Breast Mass Detection and Classification based on Digital Temporal Subtraction of Mammogram Pairs

Breast cancer is one of the deadliest malignancies worldwide. In mammography, the most reliable screening tool for its diagnosis, expert radiologists review the mammograms to determine whether the patient has any signs of disease. Unfortunately, the evaluation of breast abnormalities is challenging, even for experienced radiologists. Computer-Aided Detection (CAD) systems can assist in the detection of breast cancer. In this work, an algorithm for the automatic detection and classification of masses, based on subtraction of sequential digital mammograms, image registration and machine learning, is presented. Previous studies assessed the use of sequential mammograms to perform temporal analysis by creating a new temporal feature vector. Temporal subtraction registers and subtracts the prior mammogram from the current one, prior to performing mass detection and classification. A new dataset, which includes sequential pairs from 40 patients (160 mammograms) with precisely annotated mass locations (benign and suspicious), was created to assess the performance of the algorithm. For the classification, various features were extracted and six classifiers were used in a leave-one-patient-out cross-validation. The accuracy of the classification of masses as benign or suspicious increased from 90.83% (with the previously described temporal analysis) to 96.51% (with temporal subtraction). The improvement was statistically significant with p <; 0.05. These results demonstrate the effectiveness of the proposed technique of temporal subtraction of mammograms for the detection of masses.