[1] S. N. Acho and W. I. D. Rae, Dependence of shape-based descriptors and mass segmentation
areas on initial contour placement using the chan-vese method on digital mammograms,
Computational and Mathematical Methods in Medicine, 2015 (2015), 1-16.
[2] American Cancer Society, Cancer facts and figures, Atlanta, Ga: American Cancer Society,
(2013), 1-60.
[3] R. Bellotti, A completely automated CAD system for mass detection in a large mammographic
database, Medical Physics, 33(8) (2006), 3066–3075.
[4] L. Breslo and D. Aha, Simplifying decision trees: a survey, The Knowledge Engineering
Review, 12(1) (1997), 1-40.
[5] L. F. A. Campos, A. C Silva and A. K. Barros, Diagnosis of breast cancer in digital mammograms
using independent component analysis and neural networks, X Iberoamerican, Conference
on Pattern Recognition, Havana, Lecture notes in computer science, 3773 (2005),
460–469.
[6] T. F. Chan and L. A. Vese, Active contours without edges, IEEE Trans. Image Process, 10(2)
(2001), 266-277.
[7] R. Crandall, Image segmentation using Chan Vese algorithm, ECE532 Project fall, 2009.
[8] A. Keles and Y. Ugur,Expert system based on neuro-fuzzy rules for diagnosis breast cancer,
Expert Syst Appl., 38(5)(2011), 5719–5726.
[9] U. Khan, H. Shin, J. P. Choi and M. Kim, Weighted fuzzy decision trees for prognosis of
breast cancer survivability, Proc of the Australasian Data Mining Conferenre, Glenelg, South
Australia, 7(3) (2008), 141-152.
[10] Z. Lei and K. Ardrew Chan, An artificial intelligent algorithm for tumor detection in screening
mammogram, IEEE Trans. on Medical Imaging, 20(7) (2009), 559-567.
[11] M. Leonardo de Oliveira, G. Braz Junior, C. S. .Aristofanes, A. Cardoso de Paiva and M.
Gattass, Detection of masses in digital mammograms using K-means and support vector
machine, Electronic Letters on Computer Vision and Image Analysis, 8(2) (2009), 39-50.
[12] A. M.Maciej. Y.J.Lo, P.B.Harrawood and D. G Tourassc, Mutual information-based template
matching scheme for detection of breast masses: From mammography to digital breast
tomosynthesis, Journal of Biomedical Informatics 44(5) (2011), 815-823.
[13] C. Marsala, Apprentissage inductif en pr´esence de donn´ees impr´ecises : construction et
utilisation d’arbres de d´ecision flous, Th`ese de doctorat, Universit´e de Paris 6, (1988).
[14] C. Marsala, Fuzzy decision trees to help flexible querying, KYBERNETICA, 36 (2006), 689–
705.
[15] L. Martins, A. Dos Santos, A. Silva and A. Paiva,Classification of normal, benign and malignant
tissues using co-occurrence matrix and bayesian neural network in mammographic
images, Proceedings of the Ninth Brazilian Symposium on Neural Networks, (2006), 479–486.
[16] A. Materka and M. Strzelecki, Texture analysis methods, A review, COST B11 Technical
Report, Lodz-Brussels: Technical University of Lodz, (1998), 9-11.
[17] G. H. B. Miranda and J. C Felipe,Computer-aided diagnosis system based on fuzzy logic for
breast cancer categorization, Computers in Biology and Medicine, 64(1) (2015), 334-34.
[18] J. I. Mohamed, M. Ahmadi and A. S. A. Maher, An efficient automatic mass classification
method in digitized mammograms using artificial neural network, International Journal of
Artificial Intelligence and Application (IJAIA), 1(3) (2010), 1-13.
[19] E. Molins, F. Macia and F. Ferrer, Association between radiologists’ experience and accuracy
in interpreting screening mammograms, BMC Health Serv Res., 8(91) (2008), 1-10.
[20] S. K. Murthy, Automatic construction of decision trees from data: a multi-disciplinary survey,
Data Min Knowl Disc, 2(4) (1998), 345-389.
[21] C. Olaru anf L. Wehenkel, A complete fuzzy decision tree technique, Fuzzy Sets and Systems,
138(2) (2003), 221-254.
[22] A. Oliver, J. Freixenet, R Mart´ı et al., A novel breast tissue density classification methodology,
IEEE Transactions on Information Technology in Biomedicine, 12(1) (2008), 55–65.
[23] S. Osher and N. Paragios, Geometric level set methods in imaging, vision and Graphics,
Springer-Verlag, 2003.
[24] G. Palma, G. Peters, S. Muller and I. Bloch, Masses classification using fuzzy active contours
and fuzzy decision tree, SPIE Symposium on Medical Imaging, San Diego, CA, USA,
6915(2008), 691509.1-691509.11.
[25] o. Pitchumani Angayarkanni and N. Banu Kamal, Association rule mining based decision tree
induction for efficient detection of cancerous masses in mammogram, International Journal
of Computer Applications, 31(6) (2011), 1-5.
[26] P. Rahmati, A. Adler and G. Hamarneh, Mammography segmentation with maximum likelihood
active contours, Medical Image Analysis, 16(6) (2012), 1167–1186.
[27] R. Ramani and N. Suthanthira Vanitha, Computer aided detection of tumours in mammograms,
international .Journal of Image, Graphics and Signal Processing, 6(4) (2014), 54-59.
[28] M. Ramdani, Syst`eme d’induction formelle `a base de connaissances impr´ecises, Th`ese de
doctorat, Paris 6, LIP6, 1994.
[29] R. S. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE
Transactions on Systems, Man, and Cybernetics, 3(21) (1991), 660–674.
[30] G. Saborta, Probabilit´es, Analyse des donn´ees et Statistique, Ed. Technip, 1990.
[31] M. S. Salve and A. Chakkarwar, Classification of mammographic images using Gabor Wavelet
and discrete wavelet transform, International Journal of Advanced Research in Electronics
and Communication Engineering (IJARECE), 2(5) (2013).
[32] G. Serge and B. Charnomordic, Generating an interpretable family Of fuzzy partitions, IEEE
Transactions on Fuzzy Systems, 12(3) (2004), 324– 335.
[33] G. Serge, Induction de r`egles floues interpr´etables, Th`ese de Doctorat, INSA Toulouse,
France, 2001.
[34] S. Shanthi and M. BhaskaraR, Intuistionistic fuzzy C-means and decision tree approach for
breast cancer detection and classification, European Journal of Scientific research, 66(I3)
(2011), 345-351.
[35] J. Suckling, J. Parker, D. R. Dance et al., The mammographic image analysis society digital
mammogram database, Excerpta Medica International Congress Series, (1069) (1994), 375-
378.
[36] T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling
and control, IEEE Trans. Syst. Man Cybern, 15 (1985), 116–132.
[37] H. D. Thanh, Mesures de discrimination et leurs applications en apprentissage inductif,
Th`ese de doctorat, Universit´e de Paris 6, 2007.
[38] X. F. Wang, D. S. Huang and H. Xu, An efficient local Chan–vese model for image segmentation,
Pattern Recognition, 43(3) (2010), 603-618.
[39] Y. Wu, O. Alagoz, M. U. S. Ayvaci, A. Munoz del Rio, A. D. J. Vanness, R. Woods and E. S.
Burnsise, A comprehensive methodology for determining the most informative mammographic
features, Journal of digital imaging, 26(5) (2013), 941-947.
[40] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353.