Abstract:In the segmentation of brain image,the image quality is often reduced due to the influence of noise or outliers. And traditional fuzzy clustering has some limitations and is easily affected by the initial value,which brings great trouble for doctors to accurately identify and extract brain tissue. Aiming at these problems,an improved fuzzy clustering image segmentation method based on neighborhoods of image pixels constructed by Markov model was proposed. Firstly,the initial clustering center was determined by Genetic Algorithm(AG). Secondly,the expression of the target function was changed,the calculation method of the membership matrix was changed by adding the correction term in the target function and was adjusted by the constraint coefficient. Finally,the Markov Random Field(WRF)was used to represent the label information of the neighborhood pixels,and the maximized conditional probability of Markov random field was used to represent the neighborhood of the pixel,which improves the noise immunity. Experimental results show that the proposed method has good noise immunity,it can reduce the false segmentation rate and has high segmentation accuracy when used to segment brain images. The average accuracy of the segmented image has Jaccard Similarity(JS)index of 82. 76%,Dice index of 90. 45%,and Sensitivity index of 90. 19%. At the same time,the segmentation of brain image boundaries is clearer and the segmented image is closer to the standard segmentation image.
[1] 宋建华. 基于FCM算法的大脑MR图像分割技术研究[D]. 哈尔滨:哈尔滨工程大学,2018:10-22. (SONG J H. Research on brain MR image segmentation based on FCM algorithm[D]. Harbin:Harbin Engineering University,2018:10-22.) [2] 刘建伟, 郭雷. 直方图的脑图像分割策略[J]. 西安工业大学学报,2014,34(3):188-192. (LIU J W,GUO L. Histogram-based method for magnetic resonance brain image segmentation[J]. Journal of Xi' an Technology University,2014,34(3):188-192.) [3] 赵雪梅. 基于黎曼流形的遥感影像特征表达及分割算法研究[D]. 阜新:辽宁工程技术大学,2017:15-30. (ZHAO X M. Remote sensing image feature expression and segmentation algorithm based on Riemannian manifolds[D]. Fuxin:Liaoning Technical University,2017:15-30.) [4] 王文娜, 马瑜, 姜雲腾, 等. 分数阶粒子群的模糊聚类图像分割算法研究[J]. 现代电子技术,2019,42(11):59-63.(WANG W N, MA Y,JIANG Y T,et al. Research on fuzzy clustering image segmentation algorithm of fractional-order particle swarm[J]. Modern Electronics Technique,2019,42(11):59-63.) [5] CHAIRA T. A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images[J]. Applied Soft Computing,2011,11(2):1711-1717. [6] DAS S,DE S. Multilevel color image segmentation using Modified Genetic Algorithm(MfGA)inspired fuzzy c-means clustering[C]//Proceedings of the 2nd International Conference on Research in Computational Intelligence and Communication Networks. Piscataway:IEEE,2017:78-83. [7] AHMED M N,YAMANY S M,MOHAMED N,et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,2002,21(3):193-199. [8] BAI X,ZHANG Y,LIU H,et al. Intuitionistic center-free FCM clustering for MR brain image segmentation[J]. IEEE Journal of Biomedical and Health Informatics,2018:1-1. [9] KRINIDIS S, CHATZIS V. A robust fuzzy local information c-means clustering algorithm[J]. IEEE Transactions on Image Processing,2010,19(5):1328-1337. [10] ELAZAB A,WANG C,JIA F,et al. Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy c-means clustering[J]. Computational and Mathematical Methods in Medicine,2015, 2015:No. 485495. [11] 韩子硕, 王春平. 基于改进FCM与MRF的SAR图像分割[J]. 系统工程与电子技术,2019,41(8):1726-1734. (HAN Z S, WANG C P. SAR image segmentation based on improved FCM and MRF[J]. Systems Engineering and Electronics,2019,41(8):1726-1734.) [12] 王士龙, 徐玉如, 万磊, 等. 一种基于信息熵约束的快速FCM聚类水下图像分割算法[J]. 计算机科学,2010,37(12):243-246,286.(WANG S L,XU Y R,WAN L,et al. Fast fuzzy c-means algorithm based on entropy constraint for underwater image segmentation[J]. Computer Science,2010,37(12):243-246,286.) [13] PAL N R,BEZDEK J C. On cluster validity for the fuzzy c-means model[J]. IEEE Transactions on Fuzzy Systems,1995,3(3):370-379. [14] 黄白梅. 基于GA优化的核模糊c均值聚类算法的研究[D]. 武汉:武汉科技大学,2013:9-30. (HUANG B M. Research in kernel fuzzy-based fuzzy c-means clustering algorithm based on GA optimization[D]. Wuhan:Wuhan University of Science and Technology,2013:9-30.) [15] 董发志, 丁洪伟, 杨志军, 等. 基于遗传算法和模糊c均值聚类的WSN分簇路由算法[J]. 计算机应用,2019,39(8):2359-2365. (DONG F Z,DING H W,YANG Z J,et al. WSN clustering routing algorithm based on genetic algorithm and fuzzy c-means clustering[J]. Journal of Computer Applications,2019, 39(8):2359-2365.) [16] 赵颖超. 基于FCM的图像分割算法研究[D]. 长沙:湖南师范大学,2019:18-22. (ZHAO Y C. Research on image segmentation algorithm based on FCM[D]. Changsha:Hunan Normal University,2019:18-22.) [17] KWAN R K S,EVANS A C,PIKE G B. MRI simulation-based evaluation of image-processing and classification methods[J]. IEEE Transactions on Medical Imaging,1999,18(11):1085-1097. [18] 兰丙申, 韩红伟. 基于字典降噪改进模糊聚类MRI图像分割算法[J]. 中国数字医学,2018,13(10):49-51. (LAN B K,HAN H W. The algorithm of the segmentation of MRI image based on dictionary noise reduction to improve fuzzy clustering[J]. China Digital Medicine,2018,13(10):49-51.) [19] JI Z,SUN Q,XIA D. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image[J]. Computerized Medical Imaging and Graphics,2011,35(5):383-397. [20] LAI J,ZHU H,LING X. Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints[J]. Mathematical Problems in Engineering, 2019,2019:No. 4625371. [21] SHANKAR R,BHATTACHARYA M. Brain tumor segmentation of normal and pathological tissues using k-mean clustering with fuzzy c-mean clustering[C]//Proceedings of the 2017 European Congress on Computational Methods in Applied Sciences and Engineering,LNCVB 27. Cham:Springer,2017:286-296.