Classification of Tumor Epithelium and Stroma in Colorectal Cancer Based on Discrete Tchebichef Moments

Rodrigo Nava, Germán González, Jan Kybic, Boris Escalante-Ramírez

Abstract: Colorectal cancer is a major cause of mortality. As the disease progresses, adenomas and their surrounding tissue are modified. Therefore, a large number of samples from the epithelial cell layer and stroma must be collected and analyzed manually to estimate the potential evolution and stage of the disease. In this study, we propose a novel method for automatic classification of tumor epithelium and stroma in digitized tissue microarrays. To this end, we use discrete Tchebichef moments (DTMs) to characterize tumors based on their textural information. DTMs are able to capture image features in a non-redundant way providing a unique description. A support vector machine was trained to classify a dataset composed of 1376 tissue microarrays from 643 patients with colorectal cancer. The proposal achieved 97.62 % of sensitivity and 95 % of specificity showing the effectiveness of the methodology.