R. A. Smith, American Cancer Society Guidelines for Breast Cancer Screening: Update 2003, CA: A Cancer Journal for Clinicians, vol.53, issue.3, pp.141-169, 2003.
DOI : 10.3322/canjclin.53.3.141

A. T. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker et al., Solid breast nodules: use of sonography to distinguish between benign and malignant lesions., Radiology, vol.196, issue.1, pp.123-157, 1995.
DOI : 10.1148/radiology.196.1.7784555

S. Ciatto, M. Rosselli-del-turco, S. Catarzi, and D. Morrone, The contribution of ultrasonography to the differential diagnosis of breast cancer, Neoplasma, vol.41, issue.6, p.341, 1994.

Y. Yuan, M. L. Giger, H. Li, N. Bhooshan, and C. A. Sennett, Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI, Academic Radiology, vol.17, issue.9, p.1158, 2010.
DOI : 10.1016/j.acra.2010.04.015

D. Cremers, M. Rousson, and R. Deriche, A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape, International Journal of Computer Vision, vol.18, issue.9, 2007.
DOI : 10.1007/s11263-006-8711-1

R. Achanta, SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, 2012.
DOI : 10.1109/TPAMI.2012.120

J. Massich, Deformable object segmentation in ultra-sound images, 2013.
URL : https://hal.archives-ouvertes.fr/tel-01150950

J. Massich, SIFT Texture Description for Understanding Breast Ultrasound Images, Breast Imaging, pp.681-688, 2014.
DOI : 10.1007/978-3-319-07887-8_94

R. Szeliski, A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.6, pp.1068-1080, 2008.
DOI : 10.1109/TPAMI.2007.70844

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.23, issue.11, pp.1222-1239, 2001.

A. Delong, A. Osokin, H. N. Isack, and Y. Boykov, Fast Approximate Energy Minimization with Label Costs, International Journal of Computer Vision, vol.18, issue.9, pp.1-27, 2012.
DOI : 10.1007/s11263-011-0437-z

G. Pons, J. Martí, R. Martí, S. Ganau, J. Vilanova et al., Evaluating lesion segmentation in breast ultrasound images related to lesion typology, Journal of Ultrasound in Medicine, 2013.

B. Liu, H. D. Cheng, J. Huang, J. Tian, X. Tang et al., Probability density difference-based active contour for ultrasound image segmentation, Pattern Recognition, vol.43, issue.6, 2010.
DOI : 10.1016/j.patcog.2010.01.002

L. Gao, X. Liu, and W. Chen, Phase- and GVF-Based Level Set Segmentation of Ultrasonic Breast Tumors, Journal of Applied Mathematics, vol.64, issue.2, pp.1-22, 2012.
DOI : 10.1016/j.compmedimag.2011.06.007

M. Alemán-flores, L. Alvarez, and V. Caselles, Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation, Journal of Mathematical Imaging and Vision, vol.11, issue.11, pp.81-97, 2007.
DOI : 10.1007/s10851-007-0015-8

Q. Huang, S. Lee, L. Liu, M. Lu, L. Jin et al., A robust graph-based segmentation method for breast tumors in ultrasound images, Ultrasonics, vol.52, issue.2, pp.266-275, 2012.
DOI : 10.1016/j.ultras.2011.08.011

A. Madabhushi and D. Metaxas, Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions, IEEE Transactions on Medical Imaging, vol.22, issue.2, 2003.
DOI : 10.1109/TMI.2002.808364

Z. Hao, Q. Wang, Y. K. Seong, J. Lee, H. Ren et al., Combining CRF and Multi-hypothesis Detection for Accurate Lesion Segmentation in Breast Sonograms, Medical Image Computing and Computer- Assisted Intervention?MICCAI 2012, pp.504-511, 2012.
DOI : 10.1007/978-3-642-33415-3_62

J. Zhang, S. K. Zhou, S. Brunke, C. Lowery, and D. Comaniciu, Database-guided breast tumor detection and segmentation in 2D ultrasound images, Medical Imaging 2010: Computer-Aided Diagnosis, 2010.
DOI : 10.1117/12.844558

G. Xiao, M. Brady, J. A. Noble, and Y. Zhang, Segmentation of ultrasound B-mode images with intensity inhomogeneity correction, IEEE Transactions on Medical Imaging, vol.21, issue.1, pp.48-57, 2002.
DOI : 10.1109/42.981233

J. Massich, F. Meriaudeau, E. Pérez, R. Martí, A. Oliver et al., Lesion Segmentation in Breast Sonography, Digital Mammography, pp.39-45, 2010.
DOI : 10.1007/978-3-642-13666-5_6

J. Shan, H. D. Cheng, and Y. Wang, Completely Automated Segmentation Approach for Breast Ultrasound Images Using Multiple-Domain Features, Ultrasound in Medicine & Biology, vol.38, issue.2, pp.262-275, 2012.
DOI : 10.1016/j.ultrasmedbio.2011.10.022

C. Yeh, Y. Chen, W. Fan, and Y. Liao, A disk expansion segmentation method for ultrasonic breast lesions, Pattern Recognition, vol.42, issue.5, 2009.
DOI : 10.1016/j.patcog.2008.09.004

K. Horsch and . Other, Automatic segmentation of breast lesions on ultrasound, Medical Physics, vol.22, issue.8, 2001.
DOI : 10.1118/1.1386426

W. Gómez, Computerized lesion segmentation of breast ultrasound based on marker-controlled watershed transformation, Medical Physics, vol.17, issue.4, p.82, 2010.
DOI : 10.1118/1.3265959

Y. Huang and D. Chen, Automatic contouring for breast tumors in 2-D sonography, Engineering in Medicine and Biology Society, pp.3225-3228, 2005.

Y. Huang, Y. Jiang, D. Chen, and W. K. Moon, Level Set Contouring for Breast Tumor in Sonography, Journal of Digital Imaging, vol.8, issue.3, pp.238-247, 2007.
DOI : 10.1007/s10278-006-1041-6

J. Cui, B. Sahiner, H. Chan, and A. Nees, A new automated method for the segmentation and characterization of breast masses on ultrasound images, Medical Physics, vol.33, issue.5, p.1553, 2009.
DOI : 10.1118/1.2207129