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.
Chen Li, Yixin Li, Changhao Sun, Hao Chen, Hong Zhang