Novel Morphological Features for Non-mass-like Breast Lesion Classification on DCE-MRI

For both visual analysis and computer assisted diagnosis systems in breast MRI reading, the delineation and diagnosis of ductal carcinoma in situ ( DCIS) is among the most challenging tasks. Recent studies show that kinetic features derived from dynamic contrast enhanced MRI (DCE-MRI) are less effective in discriminating malignant non-masses against benign ones due to their similar kinetic characteristics. Adding shape descriptors can improve the differentiation accuracy. In this work, we propose a set of novel morphological features using the sphere packing technique, aiming to discriminate non-masses based on their shapes. The feature extraction, selection and the classification modules are integrated into a computer-aided diagnosis ( CAD) system. The evaluation was performed on a data set of 106 non-masses extracted from 86 patients, which achieved an accuracy of 90.56 %, precision of 90.3%, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.94 for the differentiation of benign and malignant types.