A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis

TL;DR

This paper provides a comprehensive review of Markov Random Fields and Conditional Random Fields in pathology image analysis, highlighting their mathematical foundations, recent research developments, and methodological trends in computer-aided diagnosis.

Contribution

It offers an extensive overview of MRF and CRF applications in pathology imaging, including background, mathematical modeling, recent advances, and methodological insights.

Findings

Summarizes recent research on MRFs and CRFs in pathology images

Discusses method migration among CAD systems

Highlights the importance of random field models in improving diagnosis accuracy

Abstract

Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. Among these methods, random field models play an indispensable role in improving the analysis performance. In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models. Firstly, we introduce the background of two random fields and pathology images. Secondly, we summarize the basic mathematical knowledge of MRFs and CRFs from modelling to optimization. Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented. Finally, we investigate the popular methodologies in the related works and discuss the method migration among CAD field.