X. Salvador, Missed breast carcinoma : Pitfalls and pearls 1, Radiographics, vol.23, issue.4, pp.881-895, 2003.

J. Erlay, . Ervik, . Dikshit, . Eser, and . Mathers, Cancer incidence and mortality worldwide : Iarc cancerbase no

J. Stinès, Imagerie du sein

I. Saarenmaa, T. Salminen, U. Geiger, P. Heikkinen, S. Hyvärinen et al., The effect of age and density of the breast on the sensitivity of breast cancer diagnostic by mammography and ultasonography, Breast Cancer Research and Treatment, vol.39, issue.2, pp.117-123, 2001.
DOI : 10.1080/02841860050215954

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

C. Chang, L. Justo, . Sibala, L. Steven, S. J. Fritz et al., Specific Value of Computed Tomographic Breast Scanner (CT/M) in Diagnosis of Breast Diseases, Radiology, vol.132, issue.3, pp.647-652, 1979.
DOI : 10.1148/132.3.647

C. Chang, E. Dennis, . Nesbit, . Fisher, . Fritz et al., Computed tomographic mammography using a conventional body scanner, American Journal of Roentgenology, vol.138, issue.3, pp.553-558, 1982.
DOI : 10.2214/ajr.138.3.553
URL : http://www.ajronline.org/doi/pdf/10.2214/ajr.138.3.553

A. College and . Radiology, Acr bi-rads?mammography, ultrasound & magnetic resonance imaging, Amer. coll. Radiol, p.108, 2003.

H. Cheng, R. Shi, . Min, . Hu, H. Cai et al., Approaches for automated detection and classification of masses in mammograms, Pattern Recognition, vol.39, issue.4, pp.646-668, 2006.
DOI : 10.1016/j.patcog.2005.07.006

I. Cheikhrouhou-epse and K. , Description et classification des masses mammaires pour le diagnostic du cancer du sein

. Thesis, Uiversity of Evry Val d'Essone : France, 2012.

G. Susan, N. Orel, C. Kay, . Reynolds, C. Daniel et al., Bi-rads categorization as a predictor of malignancy 1, Radiology, vol.211, issue.3, pp.845-850, 1999.

A. Jay, . Baker, L. Eric, . Rosen, Y. Joseph et al., Computer-aided detection (cad) in screening mammography : sensitivity of commercial cad systems for detecting architectural distortion, American Journal of Roentgenology, vol.181, issue.4, pp.1083-1088, 2003.

N. John and . Wolfe, Breast patterns as an index of risk for developing breast cancer

A. Stephen and . Feig, Decreased breast cancer mortality through mammographic screening : results of clinical trials, Radiology, vol.167, issue.3, pp.659-665, 1988.

D. C. Hope, E. Munday, and S. L. Smith, Evolutionary Algorithms in the Classification of Mammograms, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp.258-265, 2007.
DOI : 10.1109/CIISP.2007.369178

P. Sajda, C. Spence, and J. Pearson, Learning contextual relationships in mammograms using a hierarchical pyramid neural network, IEEE Transactions on Medical Imaging, vol.21, issue.3, pp.239-250, 2002.
DOI : 10.1109/42.996342
URL : http://liinc.bme.columbia.edu/publications/00996342.pdf

P. Mehul, . Sampat, K. Mia, A. C. Markey, and . Bovik, Computer-aided detection and diagnosis in mammography. Handbook of image and video processing, pp.1195-1217, 2005.

J. Suckling, . Parker, . Dance, . Astley, C. Hutt et al., The mammographic image analysis society digital mammogram database, Exerpta Medica. International Congress Series, pp.375-378, 1994.

M. Heath, K. Bowyer, D. Kopans, R. Moore, and P. Kegelmeyer, The digital database for screening mammography, Proceedings of the 5th international workshop on digital mammography, pp.212-218, 2000.

