Breast Mass Detection and Classification Algorithm based on Temporal Subtraction of Sequential Mammograms

Breast cancer screening with mammography is the most efficient way to reduce breast cancer mortality. However, the large population and the use of double reading creates a high workload that heavily burdens the efficiency of the radiologists. Hence, Computer-Aided Detection (CAD) systems are being developed to assist the radiologists. In this study, an algorithm for the automatic detection and classification of breast masses, based on temporal subtraction of sequential mammograms, image registration and machine learning, is presented. While, some previous studies in the literature proposed temporal analysis by creating a new feature vector, temporal subtraction takes into consideration the entire prior image. A new dataset, consisting of 40 cases (two time points and two views of each breast per patient, a total of 160 images), with precisely annotated mass locations was created. The accuracy of the classification of masses as benign or suspicious increased from 85.7% (using temporal analysis) to 92.9% (using temporal subtraction). The improvement was statistically significant with p < 0.05. These results demonstrate the effectiveness of temporal subtraction for the diagnosis of breast masses.