Robust Texture Features for Breast Density Classification in Mammograms

Abstract

This paper proposes an effective and robust texture feature descriptor to classify mammographic images into different breast density categories. Accurate breast density based categorization of images plays an important role in the risk assessment at early stages of breast cancer. Based on the commonly used local binary patterns (LBP), we investigate its variant method local quinary patterns (LQP) for considering more details of texture features. The rotation invariant approach with different concerned numbers of spatial transitions is used to extend LQP to rotation invariant LQP (RILQP). The proposed method recognizes more texture patterns and reduces the high dimensionality of its feature vector significantly, which make it a robust texture descriptor. In addition, in the breast density classification task, this paper also investigates the influence to classifying results by using resized mammogram images. Two mammogram datasets, INBreast and MIAS, are used in our experiments to test the proposed method. Comparing to state-of-the-art methods, competitive classifying results are observed using the RILQP method, with classification accuracies of 82.50% and 80.30% on INBreast and MIAS respectively. Comparative analysis also indicates that the proposed method outperforms other methods statistically.