Cell segmentation method based on hidden Markov random field

被引：0
|
作者：
Su J. [1 ]
Liu S. [2 ]
机构：
[1] School of Information Science and Engineering, University of Ji'nan, Ji'nan
[2] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2019年 / 40卷 / 02期
关键词：
Expectation maximization; Hidden Markov random field; Image segmentation; K-means clustering; Maximum a posteriori;
D O I：
10.11990/jheu.201704062
中图分类号：
学科分类号：
摘要：
A two-level segmentation algorithm based on spatial clustering and hidden Markov random field (HMRF) is proposed to improve the segmentation accuracy of cell aggregation and adhesion region. First, based on color feature of pixels in the Lab color space, k-means++ clustering method is used to obtain an initialization tag set. Second, the spatial expression model of the cell image is constructed by HMRF, which fully employs the spatial constraint relation to reduce the influence of isolated points and smooth the segmentation area. Finally, the model parameters are optimized by using the expectation maximization algorithm. Through the interaction between the marker and observation fields, the label set is adjusted by the iterative algorithm. Experimental results of six kinds of 780 cells from bone marrow smears of 61 cell types show that the proposed algorithm improves the accuracy of segmentation by≥95%. Furthermore, the algorithm is convenient for further extraction, detection, and recognition of cell pathology characteristics. © 2019, Editorial Department of Journal of HEU. All right reserved.
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页码：400 / 405
页数：5
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