Efficient System for Delimitation of Benign and Malignant Breast Masses

In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses’ regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] for benign masses and [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] for malignant ones, while the MID dataset resulted in [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] along with [Formula: see text] , [Formula: see text] , [Formula: see text] , and [Formula: see text] for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.