A. Cancer-society-atlanta, Prostate Cancer. www.cancer.org, accessed on, 2011.

S. Badiei, S. E. Salcudean, J. Varah, and W. J. Morris, Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fitting, Medical Image Computing and Computer-Assisted Intervention -MICCAI, pp.17-24, 2006.
DOI : 10.1007/11866763_3

N. Betrouni, M. Vermandel, D. Pasquier, S. Maouche, and J. Rousseau, Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter, Computerized Medical Imaging and Graphics, vol.29, issue.1, pp.43-51, 2005.
DOI : 10.1016/j.compmedimag.2004.07.007

T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, Use of active shape models for locating structures in medical images, Image and Vision Computing, vol.12, issue.6, pp.355-366, 1994.
DOI : 10.1016/0262-8856(94)90060-4

T. Cootes, G. Edwards, and C. Taylor, Active Appearance Models, Proceedings of European Conference on Computer Vision, pp.484-498, 1998.

F. A. Cosío, Automatic initialization of an active shape model of the prostate, Medical Image Analysis, vol.12, issue.4, pp.469-483, 2008.
DOI : 10.1016/j.media.2008.02.001

K. Diaz and B. Castaneda, Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach, Medical Imaging 2008: Image Processing, pp.1-8, 2008.
DOI : 10.1117/12.770965

E. Geremia, B. H. Menze, O. Clatz, E. Konukoglu, A. Criminisi et al., Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images, In: MICCAI, vol.6361, pp.111-118, 2010.
DOI : 10.1007/978-3-642-15705-9_14

S. Ghose, A. Oliver, R. Martí, X. Lladó, J. Freixenet et al., Statistical shape and texture model of quadrature phase information for prostate segmentation, International Journal of Computer Assisted Radiology and Surgery, vol.25, issue.1, pp.43-55, 2012.
DOI : 10.1007/s11548-011-0616-y
URL : https://hal.archives-ouvertes.fr/hal-00612739

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.713-716, 2011.
DOI : 10.1109/ICIP.2011.6116653
URL : https://hal.archives-ouvertes.fr/hal-00629085

J. C. Gower, Generalized procrustes analysis, Psychometrika, vol.35, issue.1, pp.33-51, 1975.
DOI : 10.1007/BF02291478

H. M. Ladak, F. Mao, Y. Wang, D. B. Downey, D. A. Steinman et al., Prostate Segmentation from 2D Ultrasound Images, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3188-3191, 2000.
DOI : 10.1109/iembs.2000.901572

W. Li, S. Liao, Q. Feng, W. Chen, and D. Shen, Learning image context for segmentation of prostate in ct-guided radiotherapy, In: MICCAI, vol.6893, pp.570-578, 2011.

H. Liu, G. Cheng, D. Rubens, J. G. Strang, L. Liao et al., Automatic segmentation of prostate boundaries in transrectal ultrasound (TRUS) imaging, Medical Imaging 2002: Image Processing, pp.412-423, 2002.
DOI : 10.1117/12.467183

D. Shen, Y. Zhan, and C. Davatzikos, Segmentation of prostate boundaries from ultrasound images using statistical shape model, IEEE Transactions on Medical Imaging, vol.22, issue.4, pp.539-551, 2003.
DOI : 10.1109/TMI.2003.809057

P. Yan, S. Xu, B. Turkbey, and J. Kruecker, Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation, IEEE Transactions on Biomedical Engineering, vol.57, pp.1158-1166, 2010.

Y. Zhan and D. Shen, Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method, IEEE Transactions on Medical Imaging, vol.25, issue.3, pp.256-272, 2006.
DOI : 10.1109/TMI.2005.862744