B. Kurt, V. Vasif, K. Nabiyev, and . Turhan, A novel automatic suspicious mass regions identification using havrda & charvat entropy and otsu's n thresholding . Computer methods and programs in biomedicine, pp.349-360, 2014.
DOI : 10.1016/j.cmpb.2014.02.014

T. Berber, A. Alpkocak, P. Balci, and O. Dicle, Breast mass contour segmentation algorithm in digital mammograms. Computer methods and programs in biomedicine, pp.150-159, 2013.
DOI : 10.1016/j.cmpb.2012.11.003

J. Anitha, J. Peter, S. , and A. Pandian, A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms, Computer Methods and Programs in Biomedicine, vol.138, pp.93-104, 2017.
DOI : 10.1016/j.cmpb.2016.10.026

A. Lotfi and . Zadeh, Fuzzy sets, Information and control, vol.8, issue.3, pp.338-353, 1965.

H. Alharbi, P. Kwan, and A. Sajeev, A comparative study of fuzzy thresholding techniques for mass detection in digital mammography, Proceedings of the 27th Conference on Image and Vision Computing New Zealand, IVCNZ '12, pp.330-334
DOI : 10.1145/2425836.2425901

A. Oliver, J. Freixenet, J. Marti, E. Pérez, J. Pont et al., A review of automatic mass detection and segmentation in mammographic images, Medical Image Analysis, vol.14, issue.2, pp.87-110, 2010.
DOI : 10.1016/j.media.2009.12.005

X. Ramió-torrentà and . Lladó, Marga : multispectral adaptive region growing algorithm for brain extraction on axial mri. Computer methods and programs in biomedicine, pp.655-673, 2014.

P. Arjun, . Monisha, G. Mullaiyarasi, and . Kavitha, Analysis of the liver in CT images using an improved region growing technique, 2015 International Conference on Industrial Instrumentation and Control (ICIC), pp.1561-1566, 2015.
DOI : 10.1109/IIC.2015.7150998

S. Charutha and M. Jayashree, An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp.1193-1199, 2014.
DOI : 10.1109/ICCICCT.2014.6993142

B. Senthilkumar, J. Umamaheswari, and . Karthik, A novel region growing segmentation algorithm for the detection of breast cancer, 2010 IEEE International Conference on Computational Intelligence and Computing Research, pp.1-4, 2010.
DOI : 10.1109/ICCIC.2010.5705725

A. Melouah, A Novel Region Growing Segmentation Algorithm for Mass Extraction in Mammograms, Modeling Approaches and Algorithms for Advanced Computer Applications, pp.95-104, 2013.
DOI : 10.1007/978-3-319-00560-7_14

D. Guliato, M. Rangaraj, . Rangayyan, A. Walter, J. Carnielli et al., Segmentation of breast tumors in mammograms by fuzzy region growing, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), pp.1002-1005, 1998.
DOI : 10.1109/IEMBS.1998.745618

M. Rangaraj, . Rangayyan, M. Nema, L. El-faramawy, and . Desautels, Measures of acutance and shape for classification of breast tumors, IEEE Transactions on medical imaging, vol.16, issue.6, pp.799-810, 1997.

S. Xu, H. Liu, and E. Song, Marker-Controlled Watershed for Lesion Segmentation in Mammograms, Journal of Digital Imaging, vol.35, issue.5, pp.754-763, 2011.
DOI : 10.1118/1.2820630
URL : http://europepmc.org/articles/pmc3180548?pdf=render

J. Anitha and J. Peter, Mass segmentation in mammograms using a kernel-based fuzzy level set method, International Journal of Biomedical Engineering and Technology, vol.19, issue.2, pp.133-153, 2015.
DOI : 10.1504/IJBET.2015.072933

Q. Huang, F. Yang, L. Liu, and X. Li, Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis, Information Sciences, vol.314, pp.293-310, 2015.
DOI : 10.1016/j.ins.2014.08.021

H. Kuo, L. Maryellen, I. Giger, . Reiser, M. John et al., Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria, Journal of Digital Imaging, vol.8670, issue.2, pp.237-247, 2014.
DOI : 10.1117/12.2008014

G. Michiel, M. Kallenberg, C. H. Lokate, N. Van-gils, and . Karssemeijer, Automatic breast density segmentation : an integration of different approaches, Physics in Medicine and Biology, vol.56, issue.9, pp.2715-2729, 2011.

M. Mustra, M. Grgic, and K. Delac, Feature selection for automatic breast density classification, Proc. ELMAR-2010, pp.9-16, 2010.

M. Mary and . Galloway, Texture analysis using gray level run lengths. Computer graphics and image processing, pp.172-179, 1975.

L. Houam, ContributionàContributionà l'analyse de textures de radiographies osseuses pour le diagnostic précoce de l'ostéoporose, 08 mai 45 Guelma (Algérie), 2013.

R. Nithya and B. Santhi, Computer Aided Diagnosis System for Mammogram Analysis: A Survey, Journal of Medical Imaging and Health Informatics, vol.5, issue.4, pp.653-674, 2015.
DOI : 10.1166/jmihi.2015.1441

A. Kumar-mohanty, S. Manas-ranjan-senapati, S. Beberta, and L. Kumar, Texture-based features for classification of mammograms using decision tree, Neural Computing and Applications, pp.3-41011, 2013.

