False Positive Reduction in Breast Mass Detection Using the Fusion of Texture and Gradient Orientation Features

Abstract

The presence of masses in mammograms is among the main indicators of breast cancer and their diagnosis is a challenging task. The one problem of Computer aided diagnosis (CAD) systems developed to assist radiologists in detecting masses is high false positive rate i.e. normal breast tissues are detected as masses. This problem can be reduced if localised texture and gradient orientation patterns in suspicious Regions Of Interest (ROIs) are captured in a robust way. Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) are among the state-of-the-art best texture descriptors whereas Histogram of Oriented Gradient (HOG) is one of the best descriptor for gradient orientation patterns. To capture the discriminative micro-patterns existing in ROIs, we propose localised DRLBP-HOG and DRLTP-HOG descriptors by fusing DRLBP, DRLTP and HOG for the description of ROIs; the localisation is archived by dividing each ROI into a number of blocks (sub-images). Support Vector Machine (SVM) is used to classify mass or normal ROIs. The evaluation on DDSM, a benchmark mammograms database, revealed that localised DRLBP-HOG with 9 (3$\times$3) blocks forms the best representation and yields an accuracy of 99.80±0.62(ACC±STD) outperforming the state-of-the-art methods.