Differentiating between Benign and Malignant non-Mass Enhancing Lesions in Breast DCE-MRI by Using Curvelet-based Textural Features

Breast DCE-MR imaging plays an important role in effective detection and diagnosis of breast cancer. Non-mass enhancing breast lesions have been less studied in CADx systems because of their challenging intrinsic. In this study, CADx system is proposed for differentiating benign and malignant nonmass enhancing lesions in breast DCE-MRI. Proposed system uses dynamic information of the 4D DCE-MRI data to segment the lesions on the basis of fuzzy clustering algorithm. Curvelet-based textural features are extracted from 3D segmented lesions and classified by SVM classifier. The results achieved the accuracy of 75% and AUC of 0.75 for non-mass enhancing breast lesions which provides comparable results to other recent methods.