Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

TLDR: A computer-aided diagnosis system to analyze breast tissues in mammograms, which performs two main tasks: breast tissue classification within a region of interest (ROI; mass or normal) and breast density classification and a simple and robust local descriptor called ULDP is proposed.

Abstract: We propose a simple and robust local descriptor of breast tissues in mammograms called ULDP.ULDP is evaluated in the task of mass/normal breast tissue classification.ULDP is evaluated in the task of breast tissue density classification.The results are comparable to the state-of-the-art methods on two databases. This paper proposes a computer-aided diagnosis system to analyze breast tissues in mammograms, which performs two main tasks: breast tissue classification within a region of interest (ROI; mass or normal) and breast density classification. The proposed system consists of three steps: segmentation of the ROI, feature extraction and classification. Although many feature extraction methods have been used to characterize breast tissues, the literature shows no consensus on the optimal feature set for breast tissue characterization. Specifically, mass detection on dense breast tissues is still a challenge. In the feature extraction step, we propose a simple and robust local descriptor for breast tissues in mammograms, called uniform local directional pattern (ULDP). This descriptor can discriminate between different tissues in mammograms, yielding a significant improvement in the analysis of breast cancer. Classifiers based on support vector machines show a performance comparable to the state-of-the-art methods.