Automated detection of breast mass based on binary decision tree

Breast cancer remains a leading cause of cancer deaths among women in many parts of the world. It has been reported that early detection of breast cancer in asymptomatic women can reduce breast cancer mortality. Mammography is considered to be the most effective technique for early detection of breast cancer. Mass automatic detection is the first step of computer-aided diagnosis. An algorithm was developed to detect masses in digital mammograms. Mass detection is very difficult because of weak contrast to background and varieties of masses in size, location, and intensity. Preprocessing method, feature extraction and classifier design are key problems. In this paper, we present a binary decision tree based method. After mammograms are enhanced, an adaptive threshold algorithm is applied to segment suspicious regions. Six features, such as area, compactness, circularity, gray variance, gray mean value and deviation, are extracted to represent suspicious region. Binary decision tree is used to classify the suspicious regions to either mass or normal tissue. A set of 50 mammograms were used to verify the performance of this scheme. Results were achieved with a sensitivity of 86% at the 1.18 false positive (FPs)/image. Experimental results demonstrate the effectiveness of the proposed method.