Digital Mammogram Segmentation and Abnormal Masses Detection System


  • Armen Sahakyan Institute for Informatics and Automation Problems of NAS RA


Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD) systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour allows the search for abnormalities to be limited to the region of the breast. We need to perform essential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present an automated system for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI).


L.-M. Wun, R. M. Merrill, and E. J. Feuer, "Estimating Lifetime and Age-Conditional Probabilitiesof Developing Cancer," Lifetime Data Analysis, vol. 4, pp. 169-186, 1998.

"WHO Cancer Facts,", 2009.

L. Shen, R. Rangayyan, and J. Desaultels, “Detection and Classification MammographicCalcifications”, International Journal of Pattern Recognition and Artificial Intelligence.Singapore: World Scientific, pp. 1403–1416, 1994.

F. Aghdasi, R.Ward, and B. Palcic, “Restoration of mammographic images in the presenceof signal-dependent noise”, in State of the Art in Digital Mammographic Image Analysis.Singapore: World Scientific, vol. 7, pp. 42–63, 1994.

Y. Chitre, A. Dhawan, and M. Moskowtz, “Artificial neural network based classification ofmammographic microcalcifications using image structure features”, in State of the Art ofDigital Mammographic Image Analysis. Singapore: World Scientific, vol. 7, pp. 167–197,1994.

Pisano and F. Shtern,“Image processing and computer-aided diagnosis in digitalmammography,” in State of the Art of Digital Mammographic Image Analysis. Singapore:World Scientific, vol. 7, pp. 280–291, 1994.

A. Sahakyan, H. Sarukhanyan, "Automatic Segmentation of the Breast Region in DigitalMammograms", Computer Science and Information Technologies, Proceedings of theConference, pp. 386 - 389, Yerevan, Armenia, September 26-30, 2011.

Indra Kanta Maitra ; Sanjay Nag and Prof. Samir K. Bandyopadhyay, "Automated DigitalMammogram Segmentation For Detection Of Abnormal Masses Using Binary HomogeneityEnhancement Algorithm", Indian Journal of Computer Science and Engineering, Issue 3, vol. 2,pp. 416-427, 2011.

J. Suckling et al., “The Mammographic Image Analysis Society digital mammogramdatabase”, Exerpta Medica., vol. 1069, pp. 375– 378, 1994.




How to Cite

Sahakyan, A. . (2021). Digital Mammogram Segmentation and Abnormal Masses Detection System. Mathematical Problems of Computer Science, 36, 41–50. Retrieved from