A. Almansa, A. Desolneux, and S. Vamech, Vanishing point detection without any a priori information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.4, p.502507, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00170785

J. Bonwein and J. Vanderwee, Convex functions : constructions, characterizations and counterexamples, 2010.

J. Bornefalk, Use of quadrature lters for detection of stellate lesions in mammograms, p.649658, 2005.

F. Cao, Good continuations in digital image level lines, Proceedings Ninth IEEE International Conference on Computer Vision, p.440448, 2003.
DOI : 10.1109/ICCV.2003.1238380

F. Cao, Application of the Gestalt principles to the detection of good continuations and corners in image level lines, Computing and Visualization in Science, vol.21, issue.1, p.313, 2004.
DOI : 10.1007/s00791-004-0123-6

F. Cao, P. Musé, and F. Sur, Extracting meaningful curves from images, Journal of Mathematical Imaging and Vision, vol.22, issue.2, p.159181, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00104264

V. Caselles, B. Coll, and J. , Junction detection and ltering : A morphological approach, Image Processing Proceedings., International Conference on, p.493496, 1996.

J. Delon, A. Desolneux, J. Lisani, and A. Petro, A Nonparametric Approach for Histogram Segmentation, IEEE Transactions on Image Processing, vol.16, issue.1, p.253261, 2007.
DOI : 10.1109/TIP.2006.884951

A. Desolneux, When the a contrario approach becomes generative, International Journal of Computer Vision, vol.27, issue.2
DOI : 10.1007/s11263-015-0825-x

A. Desolneux and F. Doré, Un cadre a contrario anisotrope pour la détection de convergences de structures linéaires dans les images, Actes du GRETSI

A. Desolneux and F. Doré, An anisotropic framework for the detection of points of convergences in images : application to the detection of stellate lesions in mammograms

A. Desolneux, S. Ladjal, L. Moisan, and J. , Dequantizing image orientation, IEEE Transactions on Image Processing, vol.11, issue.10, p.11291140, 2002.
DOI : 10.1109/TIP.2002.804566
URL : https://hal.archives-ouvertes.fr/hal-00170791

A. Desolneux, L. Moisan, and J. , Edge detection by helmholtz principle, Journal of Mathematical Imaging and Vision, vol.25, issue.4, p.271284, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00170793

A. Desolneux, L. Moisan, and J. , A grouping principle and four applications. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.25, issue.4, p.508513, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00170783

A. Desolneux, L. Moisan, and J. , Maximal meaningful events and applications to image analysis. The Annals of Statistics, p.3118221851, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00170777

A. Desolneux, L. Moisan, and J. M. , From Gestalt theory to image analysis : a probabilistic approach, 2007.
DOI : 10.1007/978-0-387-74378-3
URL : https://hal.archives-ouvertes.fr/hal-00259077

M. Dimiccoli and P. Salembier, Exploiting T-junctions for depth segregation in single images, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, p.12291232, 2009.
DOI : 10.1109/ICASSP.2009.4959812

A. R. Dominguez and A. K. Nandi, Toward breast cancer diagnosis based on automated segmentation of masses in mammograms, Pattern Recognition, vol.42, issue.6, p.11381148, 2009.

M. A. Fischler and R. Bolles, Random sample consensus : a paradigm for model tting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, p.381395, 1981.

R. Grompone-von-gioi, J. Jakubowicz, J. Morel, and G. Randall, Lsd : A fast line segment detector with a false detection control. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.4, p.722732, 2010.

B. Grosjean and L. Moisan, A-contrario Detectability of Spots in??Textured Backgrounds, Journal of Mathematical Imaging and Vision, vol.68, issue.2, p.313337, 2009.
DOI : 10.1007/s10851-008-0111-4
URL : https://hal.archives-ouvertes.fr/hal-00534713

B. Grosjean, S. Muller, and H. Souchay, Lesion detection using an a-contrario detector in simulated digital mammograms, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 2006.
DOI : 10.1117/12.654140

B. Grosjean, S. Muller, and H. Souchay, Tools and methods for exposure control optimization in digital mammography in presence of texture, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, 2007.
DOI : 10.1117/12.709797

T. Makita, T. Matsubara, H. Fujita, Y. Inenaga, T. Endo et al., Automated detection method for architectural distortion with spiculation based on distribution assessment of mammary gland on mammogram, International Workshop on Digital Mammography (IWDM), p.370375, 2006.

J. Huang and D. Mumford, Statistics of natural images and models, Computer Vision and Pattern Recognition, 1999.

T. Ichikawa, T. Matsubara, T. Hara, H. Fujita, T. Endo et al., Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis, Medical Imaging 2004: Image Processing, p.921, 2004.
DOI : 10.1117/12.535116

N. Karssemeijer and G. Te-brake, Detection of stellate distortions in mammograms, IEEE Transactions on Medical Imaging, vol.15, issue.5, p.611619, 1996.
DOI : 10.1109/42.538938

J. Pruneda, P. Bourland, and W. Kegelmeyer, Computer-aided mammographic screening for spiculated lesions, Radiology, vol.191, p.331337, 1994.

H. Kim and W. Kim, Automatic Detection of Spiculated Masses Using Fractal Analysis in Digital Mammography, Computer Analysis of Images and Patterns (CAIP), p.256263, 2005.
DOI : 10.1007/11556121_32

S. K. Kinoshita, P. M. Azevedo-marques, R. R. Pereira, J. A. Rodrigues, and R. M. Rangayyan, Radon-Domain Detection of the Nipple and the Pectoral Muscle in Mammograms, Journal of Digital Imaging, vol.18, issue.5, p.3749, 2008.
DOI : 10.1007/s10278-007-9035-6

A. Lee, K. Pedersen, and D. Mumford, The nonlinear statistics of highcontrast patches in natural images, International Journal of Computer Vision, vol.54, issue.1, p.83103, 2003.

S. Liu, C. Babbs, and E. Delph, Multiresolution detection of spiculated lesions in digital mammograms, IEEE Transactions on Medical Imaging, vol.10, issue.6, pp.874-884, 2001.

Q. Luong and O. Faugeras, The fundamental matrix: Theory, algorithms, and stability analysis, International Journal of Computer Vision, vol.11, issue.5, p.4375, 1996.
DOI : 10.1007/BF00127818

T. Matsubara, D. Fukuoka, N. Yagi, T. Hara, H. Fujita et al., Detection method for architectural distortion based on analysis of structure of mammary gland on mammograms, International Congress Series, p.10361040, 2005.
DOI : 10.1016/j.ics.2005.03.324

T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai et al., Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms, International Congress Series, p.950955, 2003.
DOI : 10.1016/S0531-5131(03)00496-5

A. Mendez, P. Tahoces, M. J. Lado, M. Souto, J. Correa et al., Automatic detection of breast border and nipple in digital mammograms. Computer methods and programs in biomedicine, p.253262, 1996.

L. Moisan and B. Stival, A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix, International Journal of Computer Vision, vol.57, issue.3, p.201218, 2004.
DOI : 10.1023/B:VISI.0000013094.38752.54
URL : https://hal.archives-ouvertes.fr/hal-00171323

M. Nemoto, S. Honmura, A. Shimizu, D. Furukawa, H. Kobatake et al., A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows, International Journal of Computer Assisted Radiology and Surgery, vol.2001, issue.2, p.2736, 2009.
DOI : 10.1007/s11548-008-0267-9

S. L. Ng and W. F. Bischof, Automated detection and classication of breast tumors, Computers and Biomedical Research, vol.25, p.218237, 1992.

G. Palma, Détection automatique des opacités en tomosynthèse numérique du sein, 2010.

G. Palma, I. Bloch, and S. Muller, Spiculated Lesions and Architectural Distortions Detection in Digital Breast Tomosynthesis Datasets, International Workshop on Digital Mammography (IWDM), 2010.
DOI : 10.1007/978-3-642-13666-5_96

G. Palma, S. Muller, I. Bloch, and R. Iordache, Fast detection of convergence areas in Digital Breast Tomosynthesis, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, p.847850, 2009.
DOI : 10.1109/ISBI.2009.5193185

G. Peters, S. Muller, and B. Grosjean, A hybrid active contour model for mass detection in digital breast tomosynthesis, Medical Imaging 2007: Computer-Aided Diagnosis, 2007.
DOI : 10.1117/12.709593

R. Rangayyan and F. J. Ayres, Gabor lters and phase portraits for the detection of architectural distortion in mammograms, Medical and Biological Engineering and Computing, vol.44, issue.10, p.883894, 2006.

D. Ruderman, Origins of scaling in natural images, Vision Research, vol.37, issue.23, p.33853398, 1997.
DOI : 10.1016/S0042-6989(97)00008-4

D. Ruderman and W. Bialek, Statistics of natural images: Scaling in the woods, Physical Review Letters, vol.73, issue.6, p.814817, 1994.
DOI : 10.1103/PhysRevLett.73.814

J. Salvi and X. Armangue, A survey addressing the fundamental matrix estimation problem, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), p.209212, 2001.
DOI : 10.1109/ICIP.2001.958461

M. P. Sampat and A. Bovik, Detection of spiculated lesions in mammograms, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), p.810813, 2003.
DOI : 10.1109/IEMBS.2003.1279888

L. Antonio and S. , Integral Geometry and Geometric Probability, 2004.

G. S. Xia, J. Delon, and Y. Gousseau, Accurate Junction Detection and Characterization in Natural Images, International Journal of Computer Vision, vol.13, issue.9, p.3156, 2014.
DOI : 10.1007/s11263-013-0640-1
URL : https://hal.archives-ouvertes.fr/hal-00631609

R. Zwiggelaar, T. C. Parr, J. E. Schumm, I. W. Hutt, C. J. Taylor et al., Model-based detection of spiculated lesions in mammograms, Medical Image Analysis, vol.3, issue.1, p.3962, 1999.
DOI : 10.1016/S1361-8415(99)80016-4