Mammography is at present most popular and available method for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to quickly and accurately overcome the development of breast cancer, which affects more and more women through the world. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the mammogram image. In this paper, some simple segmentation processes have been develop to make a supporting tool to easy and less time consuming method of identification abnormal masses in mammography images. The identification technique is divided into four distinct parts i.e. preprocessing, selection, isolation and projection. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc are clearly sited after proposed method is executed on raw mammogram for easy and early detection of abnormality. The outcomes of the results are satisfactory and acceptable.

1. Ball JE. Digital mammogram spiculated mass detection and spicule segmentation using level sets. Proceedings of the 29th Annual International Conference of the IEEE EMBS. 2007: 4979-84.
2. Bovis K, Singh S. Detection of masses in mammograms using texture features. 15th International Conference on Pattern Recognition. 2000: 267-70.
3. Breast Cancer Facts & Figures, 2009-2010, American Cancer Society, Inc.
4. FDA Web site, http://www.accessdata.fda.gov/scripts/ cdrh/cfdocs/ cfMQSA/mqsa.cfm [last visited on September 30, 2009].
5. R. Highnam and M. Brady, Mammographic Image Analysis, Kluwer Academic Publishers, 1999. ISBN: 0-7923- 5620-9.
6. Dr. H. B. Kekre, Tanuja K. Sarode and Saylee M. Gharge, "Tumor Detection in Mammography Images using Vector Quantization Technique", International Journal of Intelligent Information Technology Application, 2009, 2(5):237-242.
7. Michaelson J, Satija S, Moore R, et al. The pattern of breast cancer screening utilization and its consequences. Cancer. Jan 1 2002; 94(1): 37-43.
8. A Mohd. Khuzi1, R Besar, WMD Wan Zaki, NN Ahmad1,"Identification of masses in digital mammogram using gray level co-occurrence matrices", Biomedical Imaging and Intervention Journal, http://www.biij.org/2009/3/e17.
9. National Cancer Institute (NCI) Web site, http://www.cancernet.gov [last visited on September 30, 2009].
10. RadiologyInfo.org developed jointly by Radiological Society of North America and American College of Radiology.
11. E. E. Sterns, “Relation between clinical and mammographic diagnosis of breast problems and the cancer/ biopsy rate,” Can. J. Surg., vol. 39, n°. 2, pp. 128-132, 1996.
12. J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.
13. Yang SC, Lin YJ, Hsu GC et al. Mass screening and features reserved compression in a computer-aided system for mammograms. IEEE World Congress on Computational Intelligence. 2008: 3779-86.

Breast Cancer Mammogram Masses GLCM Contrast Homogeneity Energy