time series, Magnetic Resonance Materials in Physics Biology and Medicine, vol.19, pp.103-104, 2006.

T. Song, V. S. Lee, H. Rusinek, J. B. Sajous, and A. F. Laine, Registration and segmentation of dynamic three-dimensional MR renography based on Fourier representations and k-means clustering, Proceedings of the 13th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, p.page, 2005.

F. G. Zoellner, M. Kocinski, A. Lundervold, and J. Roervik, Bildverarbeitung für die Medizin 2007, chapter Assessment of Renal Function from 3D Dynamic Contrast Enhanced MR images Using Independent Component Analysis, pp.237-241, 2007.

H. Rusinek, Y. Boykov, S. Kaur, L. Wong, J. B. Bokacheva et al., Performance of an automated segmentation algorithm for 3D MR renography, Magnetic Resonance in Medicine, vol.46, issue.6, pp.1159-1167, 2007.
DOI : 10.1002/mrm.21240

T. Martinetz, S. Berkovich, and K. Schulten, 'Neural-gas' network for vector quantization and its application to time-series prediction, IEEE Transactions on Neural Networks, vol.4, issue.4, pp.558-569, 1993.
DOI : 10.1109/72.238311

A. Gersho and R. M. Gray, Vector quantization and signal compression, 1992.
DOI : 10.1007/978-1-4615-3626-0

T. Martinetz and K. Schulten, Topology representing networks, Neural Networks, vol.7, issue.3, pp.507-522, 1994.
DOI : 10.1016/0893-6080(94)90109-0

H. Frezza-buet, Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network, Neurocomputing, vol.71, issue.7-9, pp.1191-1202, 2008.
DOI : 10.1016/j.neucom.2007.12.024

URL : https://hal.archives-ouvertes.fr/hal-00282087

B. Fritzke, A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, pp.625-632, 1995.

L. Bottou and Y. Bengio, Convergence properties of the k-means algorithms, Proceedings of the 9th Conference on Neural Information Processing Systems (NIPS), pp.585-92, 1995.

M. Cottrell, B. Hammer, A. Hasenfuss, and T. Villmann, Batch and median neural gas, Neural Networks, vol.19, issue.6-7, pp.762-771, 2006.
DOI : 10.1016/j.neunet.2006.05.018

URL : https://hal.archives-ouvertes.fr/hal-00107462

T. Kohonen, Self-organizing maps, 2001.

G. Patané and M. Russo, The enhanced LBG algorithm, Neural Networks, vol.14, issue.9, pp.1219-1237, 2001.
DOI : 10.1016/S0893-6080(01)00104-6

A. Gersho, Asymptotically optimal block quantization Information Theory, IEEE Transactions on, vol.25, issue.4, pp.373-380, 1979.
DOI : 10.1109/tit.1979.1056067

C. Chinrungrueng and C. H. Sequin, Optimal adaptive k-means algorithm with dynamic adjustment of learning rate, IEEE Transactions on Neural Networks, vol.6, issue.1, pp.157-169, 1995.
DOI : 10.1109/72.363440

S. Ben-david, D. Pal, and H. U. Simon, Stability of k-Means Clustering, Proceedings of the 20th Annual Conference on Learning Theory, pp.20-34, 2007.
DOI : 10.1007/978-3-540-72927-3_4

M. Ackerman and S. Ben-david, Measures of clustering quality : A working set of axioms for clustering, Proceedings of the 22nd Conference on Neural Information Processing Systems, 2008.

M. Meila, Comparing clusterings???an information based distance, Journal of Multivariate Analysis, vol.98, issue.5, pp.873-895, 2007.
DOI : 10.1016/j.jmva.2006.11.013

R. J. Campello, A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment, Pattern Recognition Letters, vol.28, issue.7, pp.833-841, 2007.
DOI : 10.1016/j.patrec.2006.11.010

E. B. Fowlkes and C. L. Mallows, A Method for Comparing Two Hierarchical Clusterings, Journal of the American Statistical Association, vol.66, issue.383, pp.553-569, 1983.
DOI : 10.1016/0022-5371(76)90049-9

J. Kleinberg, An impossibility theorem for clustering, Proceedings of the 16th Conference on Neural Information Processing Systems, 2002.

M. Ackerman and S. Ben-david, Clusterability : A theoretical study, Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pp.1-8, 2009.

E. Pichon, A. Tannenbaum, and R. Kikinis, A statistically based flow for image segmentation, Medical Image Analysis, vol.8, issue.3, pp.267-274, 2004.
DOI : 10.1016/j.media.2004.06.006

A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. Palmer, Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Transactions on Medical Imaging, vol.13, issue.4, pp.716-740, 1994.
DOI : 10.1109/42.363096

V. Vapnik, Statistical Learning Theory, 1998.

V. Vapnik, The Nature of Statistical Learning Theory, 1999.

M. J. Kearns and U. V. Vazirani, An introduction to computational learning theory, 1994.

U. , V. Luxburg, and S. Ben-david, Towards a statistical theory of clustering, Proceedings of the PASCAL Workshop on Statistics and Optimization of Clustering, 2005.

R. D. Nowak, Wavelet-based Rician noise removal for magnetic resonance imaging, IEEE Transactions on Image Processing, vol.8, issue.10, pp.1408-1427, 1999.
DOI : 10.1109/83.791966

J. Sijbers, A. J. Den-dekker, P. Scheunders, and D. Van-dyck, Maximum-likelihood estimation of Rician distribution parameters, IEEE Transactions on Medical Imaging, vol.17, issue.3, 1998.
DOI : 10.1109/42.712125

J. Sijbers, D. Poot, A. J. Dekker, and W. Pintjens, Automatic estimation of the noise variance from the histogram of a magnetic resonance image, Physics in Medicine and Biology, vol.52, issue.5, pp.1335-1383, 2007.
DOI : 10.1088/0031-9155/52/5/009

M. A. Ballester, A. Zisserman, and M. Brady, Estimation of the partial volume effect in MRI, Medical Image Analysis, vol.6, pp.389-405, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615630

D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank, PET-CT image registration in the chest using free-form deformations, IEEE Transactions on Medical Imaging, vol.22, issue.1, pp.120-128, 2003.
DOI : 10.1109/TMI.2003.809072

B. Chevaillier, Y. Ponvianne, J. L. Collette, D. Mandry, M. Claudon et al., Functional semi-automated segmentation of renal DCE-MRI sequences, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.525-528, 2008.
DOI : 10.1109/ICASSP.2008.4517662

URL : https://hal.archives-ouvertes.fr/hal-00276131

B. Chevaillier, Y. Ponvianne, J. L. Collette, D. Mandry, M. Claudon et al., Functional semi-automated segmentation of renal DCE-MRI sequences using a growing neural gas algorithm, Proceedings of the 16th European Signal Processing Conference Lausanne (Switzerland), 2008.
URL : https://hal.archives-ouvertes.fr/hal-00327613

J. Harthong, Probabilités et statistiques, de l'intuition aux applications, Diderot éditeur, Arts et Sciences, 1996.

L. Bottou, Online algorithms and stochastic approximations, Online Learning and Neural Networks, 1998.

H. J. Kushner and Y. G. , Stochastic Approximation and Recursive Algorithms and Applications, 2003.