https://doi.org/10.20965/jaciii.2020.p0963 Cell Image Segmentation via Improved Markov Random Field Paper: Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model Dongming Li∗1,∗2, Changming Sun∗3,SuWei∗4,YueYu∗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 E-mail: [email protected] ∗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] In this paper, a segmentation method for cell images common approaches for cell segmentation is image inten- using Markov random field (MRF) based on a Chi- sity thresholding which suffers from the issue of inten- nese restaurant process model (CRPM) is proposed. sity inhomogeneity. Watershed based methods are com- Firstly, we carry out the preprocessing on the cell im- monly utilized for clustered nuclei segmentation. Dunn ages, and then we focus on cell image segmentation proposed the fuzzy C-means (FCM) method for image using MRF based on a CRPM under a maximum a segmentation [2]. The FCM method is an unsupervised posteriori (MAP) criterion. The CRPM can be used clustering algorithm, which has been widely used in the to estimate the number of clusters in advance, adjust- field of image segmentation. However, its high compu- ing the number of clusters automatically according to tational complexity and the fact that its performance de- the size of the data. Finally, the conditional iteration grades significantly with increased noise. Ng et al. pro- mode (CIM) method is used to implement the MRF posed a methodology that incorporates K-means and im- based cell image segmentation process. To validate our proved watershed segmentation algorithms for medical proposed method, segmentation experiments are per- image segmentation [3]. Zong and Tian proposed seg- formed on oral mucosal cell images. The segmentation mentation and feature extraction of brain tumors based on results were compared with other methods, using pre- magnetic resonance images using a K-means method [4]. cision, Dice, and mean square error (MSE) as the ob- K-means clustering is a simple clustering method with jective evaluation criteria. The experimental results low computational complexity compared to FCM. An show that our method produces accurate cell image image threshold segmentation algorithm based on maxi- segmentation results, and our method can effectively mum entropy and a genetic algorithm is presented in [5]. improve segmentation for the nucleus, binuclear cell, Meng et al. proposed an image segmentation method us- and micronucleus cell. This work will play an impor- ing multi-resolution Markov random field (MRF) [6]. The tant role in cell image recognition and analysis. MRF probabilistic framework is a powerful tool for image segmentation. It introduces spatial dependencies between labels, providing a robust-to-noise segmentation. How- Keywords: cell image segmentation, Markov ran- ever, MRF needs to set the initial classification number in dom field, dual-tree complex wavelet transform, Chinese advance, which is greatly influenced by subjective factors restaurant process model, morphology top-hat transform and is time-consuming. Our work focuses on cell image segmentation using an MRF model based on a Chinese restaurant process model (CRPM) under the maximum 1. Introduction a posteriori (MAP) criterion. The quality of segmenta- tion is evaluated on cell images using the precision, Dice, Accurate segmentation of cells in fluorescence mi- and mean square error (MSE) criteria. We have used cell croscopy images plays a key role in high-throughput ap- image datasets to evaluate several segmentation methods. plications such as the study of cell function and quantifi- The results show that our proposed method is better than cation of protein expression [1]. Recently, several meth- threshold based techniques. ods have been proposed for the segmentation of cell nu- The remainder of this paper is organized as follows. In clei in fluorescence microscopy images. One of the most Section 2, the related works on cell image segmentation Vol.24 No.7, 2020 Journal of Advanced Computational Intelligence 963 and Intelligent Informatics © Fuji Technology Press Ltd. Creative Commons CC BY-ND: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nd/4.0/). Li,D.etal. are briefly reviewed. Section 3 introduces and explains is that it can segment complex-shaped objects, and the proposed improved MRF-based cell image segmenta- it can deal with topological changes such as image tion method. The cell image segmentation algorithm is splitting and merging. Li et al. [15] proposed a new applied to real cell images and the results are presented in variational formulation for geometric active contour, Section 4. Finally in Section 5, we conclude. which makes the level-set function close to the signed distance function. Therefore, it completely eliminates the cost of reinitialization and improves the perfor- 2. Related Work mance of cell image segmentation. (c) Localized region-based active contour model meth- In the following section, we review existing methods ods [16–17] are region-based methods that use local for cell image segmentation. A wide range of cell im- region parameters for segmentation. They show su- age segmentation methods have been proposed in recent periority in the location of regional information, and years, and they can be categorized into three groups: man- they have the ability to deal with heterogeneous tex- ual segmentation, semi-automatic segmentation, and fully tures. Lankton and Tannenbaum [16] proposed a nat- automatic segmentation. ural framework that allows any region-based segmen- tation energy to be re-formulated locally. Kaur and (1) Manual segmentation (MS) method requires do- Jindal [17] proposed the thyroid segmentation algo- main experts to first determine the region of interest rithm for ultrasound and scintigraphy images by us- (ROI), and then draw precise boundaries around the ROI ing active contour without edges, localized region- to annotate each image pixel correctly. An MS method based active contour, and distance regularized level- is necessary because it provides a ground-truth image set methods. for further development of semi-automatic and automatic (d) Clustering-based method transforms image seg- segmentation methods. However, the disadvantage of mentation into clustering analysis in pattern recogni- manual segmentation is that it is subjective. Because the tion. There are two clustering-based methods for cell manual segmentation method depends on the knowledge image segmentation: K-means clustering [18] and and experience of domain experts, there will be signifi- FCM [2, 19]. The FCM clustering method has the ad- cant differences among experts and even for one expert vantage of obtaining accurate results because it can but at different times [7]. provide a global optimal value when initialization er- (2) Semi-automatic segmentation method uses auto- ror occurs. Therefore, the FCM clustering method is matic algorithms for small-scale user interaction, and gen- suitable for cell image segmentation with uncertainty erates accurate segmentation results of microscopic cell and complexity. images [8]. In microscopic cell image segmentation, the common methods of semi-automatic segmentation are as (3) Fully automatic segmentation method does not follows: require any user interaction, and the typical representa- tive is deep learning-based methods that are widely used in cell image segmentation. Long et al. [20] proposed (a) Seeded region growing methods [9–13] are based fully convolutional networks (FCN) for semantic segmen- on initial seed points provided by users and it- tation, and realized end-to-end pixel-level semantic seg- eratively fuse neighborhood pixels with similarity. mentation. Ronneberger et al. [21] presented a network In [9], Stewart et al. proposed a novel region grow- (U-Net) and a training strategy on biomedical images, ing method with pulse-coupled neural networks. This which greatly improved the segmentation performance. method introduces a new idea for automatic segmen- Jia et al. [22] developed a new weakly supervised learn- tation. Zhou et al. [10] proposed a region segmen- ing algorithm to learn to segment cancerous regions in tation method based on the Otsu method [11] and histopathology images, which was easy to implement and the Chan–Vese method [12] for cytoskeleton images. could be trained efficiently. In recent years, scholars have The method achieves an ideal processing effect, but made full use of the advantages of deep learning methods has high time and space complexities. In [13], a re- for cell image segmentation, and they continue to propose gion growing method with a multi-seed voting mech- new image segmentation methods to provide a reliable ba- anism