Automated Classification of Local Patches in Colon Histopathology ∗
Habil Kalkan, Marius Nap, Robert P. W. Duin, Marco Loog · 2016
An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: nor-mal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly seg-mented nuclei are combined with texture features for classification. The classification is analyzed under the three scenarios: normal vs. abnormal, cancer vs. non-cancer and four-class classification on a labeled dataset consisting of 2000 patches per class which were collected from 55 different slices. The proposed method achieves 79.28 % mean accuracy between normal and abnormal; 87.67 % accuracy between cancer and non-cancer and 75.15 % between the four classes with equal class priories. 1.