Unsupervised Breast Masses Classification through Optimum-Path Forest

Abstract

Computer-Aided Diagnosis (CAD) can be divided into two main categories: CADe (Computer-Aided Detection), which is focused on the detection of structures of interest, as well as to assist radiologists to find out signals of interest that might be hidden to human vision, and the CADx (Computer-Aided Diagnosis), which works as a second observer, being responsible to give an opinion on a specific lesion. In CADe - based systems, the identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest. The main contribution of this study is to introduce the unsupervised classifier Optimum-Path Forest to identify breast masses, and to evaluate its performance against with two other unsupervised techniques (Gaussian Mixture Model and k-Means) using texture features from images obtained from a private dataset composed by 120 images with and without the presence of masses.