R. Alterovitz, K. Goldberg, J. Kurhanewicz, J. Pouliot, and I. C. Hsu, Image registration for prostate MR spectroscopy using biomechanical modeling and optimization of force and stiffness parameters, Proc. of 26th, 2004.

G. L. Andriole, E. D. Crawford, R. L. Grubb, S. S. Buys, D. Chia et al., Mortality Results from a Randomized Prostate-Cancer Screening Trial, New England Journal of Medicine, vol.360, issue.13, pp.1310-1319, 2009.
DOI : 10.1056/NEJMoa0810696

B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain, Medical Image Analysis, vol.12, issue.1, pp.26-41, 2008.
DOI : 10.1016/j.media.2007.06.004

M. S. Bartlett, Properties of Sufficiency and Statistical Tests, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.160, issue.901, pp.268-282, 1937.
DOI : 10.1098/rspa.1937.0109

M. Baumann, P. Mozer, V. Daanen, and J. Troccaz, Prostate Biopsy Assistance System with Gland Deformation Estimation for Enhanced Precision, Proc. of MICCAI, pp.57-64, 2009.
DOI : 10.1007/978-3-642-04268-3_9

URL : https://hal.archives-ouvertes.fr/hal-00420370

M. Baumann, P. Mozer, V. Daanen, and J. Troccaz, Prostate biopsy tracking with deformation estimation, Medical Image Analysis, vol.16, issue.3, 2011.
DOI : 10.1016/j.media.2011.01.008

URL : https://hal.archives-ouvertes.fr/hal-00606407

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.4, pp.509-522, 2002.
DOI : 10.1109/34.993558

P. Besl and N. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.239-256, 1992.
DOI : 10.1109/34.121791

A. Bhattacharyya, On a measure of divergence between two statistical populations defined by their probability distribution, Bulletin of the Calcutta Mathematical Society, vol.35, pp.99-110, 1943.

H. A. Bogers, J. P. Sedelaar, H. P. Beerlage, J. J. De-la-rosette, F. M. Debruyne et al., Contrast-enhanced three-dimensional power doppler angiography of the human prostate: correlation with biopsy outcome, Urology, vol.54, issue.1, pp.97-104, 1999.
DOI : 10.1016/S0090-4295(99)00040-0

A. Du-bois-d-'aische, M. D. Craene, S. Haker, N. Weisenfeld, C. Tempany et al., Improved Non-rigid Registration of Prostate MRI, Proc. of MICCAI, pp.845-852, 2004.
DOI : 10.1007/978-3-540-30135-6_103

F. Bookstein, Morphometric Tools for Landmark Data: Geometry and Biology, 1991.
DOI : 10.1017/CBO9780511573064

P. Carroll and K. Shinohara, Transrectal Ultrasound Guided Prostate Biopsy, 2010.

T. Chen, S. Kim, J. Zhou, D. Metaxas, G. Rajagopal et al., 3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy, Proc. of MICCAI, pp.43-50, 2009.
DOI : 10.1007/978-3-642-04268-3_6

M. R. Cheung and K. Krishnan, Interactive Deformation Registration of Endorectal Prostate MRI Using ITK Thin Plate Splines, Academic Radiology, vol.16, issue.3, pp.351-357, 2009.
DOI : 10.1016/j.acra.2008.09.011

N. Chrisochoides, A. Fedorov, A. Kot, N. Archip, P. Black et al., Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing, ACM/IEEE SC 2006 Conference (SC'06), p.37, 2006.
DOI : 10.1109/SC.2006.65

URL : https://hal.archives-ouvertes.fr/inria-00615612

D. W. Cool, J. Bax, C. Romagnoli, A. D. Ward, L. Gardi et al., Fusion of MRI to 3D TRUS for Mechanically-Assisted Targeted Prostate Biopsy: System Design and Initial Clinical Experience, Proc. of MICCAI Workshop on Prostate Cancer Imaging, pp.121-133, 2011.
DOI : 10.1148/radiol.2343040363

V. Daanen, J. Gastaldo, J. Y. Giraud, P. Fourneret, J. L. Descotes et al., MRI/TRUS data fusion for brachytherapy, The International Journal of Medical Robotics and Computer Assisted Surgery, vol.18, issue.3, pp.256-261, 2006.
DOI : 10.1002/rcs.95

URL : https://hal.archives-ouvertes.fr/hal-00195006

C. Davatzikos, Spatial transformation and registration of brain images, 1997.

S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Freixenet et al., Prostate Segmentation with Texture Enhanced Active Appearance Model, 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems, pp.18-22, 2010.
DOI : 10.1109/SITIS.2010.14

S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Freixenet et al., A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance, 2011 18th IEEE International Conference on Image Processing, pp.725-728, 2011.
DOI : 10.1109/ICIP.2011.6116653

URL : https://hal.archives-ouvertes.fr/hal-00629085

W. S. Gosset, The probable error of a mean, Biometrika, vol.6, pp.1-25, 1908.

Y. Hu, H. U. Ahmed, Z. Taylor, C. Allem, M. Emberton et al., MR to ultrasound registration for imageguided prostate interventions, Medical Image Analysis, 2011.

