A new method for breast micro-calcification detection and characterization using digital temporal subtraction of mammogram pairs
Source2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI)
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Breast cancer is one of the most deadly malignancies worldwide and the second leading cause of death in women. Mammography, i.e. the screening for breast cancer with x-ray imaging, has significantly improved the prognosis of patients diagnosed with the disease. The evaluation of mammograms requires a panel of radiologists, but even well trained experts can err in their assessment. For this reason, Computer-Aided Detection (CAD) systems are becoming more prevalent. In this paper, we introduce a novel approach for breast Micro-Calcification (MC) diagnosis using temporal sequences of digital mammograms. The goal is to increase the MC detection accuracy by subtracting prior images. A new dataset, with precise marking of MC locations, was created specifically for this study. The proposed approach began with temporal subtraction of mammograms, after demon-based registration, which effectively removed unchanged regions and MCs (17.3% reduction in the number of MCs). The second step was the classification of the MCs as benign or suspicious using the subtracted images. A set of diverse features were selected for the classification. Four different classifiers were tested with leave-one-patient-out cross-validation. For comparison, the MC classification was also performed, using single mammograms, without temporal subtraction. The average accuracy of the classification of the MCs as benign or suspicious was 91.3% without and 99.2% with temporal subtraction using Support Vector Machines (statistically significant p=0.026). These results show that temporal subtraction could be a valuable addition to CAD systems to assist radiologists in effectively detecting breast MCs.