Model-based characterization of mammographic masses

The discrimination of benign and malignant types of mammographic masses is a major challenge for radiologists. The classic Eigenfaces method was recently adapted for the detection of masses in mammograms. In the work at hand we investigate if this method is also suited for the problem of distinguishing benign and malignant types of this mammographic lesion. We furthermore evaluate two extended versions of the Eigenfaces approach (Fisherface and Eigenfeature-regularizationextraction) and compare the performance of all three methods on a public mammography database. Our results indicate that all three methods can be applied to discriminate benign and malignant types of mammographic masses. However, our ROC analysis shows that the methods are not reliable enough for diagnosis, yet.