Abstract
In this paper, we propose a new feature representation for automatic classification of different tissue types in histopathological images. Our proposed approach is to combine different sets of features into a novel texture feature space. We compute all features in the Complex Shearlet domain, where we have not only examined the magnitude coefficients, but we also incorporate the relative phase (RP) coefficients to form the feature representation. Our Shearlet feature space consists of Haralick texture features, segmentation-based Fractal Texture Analysis, Local Binary patterns, and Local Oriented Statistic Information Booster. These descriptors are also used jointly to increase the classification accuracy of histopathological images when used with standard classifier. We demonstrate the effectiveness of our novel feature space with a support vector machine (SVM). The proposed features provide high classification performance competing with state-of-the-art systems equally on four publicly available datasets: Warwick-QU, Epistroma, BreaKHis, multi-class Kather.