Automatic Characterization of Mammograms using Fractal Texture Analysis and Fast Correlation Based Filter Method
Shradhananda Beura, Banshidhar Majhi, Ratnakar Dash · 2015
This paper presents an effective scheme to identify the abnormal mammograms in order to detect the breast cancer. The scheme utilizes the segmentation-based fractal texture analysis (SFTA) method to extract the textural features from the mammograms for the classification of normal and abnormal mammograms. A fractal analysis has been applied to collect the qualitative information of textural features. A fast correlation-based filter (FCBF) method has been used to select feature subsets containing significant features, which are used for classification purpose. The scheme was tested on the mammogram images of MIAS database. In this paper, support vector machine (SVM) has been utilized for classification of mammograms. Simulation results show an optimal classification performance index as the area under the curve (AUC) of 0.9831 in the ROC analysis.