J. Kim, J. M. Park, K. S. Song, and H. W. Park, Detection of clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network, The Journal of VLSI Signal Processing, vol.18, issue.3, pp.251-262, 1998.
DOI : 10.1023/A:1007989231205

O. Augereau, Reconnaissance et classification d'images de documents, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00821889

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp.281-297, 1967.

Y. Li, H. Chen, X. Wei, Y. Peng, and L. Cheng, Mass classification in mammograms based on two-concentric masks and discriminating texton, Pattern Recognition, vol.60, pp.648-656, 2016.
DOI : 10.1016/j.patcog.2016.06.021

H. John and . Holland, Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence, 1992.

B. Verma and P. Zhang, A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms, Applied Soft Computing, vol.7, issue.2, pp.612-625, 2007.
DOI : 10.1016/j.asoc.2005.02.008

P. Zhang, B. Verma, and K. Kumar, Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection, Pattern Recognition Letters, vol.26, issue.7, pp.909-919, 2005.
DOI : 10.1016/j.patrec.2004.09.053

M. Karnan and K. Thangavel, Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. Computer methods and programs in biomedicine, pp.12-20, 2007.

C. Castella, K. Kinkel, F. Descombes, P. Miguel, P. Eckstein et al., Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm, Optics Express, vol.16, issue.11, pp.7595-7607, 2008.
DOI : 10.1364/OE.16.007595.m001

J. Sublemontier, Classification non supervisée : de la multiplicité des donnéesdonnéesà la multiplicité des analyses, 2012.

X. Liu, J. Liu, D. Zhou, and J. Tang, A Benign and Malignant Mass Classification Algorithm Based on an Improved Level Set Segmentation and Texture Feature Analysis, 2010 4th International Conference on Bioinformatics and Biomedical Engineering, pp.1-4, 2010.
DOI : 10.1109/ICBBE.2010.5518284

H. Mahersia, H. Boulehmi, and K. Hamrouni, Development of intelligent systems based on bayesian regularization network and neuro-fuzzy models for mass detection in mammograms : A comparative analysis. Computer methods and programs in biomedicine, pp.46-62, 2016.

Y. Zhang, N. Tomuro, J. Furst, and D. S. Raicu, Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms, MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, pp.69-76, 2009.
DOI : 10.1016/S0004-3702(97)00043-X
URL : http://facweb.cs.depaul.edu/research/vc/publications/papers/bi-rads.pdf

K. Ganesan, U. Rajendra, . Acharya, K. Chua, . Chua et al., Decision support system for breast cancer detection using mammograms, Proceedings of the Institution of Mechanical Engineers, pp.721-732, 2013.
DOI : 10.1109/42.887618

M. Sidney, . Lima, G. Abel, W. Da-silva-filho, . Pinheiro et al., Detection and classification of masses in mammographic images in a multi-kernel approach. computer methods and programs in biomedicine, pp.11-29, 2016.

K. Ganesan, C. Acharya, . Kuang-chua, T. Lim-choo-min, K. Abraham et al., Computer-Aided Breast Cancer Detection Using Mammograms: A Review, IEEE Reviews in Biomedical Engineering, vol.6, pp.77-98, 2013.
DOI : 10.1109/RBME.2012.2232289

S. Shanthi and V. Bhaskaran, Intuitionistic fuzzy c-means and decision tree approach for breast cancer detection and classification, European Journal of Scientific Research, vol.66, issue.3, pp.345-351, 2011.

M. Dong, X. Lu, Y. Ma, Y. Guo, Y. Ma et al., An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms, Journal of Digital Imaging, vol.16, issue.8, pp.613-625, 2015.
DOI : 10.1093/bioinformatics/btg419
URL : http://europepmc.org/articles/pmc4570896?pdf=render

L. Li, W. Qian, P. Laurence, . Clarke, A. Robert et al., <title>Improving mass detection by adaptive and multiscale processing in digitized mammograms</title>, Medical Imaging 1999: Image Processing, pp.490-498, 1999.
DOI : 10.1117/12.348604

M. Rangaraj, . Rangayyan, M. Thanh, . Nguyen, J. Fábio et al., Effect of pixel resolution on texture features of breast masses in mammograms, Journal of digital imaging, vol.23, issue.5, pp.547-553, 2010.