D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, Comparing images using the Hausdorff distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.9, pp.850-863, 1993.
DOI : 10.1109/34.232073

R. Jonker and A. Volgenant, Ein Algorithmus mit k??rzesten alternierenden Wegen f??r dichte und d??nne Zuordnungsprobleme, Computing, vol.1, issue.4, pp.325-340, 1987.
DOI : 10.1007/BF02278710

S. Kadoury, P. Yan, S. Xu, N. Glossop, P. Choyke et al., Realtime TRUS/MRI fusion targetedbiopsy for prostate cancer: A clinical demonstration of increased positive biopsy rates, Proc. of MICCAI Workshop on Prostate Cancer Imaging, pp.52-62, 2010.

I. Kaplan, N. E. Oldenburg, P. Meskell, M. Blake, P. Church et al., Real time MRI-ultrasound image guided stereotactic prostate biopsy, Magnetic Resonance Imaging, vol.20, issue.3, pp.295-299, 2002.
DOI : 10.1016/S0730-725X(02)00490-3

V. V. Karnik, A. Fenster, J. Bax, D. W. Cool, L. Gardi et al., Assessment of image registration accuracy in three-dimensional transrectal ultrasound guided prostate biopsy, Medical Physics, vol.175, issue.1, pp.802-813, 2010.
DOI : 10.1118/1.3298010

H. J. De-koning, A. Auvinen, and A. B. Sanchez, Large-scale randomized prostate cancer screening trials: Program performances in the European randomized screening for prostate cancer trial and the prostate, lung, colorectal and ovary cancer trial, International Journal of Cancer, vol.90, issue.2, pp.237-244, 2002.
DOI : 10.1002/ijc.1588

D. J. Kroon, B-spline grid, image and point based registration, 2008.

W. H. Kruskal and W. A. Wallis, Use of Ranks in One-Criterion Variance Analysis, Journal of the American Statistical Association, vol.3, issue.260, pp.583-621, 1952.
DOI : 10.1080/01621459.1952.10483441

H. Levene, Robust tests for equality of variances. Contributions to Probability and Statistics, 1960.

H. W. Lilliefors, On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown, Journal of the American Statistical Association, vol.35, issue.318, pp.399-402, 1967.
DOI : 10.1214/aoms/1177728726

J. Lu, R. Srikanchana, M. Mcclain, Y. Wang, J. Xuan et al., A statistical volumetric model for characterization and visualization of prostate cancer, Proc. of SPIE, pp.142-153, 2000.

D. W. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.434-441, 1963.
DOI : 10.1137/0111030

C. R. Maurer, J. M. Fitzpatrick, M. Y. Wang, S. Member, R. L. Galloway et al., Registration of head volume images using implantable fiducial markers, IEEE Transactions on Medical Imaging, vol.16, issue.4, pp.447-462, 1997.
DOI : 10.1109/42.611354

C. R. Maurer, J. J. Mccrory, and J. M. Fitzpatrick, Estimation of accuracy in localizing externally attached markers in multimodal volume head images, Proceedings of SPIE Medical Imaging 1898, pp.43-54, 1993.

J. Mitra, R. Martí, A. Oliver, X. Lladó, S. Ghose et al., Prostate multimodality image registration based on B-splines and quadrature local energy, International Journal of Computer Assisted Radiology and Surgery, vol.13, issue.5, pp.11548-11559, 2011.
DOI : 10.1007/s11548-011-0635-8

J. Mitra, A. Oliver, R. Martí, X. Lladó, J. C. Vilanova et al., Multimodal Prostate Registration Using Thin-Plate Splines from Automatic Correspondences, 2010 International Conference on Digital Image Computing: Techniques and Applications, pp.587-592, 2010.
DOI : 10.1109/DICTA.2010.104

S. Oguro, J. Tokuda, H. Elhawary, S. Haker, R. Kikinis et al., MRI signal intensity based B-Spline nonrigid registration for pre- and intraoperative imaging during prostate brachytherapy, Journal of Magnetic Resonance Imaging, vol.47, issue.Pt 1, pp.1052-1058, 2009.
DOI : 10.1002/jmri.21955

S. Ourselin, A. Roche, S. Prima, and N. Ayache, Block matching: A general framework to improve robustness of rigid registration of medical images, in: Medical Image Computing and Computer-Assisted Intervention, of Lecture Notes in Computer Science, pp.373-373, 1935.

