Digital Imaging in Mammography towards Detection and Analysis of Human Breast Cancer

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.