S. Krishnaveni, T. Bhanumathi, and . Pugazharasan, Study of mammogram microcalcification to aid tumour detection using naive bayes classifier, International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, vol.3, issue.3, p.2014

K. Kavitha and N. Kumaravel, A Comparitive Study of Various MicroCalcification Cluster Detection Methods in Digitized Mammograms, 2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, pp.405-409, 2007.
DOI : 10.1109/IWSSIP.2007.4381127

E. Mazen, . Osman, . Wahed, S. Ahmed, . Mohamed et al., Computer aided diagnosis system for classification of microcalcifications in digital mammograms, Radio Science Conference, pp.1-6, 2009.

N. Anna, . Karahaliou, S. Ioannis, . Boniatis, G. Spyros et al., Breast cancer diagnosis : analyzing texture of tissue surrounding microcalcifications, IEEE transactions on information technology in biomedicine, vol.12, issue.6, pp.731-738, 2008.

A. Mohamed, . Alolfe, A. Wael, . Mohamed, M. Abo-bakr et al., Feature selection in computer aided diagnostic system for microcalcification detection in digital mammograms, Radio Science Conference, pp.1-9, 2009.

A. Karahaliou, . Skiadopoulos, . Boniatis, . Sakellaropoulos, . Likaki et al., Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. The British journal of radiology, 2014.
DOI : 10.1259/bjr/30415751

E. Michael, . Mavroforakis, V. Harris, N. Georgiou, and . Dimitropoulos, Dionisis Cavouras , and Sergios Theodoridis. Mammographic masses characterization based 114

J. Kharroubi, Etude de techniques de classement " MachinesàMachinesà vecteurs supports " pour la vérification automatique du locuteur, 2002.

E. M. Wener-borges-sampaio, A. Diniz, A. Silva, M. Cardoso-de-paiva, and . Gattass, Detection of masses in mammogram images using CNN, geostatistic functions and SVM, Computers in Biology and Medicine, vol.41, issue.8, pp.41653-664, 2011.
DOI : 10.1016/j.compbiomed.2011.05.017

U. Raghavendra, H. Acharya, A. Fujita, J. H. Gudigar, S. Tan et al., Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images, Applied Soft Computing, vol.46, pp.151-161, 2016.
DOI : 10.1016/j.asoc.2016.04.036

J. De, N. Silva, A. Oseas-de-carvalho-filho, A. Silva, A. Cardoso-de-paiva et al., Automatic detection of masses in mammograms using quality threshold clustering, correlogram function, and svm, Journal of digital imaging, vol.28, issue.3, p.323, 2015.

B. Solaiman, <title>Information fusion concepts: from information elements definition to the application of fusion approaches</title>, Sensor Fusion: Architectures, Algorithms, and Applications V, pp.205-212, 2001.
DOI : 10.1117/12.421108

M. Homam and A. Sun, Indexation guidée par les connaissances en imagerie médicale, 2012.

P. A. and C. Aguilar, RéseauxRéseaux´RéseauxÉvidentiels pour la fusion de données multimodales hétérogènes : ApplicationàApplicationà la détection de chutes, 2012.

. Bassem-alsahwa-bretagne, Représentation d'un environnement par un système multicapteurs : fusion et interprétation de scène, Télécom, 2014.

S. Kharbouche, Fonctions de Croyance et Indexation Multimodale Application ` a l'Identification de Personnes dans des Albums, 2006.
URL : https://hal.archives-ouvertes.fr/tel-00232806

C. De and R. , Imperfection, temps et espace : modélisation, analyse et visualisation dans un SIG archéologique, 2008.

W. Eziddin, Segmentation itérative d'images par propagation de connaissances dans le domaine possibiliste : applicationàapplicationà la détection de tumeurs en imagerie mammographique, 2012.

I. Bloch and H. Ma??trema??tre, Fusion de données en traitement d'images : modèles d'information et décisions, TS. Traitement du signal, vol.11, issue.6, pp.435-446, 1994.

P. Walley, Measures of uncertainty in expert systems, Artificial Intelligence, vol.83, issue.1, pp.1-58, 1996.
DOI : 10.1016/0004-3702(95)00009-7
URL : https://doi.org/10.1016/0004-3702(95)00009-7

P. Arthur and . Dempster, Upper and lower probabilities induced by a multivalued mapping. The annals of mathematical statistics, pp.325-339, 1967.

