Predicting Breast Density of Digital Breast Tomosynthesis from 2D Mammograms

Breast density may be used as a predictor of breast cancer risk and can measure the condition of tissues on mammograms. This research developed a computer-aided diagnosis (CAD) system to predict breast density on digital breast tomosynthesis (DBT) images. We used two-dimensional (2D) mammograms to train the linear discriminant analysis (LDA) classifier. Then load the DBT projection image to predict breast density. Experimental results show that LDA is better than other classification methods, such as one rule, naive Bayes, decision tree and support vector machine. The breast density prediction accuracy of DBT is 80%.