Breast Density Classification Using Multiresolution Local Quinary Patterns in Mammograms

This paper presents a method for breast density classification using local quinary patterns (LQP) in mammograms. LQP operators are used to capture the texture characteristics of the fibroglandular disk region  instead of the whole breast region as the majority of current studies have done. To maximise the local information, a multiresolution approach is employed followed by dimensionality reduction by selecting dominant patterns only. Subsequently, the Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to 85.6% accuracy which is comparable with the state-of-the-art in the literature. Our contributions are two fold: firstly, we show the role of the fibroglandular disk area in representing the whole breast region as an important region for more accurate density classification and secondly we show that the LQP operators can extract discriminative features comparable with the other popular techniques such as local binary patterns, textons and local ternary patterns (LTP).