A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
It addresses the need for improved accuracy and objectivity in clinical diagnosis through a review of established methods, making it incremental by consolidating existing knowledge rather than introducing new techniques.
This paper provides a comprehensive review of Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) in pathology image analysis, summarizing their mathematical foundations and recent research applications to enhance computer-aided diagnosis systems.
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.