Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

TL;DR

This paper presents an automatic gland segmentation algorithm in colon histology images using local intensity and texture features, employing a hierarchical random forest classifier to assist in colon cancer grading.

Contribution

It introduces a novel hierarchical random forest approach utilizing local intensity and texture features for gland segmentation in colon histology images.

Findings

The method achieves high accuracy in gland segmentation.

It is fast and suitable for clinical application.

The approach improves upon manual segmentation methods.

Abstract

Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup.