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.
翻译：病理学图像分析是临床诊断许多疾病的基本程序。为了提高检测的准确性和客观性,现提出越来越多的计算机辅助诊断(CAD)系统。在这些方法中,随机实地模型在改进分析性能方面发挥着不可或缺的作用。在这次审查中,我们全面概述了基于马克夫随机字段和有条件随机字段的病理图像分析,它们是两种受欢迎的随机字段模型。首先,我们介绍两个随机字段和病理图像的背景。第二,我们总结了从建模到优化的MRF和通用报告格式的基本数学知识。然后,我们透彻审查了最近对MRF和病理图像分析通用报告格式的研究。最后,我们调查了相关工作中流行的方法,并讨论了CAD领域之间迁移的方法。
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