A Multi view CADx System for the mammography images is implemented. Two types of systems are widely used in mammography. They are Computer-aided detection (CADe) and Computer-aided diagnosis (CADx). The different views of mammography images MLO (Mediolateral Oblique) and CC (Crani-caudal) are assessed. Segmentation will be implemented for the images obtained from the two views for extracting the mass contour. A set of features related to the geometry of the boundary and the structure inside it will be computed for both of the images. An optimal subset of similar features will be extracted. Using the ranked features extracted the classification will be implemented using SVM. A Monte – Carlo method owing to the iterative and complex structure of the algorithms is used. The validation of the results is based on confidence intervals for given coverage probabilities and performance metrics.

1. Arianna Mencattini, Marcello Salmeri., "Metrological Characterization of a CADx System for the Classification of Breast Masses in Mammograms", IEEE Transactions on Instrumentation and Measurement, Vol.59, No.11.Nov. 2010.
2. Arianna Mencattini, Marcello Salmeri, "Assessment of a breast mass identification procedure using an Iris detector", IEEE Transactions on Instrumentation and Measurement, Vol.59, No.10, Oct.2010.
3. Arianna Mencattini, Marcello Salmeri, et. al. "Uncertainty propagation for the assessment of tumoral masses segmentation", AMUEM 2009–International Workshop, July 2009.
4. Arianna Mencattini, Marcello Salmeri, et. al. "Uncertainty modeling and propagation through RFVs for the assessment of CADx systems in Digital mammography", IEEE Transactions on Instrumentation and Measurement, Vol.59, No.1, Jan.2010.
5. El-Sanosi M.D., Habbani.A.K., et. al. "Computer Aided Detection of Benign Tumors of the Female Breast", Proceedings of the 2008 IEEE, CIBEC'08.
6. Hala Al-Shamlan, Ali El-Zaart, "Feature Extraction values for Breast cancer Mammography Images", 2010 International Conference on Bioinformatics and Biomedical Technology.
7. Ioanna Christoyianni, Evalgelos Dermatas, et. al, "Fast detection of masses in Computer-Aided Mammography", IEEE Signal Processing Magazie, Jan.2000.
8. Jinshan Tang, Rangaraj M.Rangayyan, et. al. "Computer Aided Detection and Diagnosis of Breast Cancer With Mammography:Recent Advances", IEEE Transactions on Biomedicine, Vol.13, No.2, March 2009.
9. Maurice Samulski, Nico Karssemeijer., "Optimizing Case-Based Detection Performance in a Multiview CAD System for Mammography" IEEE Transactions on Medical Imaging, Vol.30, No.4. Apr. 2011.
10. Nalini Singh, Ambarish G.Mohapatra, "Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering", International Journal of Computer Applications, Vol.22, No.2, May 2011.
11. Sukhwinder Singh, Vinod Kumar, "SVM Based System for Classification of Microcalcifications in Digital Mammograms",EMBS Annual International Conference, Sep. 2006.
12. H. Kobatake and M. Murakami, "Adaptive filter to detect rounded convex regions: Iris filter," in Proc. IEEE Int. Conf. Pattern Recog., 1996, vol. 2, pp. 340–344.
13. University of South Florida Digital Mammography Home Page, Univ. South Florida, 2000. [Online] Available: http://marathon.csee.usf.edu/Mammography/Database.html

Monte-carlo Method Computer-aided Detection Support Vector Machine