Investigation of methods for the computerized detection and analysis of mammographic masses

At present, mammography is the most effective method for the early detection of breast cancer. Detection and classification of masses in mammograms are among the most important and difficult tasks performed by radiologists. Various studies have indicated that regular mammographic screening can reduce the mortality from breast cancer in women. Thus, mammography may become one of the largest volume x-ray procedures routinely interpreted by radiologists. The miss rate for the radiographic detection of malignant masses ranges from 12 to 30 percent. In addition, although general rules exist for the visual differentiation of benign and malignant masses, error does occur in the classification of masses with the current methods of radiologic characterization. Thus, it is apparent that the efficiency and effectiveness of screening procedures could be increased by use of a computer system that successfully aids the radiologist in detecting and characterizing mammographic masses. We are developing computerized schemes for the automated detection and classification of masses in digital mammograms. The detection scheme utilizes the architectural symmetry of the left and right breasts and digital bilateral-subtraction techniques in order to increase the conspicuity of the mammographic mass prior to the application of feature-extraction techniques. The classification scheme involves the extraction of border information from the mammographic mass in order to quantify the degree of spiculation, which is related to the likelihood of malignancy.

Duke Scholars

Author Fang-Fang Yin Radiation Oncology