论文标题
关于病理图像分析中MRF和CRF方法的全面综述
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
论文作者
论文摘要
病理图像分析是许多疾病临床诊断的重要程序。为了提高检测的准确性和客观性，如今，提出了越来越多的计算机辅助诊断（CAD）系统。在这些方法中，随机场模型在改善分析性能中起着必不可少的作用。在这篇综述中，我们介绍了基于马尔可夫随机字段（MRF）和条件随机场（CRF）的病理图像分析的全面概述，这是两个流行的随机场模型。首先，我们介绍了两个随机字段和病理图像的背景。其次，我们总结了从建模到优化的MRF和CRF的基本数学知识。然后，对最新的有关病理图像分析的MRF和CRF的研究进行了详尽的回顾。最后，我们研究了相关工作中流行的方法论，并讨论了CAD领域之间的方法迁移。
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.