C. Papadimitriou and K. Stieglitz, Combinatorial Optimization:Algorithms and Complexity, 1982.

C. R. Porter, C. O-'donnell, E. D. Crawford, E. J. Gamito, J. Kim et al., Predicting the Outcome of the Random Prostate Biopsy, 2010.

C. Reynier, J. Troccaz, P. Fourneret, A. Dusserre, C. Gay-jeune et al., MRI/TRUS data fusion for prostate brachytherapy. Preliminary results, Medical Physics, vol.23, issue.5, pp.1568-1575, 2004.
DOI : 10.1118/1.1739003

URL : https://hal.archives-ouvertes.fr/hal-00206282

K. Rohr, H. S. Stiehl, R. Sprengel, T. M. Buzug, J. Weese et al., Landmark-based elastic registration using approximating thin-plate splines, IEEE Transactions on Medical Imaging, vol.20, issue.6, pp.526-534, 2001.
DOI : 10.1109/42.929618

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.586.259

M. J. Roobol and F. H. Schroder, European Randomized Study of Screening for Prostate Cancer: achievements and presentation, BJU International, vol.95, issue.19, pp.117-122, 2003.
DOI : 10.1023/A:1023402706666

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach et al., Nonrigid registration using free-form deformations: application to breast MR images, IEEE Transactions on Medical Imaging, vol.18, issue.8, pp.712-721, 1999.
DOI : 10.1109/42.796284

D. G. Shen, E. Herskovits, and C. Davatzikos, An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures, IEEE Transactions on Medical Imaging, vol.20, issue.4, pp.257-271, 2001.
DOI : 10.1109/42.921475

A. K. Singh, J. Kruecker, S. Xu, N. Glossop, P. Guion et al., Initial clinical experience with real-time transrectal ultrasonography-magnetic resonance imaging fusion-guided prostate biopsy, BJU International, vol.8752, issue.7, pp.841-845, 2008.
DOI : 10.1016/j.eururo.2006.03.007

R. R. Sokal and F. J. Rohlf, Biometry: The principles and practice of statistics in biological research, 1995.

R. Szeliski and S. Lavalle, Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines, International Journal of Computer Vision, vol.49, issue.4, pp.171-196, 1996.
DOI : 10.1007/BF00055001

J. Veltman, T. Goossen, P. Laguna, H. Wijkstra, and J. De-la-rosette, New Technical Improvements for TRUS in the Diagnosis of Prostate Cancer, European Urology Supplements, vol.1, issue.6, 2002.
DOI : 10.1016/S1569-9056(02)00063-5

J. C. Vilanova, C. Barceló-vidal, J. Comet, M. Boada, J. Barceló et al., Usefulness of Prebiopsy Multifunctional and Morphologic MRI Combined With Free-to-Total Prostate-Specific Antigen Ratio in the Detection of Prostate Cancer, American Journal of Roentgenology, vol.196, issue.6, pp.715-722, 2011.
DOI : 10.2214/AJR.10.5700

S. Vishwanath, B. N. Bloch, M. Rosen, J. Chappelow, R. Toth et al., Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol In Vivo 3 Tesla MRI, Proceedings of SPIE 7260, pp.72603-72604, 2009.

B. L. Welch, The generalization of 'student´sstudent´s' problem when several different population varlances are involved, Biometrika, vol.34, pp.28-35, 1947.

G. Xiao, B. Bloch, J. Chappelow, E. Genega, N. Rofsky et al., A structural-functional MRI-based disease atlas:application to computer-aided-diagnosis of prostate cancer, Proc. of SPIE Medical Imaging:Image Processing, Feb, pp.7623031-7623043, 2010.

S. Xu, J. Kruecker, B. Turkbey, N. Glossop, A. K. Singh et al., Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies, Computer Aided Surgery, vol.13, issue.5, pp.255-264, 2008.
DOI : 10.1007/978-3-540-75759-7_4

J. H. Zar, Biostatistical analysis, 1999.

Y. Zhu, S. Williams, and R. Zwiggelaar, Computer technology in detection and staging of prostate carcinoma: A review, Medical Image Analysis, vol.10, issue.2, pp.178-199, 2006.
DOI : 10.1016/j.media.2005.06.003