Automated Gland Segmentation and Classification for Gleason Grading of Prostate Tissue Images

Abstract

The well-known Gleason grading method for an H&E prostatic carcinoma tissue image uses morphological features of histology patterns within a tissue slide to classify it into 5 grades. We have developed an automated gland segmentation and classification method that will be used for automated Gleason grading of a prostatic carcinoma tissue image. We demonstrate the performance of the proposed classification system for a three-class classification problem (benign, grade 3 carcinoma and grade 4 carcinoma) on a dataset containing 78 tissue images and achieve a classification accuracy of 88.84%. In comparison to the other segmentation-based methods, our approach combines the similarity of morphological patterns associated with a grade with the domain knowledge such as the appearance of nuclei and blue mucin for the grading task.