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JACIII Vol.24 No.7 pp. 963-971
doi: 10.20965/jaciii.2020.p0963
(2020)

Paper:

Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model

Dongming Li*1,*2, Changming Sun*3, Su Wei*4, Yue Yu*2,*5, and Jinhua Yang*1,†

*1College of Opto-Electronic Engineering, Changchun University of Science and Technology
No.7089 Weixin Road, Chaoyang District, Changchun, Jilin 130022, China

*2School of Information Technology, Jilin Agricultural University
No.2888 Xincheng Road, Jingyue District, Changchun, Jilin 130118, China

*3CSIRO Data61
P.O. Box 76, Epping, New South Wales 1710, Australia

*4Modern Educational Technology Center, Changchun University of Chinese Medicine
No.1035 Boshuo Road, Jingyue District, Changchun, Jilin 130117, China

*5College of Artificial Intelligence, Tourism College of Changchun University
Sheling Town, University Campus District, Changchun, Jilin 130607, China

Corresponding author

Received:
October 5, 2020
Accepted:
November 18, 2020
Published:
December 20, 2020
Keywords:
cell image segmentation, Markov random field, dual-tree complex wavelet transform, Chinese restaurant process model, morphology top-hat transform
Abstract
Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model

Cell image segmentaion by our method

In this paper, a segmentation method for cell images using Markov random field (MRF) based on a Chinese restaurant process model (CRPM) is proposed. Firstly, we carry out the preprocessing on the cell images, and then we focus on cell image segmentation using MRF based on a CRPM under a maximum a posteriori (MAP) criterion. The CRPM can be used to estimate the number of clusters in advance, adjusting the number of clusters automatically according to the size of the data. Finally, the conditional iteration mode (CIM) method is used to implement the MRF based cell image segmentation process. To validate our proposed method, segmentation experiments are performed on oral mucosal cell images. The segmentation results were compared with other methods, using precision, Dice, and mean square error (MSE) as the objective evaluation criteria. The experimental results show that our method produces accurate cell image segmentation results, and our method can effectively improve segmentation for the nucleus, binuclear cell, and micronucleus cell. This work will play an important role in cell image recognition and analysis.

Cite this article as:
Dongming Li, Changming Sun, Su Wei, Yue Yu, and Jinhua Yang, “Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 963-971, 2020.
Data files:
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