Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms.

In this paper, a new and novel approach is designed for extracting local binary pattern (LBP) texture features from the computer-identified mass regions, aiming to reduce false-positive (FP) detection in a computerized mass detection framework. The proposed texture feature, the so-called multiresolution LBP feature, is well able to characterize the regional texture patterns of core and margin regions of a mass, as well as to preserve the spatial structure information of the mass. In addition, to maximize a complementary effect on improving classification accuracy, multiresolution texture analysis has been incorporated into the extraction of LBP features. Further, SVM-RFE-based variable selection strategy is applied for selecting an optimal subset of variables of multiresolution LBP texture features to maximize the separation between breast masses and normal tissues. Extensive and comparative experiments have been conducted to evaluate the proposed method on two public benchmark mammogram databases (DBs). Experimental results show that the proposed multiresolution LBP features (extracted from automatically segmented mass boundaries) outperform other state-of-the-art texture features developed for FP reduction. Our results also indicate that combining our multiresolution LBP features with variable selection strategy is an effective solution for reducing FP signals in computer-aided detection (CAD) of mammographic masses.