G. Shafer, A mathematical theory of evidence, 1976.

W. Eziddin, J. Montagner, and B. Solaiman, An iterative possibilistic image segmentation system: Application to breast cancer detection, 2010 13th International Conference on Information Fusion, pp.1-8, 2010.
DOI : 10.1109/ICIF.2010.5712098

J. Zhao, K. Bimal, and . Bose, Evaluation of membership functions for fuzzy logic controlled induction motor drive, IECON 02 [Industrial Electronics Society, pp.229-234, 2002.

. Heng-da, J. Cheng, and . Chen, Automatically determine the membership function based on the maximum entropy principle, Information Sciences, vol.96, issue.3-4, pp.163-182, 1997.

G. Nieradka and B. Butkiewicz, A Method for Automatic Membership Function Estimation Based on Fuzzy Measures, International Fuzzy Systems Association World Congress, pp.451-460, 2007.
DOI : 10.1007/978-3-540-72950-1_45

. Lotfi-asker and . Zadeh, Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, pp.3-28, 1978.

D. Dudois, H. Prade, and D. Dubois, Possibility theory : an approach to computerized processing of uncertainty, 1988.

B. Bouchon-meunier, Uncertainty management in medical applications, Nonlinear Biomedical Signal Processing, vol.1, pp.1-26, 2000.
URL : https://hal.archives-ouvertes.fr/hal-01573420

I. Bloch, Fusion d'informations en traitement du signal et des images, ser. Traitement du signal et de l'image, 2003.

I. Bloch, Information combination operators for data fusion: a comparative review with classification, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.26, issue.1, pp.52-67, 1996.
DOI : 10.1109/3468.477860

C. James, R. Bezdek, W. Ehrlich, and . Full, Fcm : The fuzzy c-means clustering algorithm, Computers & Geosciences, vol.10, issue.2-3, pp.191-203, 1984.

R. Krishnapuram, M. James, and . Keller, The possibilistic C-means algorithm: insights and recommendations, IEEE Transactions on Fuzzy Systems, vol.4, issue.3, pp.385-393, 1996.
DOI : 10.1109/91.531779

J. Fasquel and M. Bruynooghe, New hybrid opto-electronic method for fast and unsupervised object detection, Optical Engineering, vol.42, issue.11, pp.3352-3364, 2003.

S. Hirano and S. Tsumoto, Rough representation of a region of interest in medical images, International Journal of Approximate Reasoning, vol.40, issue.1-2, pp.23-34, 2005.
DOI : 10.1016/j.ijar.2004.11.008

S. Huang, F. Cheng, and Y. Chiu, Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution, IEEE Transactions on Image Processing, vol.22, issue.3, pp.1032-1041, 2013.
DOI : 10.1109/TIP.2012.2226047

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.285-29623, 1975.
DOI : 10.1109/TSMC.1979.4310076

S. Osher, A. James, and . Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations, Journal of Computational Physics, vol.79, issue.1, pp.12-49, 1988.
DOI : 10.1016/0021-9991(88)90002-2
URL : http://www.ann.jussieu.fr/~frey/papers/levelsets/Osher S., Fronts propagating with curvature dependent speed.pdf

C. Li, C. Xu, C. Gui, D. Martin, and . Fox, Level set evolution without re-initialization : a new variational formulation, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, pp.430-436, 2005.

N. Paragios, Y. Chen, D. Olivier, and . Faugeras, Handbook of mathematical models in computer vision, 2006.
DOI : 10.1007/0-387-28831-7

J. Robert and . Walker, Algebraic curves, 1978.

A. Montanvert and J. M. Chassery, Géométrie discrète en analyse d'images, 1991.

F. Tony, . Chan, A. Luminita, and . Vese, Active contours without edges, IEEE Transactions on image processing, vol.10, issue.2, pp.266-277, 2001.

X. Hao, Y. Shen, and S. Xia, Automatic mass segmentation on mammograms combining random walks and active contour, Journal of Zhejiang University SCIENCE C, vol.13, issue.9, pp.635-648, 2012.
DOI : 10.1631/jzus.C1200052

Y. Wang, D. Tao, X. Gao, X. Li, and B. Wang, Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions, Pattern Recognition, vol.44, issue.9, pp.1903-1915, 2011.
DOI : 10.1016/j.patcog.2010.08.002

Y. Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition, vol.29, issue.8, pp.1335-1346, 1996.
DOI : 10.1016/0031-3203(95)00169-7

P. Liao, T. Chen, and P. Chung, A fast algorithm for multilevel thresholding, J. Inf. Sci. Eng, vol.17, issue.5, pp.713-727, 2001.

