Automatic Segmentation of Digital Mammograms to Detect Masses
Mohsen A. M. El‐Bendary, Heba Abdellatif, M. El-Tokgy, Tarek Taha, El‐Sayed M. El‐Rabaie, Osama F. Zahran, Waleed Al-Nauimy, Saleh Ud-din Ahmad, F. E. Abd El-Samie · CiiT international journal of digital image processing · 2014
Mammography is well known method for detection of breast tumors. Early detection and removal of the primary tumor is an essential and effective method to enhance survival rate and reduce mortality. Breast tumor segmentation is needed for monitoring and quantifying breast cancer. However, automated tumor segmentation in mammograms poses many challenges with regard to characteristics of an image. In this paper we propose a fully automatic algorithm for segmentation of a breast masses, using two types of image segmentation, Normalized graph cuts to delineate pectoral muscle and optimal threshold based on the two-dimensional entropy for masses detection.