A machine learning approach to identify prostate cancer areas in complex histological images

Abstract

Separating benign glands, and cancer areas from stroma is one of the vital steps towards automated grading of prostate cancer in digital images of H&E preparations. In this work we present a novel tool that utilizes a supervised classification of histograms of staining components in hematoxylin and eosin images to delineate areas of benign and cancer glands. Using high resolution images of whole slide prostatectomies we compared several image classification schemes which included intensity histograms, histograms of oriented gradients, and their concatenations to the manual annotations of tissues by a pathologist, and showed that joint intensity histograms of hematoxylin and eosin components performed with the highest accuracy.