Feasibility of Haralick's Texture Features for the Classification of Chromogenic In-situ Hybridization Images

TL;DR

This study investigates the potential of Haralick's second-order texture features for classifying chromogenic in-situ hybridization images, aiming to assist gene expression analysis without relying solely on expert assessment.

Contribution

It demonstrates that Haralick texture features can effectively classify chromogenic in-situ hybridization images, providing a new approach for structural and functional image analysis.

Findings

Haralick features are viable for image classification

Unsupervised clustering groups images based on texture

PCA yields more interpretable classes

Abstract

This paper presents a proof of concept for the usefulness of second-order texture features for the qualitative analysis and classification of chromogenic in-situ hybridization whole slide images in high-throughput imaging experiments. The challenge is that currently, the gold standard for gene expression grading in such images is expert assessment. The idea of the research team is to use different approaches in the analysis of these images that will be used for structural segmentation and functional analysis in gene expression. The article presents such perspective idea to select a number of textural features that are going to be used for classification. In our experiment, natural grouping of image samples (tiles) depending on their local texture properties was explored in an unsupervised classification procedure. The features are reduced to two dimensions with fuzzy c-means clustering. The overall conclusion of this experiment is that Haralick features are a viable choice for classification and analysis of chromogenic in-situ hybridization image data. The principal component analysis approach produced slightly more "understandable" from an annotator's point of view classes.