A. William, . Yasnoff, K. Jack, J. W. Mui, and . Bacus, Error measures for scene segmentation, Pattern recognition, vol.9, issue.4, pp.217-231, 1977.

J. Zhang, Y. Wang, and C. Zhang, Ultrasonographic Segmentation of Cervical Lymph Nodes Based on Graph Cut with Elliptical Shape Prior, 13th International Conference on Biomedical Engineering, pp.125-128, 2009.
DOI : 10.1007/978-3-540-92841-6_31

B. Sahiner, N. Petrick, H. Chan, M. Lubomir, C. Hadjiiski et al., Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization, IEEE Transactions on Medical Imaging, vol.20, issue.12, pp.1275-1284, 2001.
DOI : 10.1109/42.974922

V. Chalana and Y. Kim, A methodology for evaluation of boundary detection algorithms on medical images, IEEE Transactions on Medical Imaging, vol.16, issue.5, pp.642-652, 1997.
DOI : 10.1109/42.640755
URL : http://www.cs.rpi.edu/research/groups/vision/ken/method_for_eval.pdf

J. Anitha and J. Peter, Mammogram segmentation using maximal cell strength updation in cellular automata, Medical & Biological Engineering & Computing, vol.10, issue.7, pp.737-749, 2015.
DOI : 10.1109/83.931096

M. Yang, K. Kpalma, and J. Ronsin, A survey of shape feature extraction techniques, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00446037

D. Zhang and G. Lu, Review of shape representation and description techniques, Pattern Recognition, vol.37, issue.1, pp.1-19, 2004.
DOI : 10.1016/j.patcog.2003.07.008
URL : http://www.gscit.monash.edu.au/~dengs/resource/papers/PR_ShapeReview.pdf

L. Shen, M. Rangaraj, L. Rangayyan, and . Desautels, DETECTION AND CLASSIFICATION OF MAMMOGRAPHIC CALCIFICATIONS, International Journal of Pattern Recognition and Artificial Intelligence, vol.07, issue.06, pp.1403-1416, 1993.
DOI : 10.1142/S0218001493000686

J. Kilday, F. Palmieri, D. Martin, and . Fox, Classifying mammographic lesions using computerized image analysis, IEEE Transactions on Medical Imaging, vol.12, issue.4, pp.664-669, 1993.
DOI : 10.1109/42.251116

D. Dubois, L. Foulloy, G. Mauris, and H. Prade, Probabilitypossibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable computing, pp.273-297, 2004.
DOI : 10.1023/b:reom.0000032115.22510.b5
URL : http://www.irit.fr/~Henri.Prade/Papers/DFoulMauP.pdf

D. Dubois and H. Prade, Unfair coins and necessity measures : towards a possibilistic interpretation of histograms. Fuzzy sets and systems, pp.15-20, 1983.
DOI : 10.1016/s0165-0114(83)80099-2

J. George and . Klir, A principle of uncertainty and information invariance, International Journal Of General System, vol.17, issue.2-3, pp.249-275, 1990.

K. Yamada, Probability-possibility transformation based on evidence theory, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)
DOI : 10.1109/NAFIPS.2001.944229

J. Bezdek, Pattern recognition with fuzzy objective algorithms plenum press new york google scholar, 1981.
DOI : 10.1007/978-1-4757-0450-1

. Ahmed-nasreddine-benaichouche, Conception de métaheuristiques d'optimisation pour la segmentation d'images : application aux images IRM du cerveau et aux images de tomographie parémissionparémission de positons, 2014.

R. Duda, 0. and hart, pe (1973) pattern classification and scene analysis
URL : https://hal.archives-ouvertes.fr/jpa-00228580

M. James, . Keller, R. Michael, . Gray, A. James et al., A fuzzy k-nearest neighbor algorithm, IEEE transactions on systems, man, and cybernetics, issue.4, pp.580-585, 1985.

S. Kikuchi and V. Perincherry, Handling uncertainty in large scale systems with certainty and integrity, 2004.

F. Saki, A. Tahmasbi, H. Soltanian-zadeh, B. Shahriar, and . Shokouhi, Fast opposite weight learning rules with application in breast cancer diagnosis. Computers in biology and medicine, pp.32-41, 2013.
DOI : 10.1016/j.compbiomed.2012.10.006

I. Buciu and A. Gacsadi, Directional features for automatic tumor classification of mammogram images, Biomedical Signal Processing and Control, vol.6, issue.4, pp.370-378, 2011.
DOI : 10.1016/j.bspc.2010.10.003