融合混沌优化和改进模糊聚类的图像分割算法

doi:10.3969/j.issn.0372-2112.2020.05.019

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摘要基于邻域广义模糊聚类算法能够分割含噪声灰度图像，但是如果图像灰度分布不均衡或者起始的聚类中心设置不合适仍会导致该算法分割失败，为此，提出一种基于混沌优化和改进模糊聚类算法相融合的图像分割算法.首先，将每一类的隶属度之和引入基于邻域广义模糊聚类算法的目标函数中，从而能够均衡较大类和较小类对目标函数的贡献.其次，以新目标函数为基础，利用拉格朗日乘子法推导出相应的隶属度和聚类中心.再次，将混沌优化和改进模糊聚类算法联合得到最优解，即最合适的聚类中心，细节上，每一代的聚类中心分别由混沌系统和改进模糊聚类算法两种路径产生，具有较小目标函数的聚类中心进入下一个迭代进程.最后，利用具有不平衡特性的无损检测图像进行实验，结果表明本文算法具有更高的分割准确率和更好的视觉效果.

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	朱占龙
	刘永军

关键词 ：图像分割, 混沌优化, 模糊聚类, 灰度分布不均衡

Abstract：The spatial generalized fuzzy c-means clustering algorithm (GFCM_S) is a popular technique for image segmentation,but it is not so effective when the image has the features of unequal cluster sizes or the initial cluster centers we choose are improper.In this paper,for solving the above shortcomings of GFCM_S,a novel algorithm incorporating chaos optimization and improved fuzzy c-means (CIGFCM_S) is proposed.Firstly,each size of clusters is integrated into the objective function of GFCM_S so as to equalize the contribution of larger and smaller clusters to the objective function.Secondly,the iteratively membership degree and cluster centers are deduced by the Lagrange multiplier method.Thirdly,a new iterative strategy is used to seek the optimal solutions.In detail,the optimal solutions of next generation are searched by two-paths,one path originates chaos optimization and the other is obtained by updating membership degree and cluster centers on the basis of current optimal solutions,and then the better solutions go to next generation until the end.Lastly,the non-destructive testing (NDT) images with the characters of unequal cluster sizes are used for experiments,the results show that the proposed algorithm has better segmentation accuracy and visual effects.

Key words： image segmentation chaos optimization fuzzy clustering unequal cluster sizes

收稿日期: 2019-03-11 出版日期: 2019-11-20

ZTFLH:

TP391.4

基金资助:河北省高等学校科学技术研究（No.BJ2018029）；河北省教育厅重点（No.ZD2018212）；河北地质大学博士科研启动基金（No.BQ201606）

作者简介: 朱占龙 男,1984年5月出生,河北石家庄人.分别于2008年和2011年在燕山大学获得学士学位、硕士学位,2015年于东南大学获得博士学位.现为河北地质大学信息工程学院讲师,主要研究方向为图像处理.E-mail:zhuzl@hgu.edu.cn;刘永军 男,1970年9月出生,河北石家庄人.副教授、中国计算机学会高级会员.1992年于哈尔滨工业大学获得学士学位,2013年于石家庄铁道大学获得硕士学位.现为河北地质大学信息工程学院教师,主要从事计算机专业教育、智能处理研究.

引用本文:

朱占龙, 刘永军. 融合混沌优化和改进模糊聚类的图像分割算法[J]. 电子学报, 2020, 48(5): 975-984. ZHU Zhan-long, LIU Yong-jun. A Novel Algorithm by Incorporating Chaos Optimization and Improved Fuzzy C-Means for Image Segmentation. Acta Electronica Sinica, 2020, 48(5): 975-984.

链接本文:

http://www.ejournal.org.cn/CN/10.3969/j.issn.0372-2112.2020.05.019 或 http://www.ejournal.org.cn/CN/Y2020/V48/I5/975

[1] DESPOTOVIC I,VANSTEENKISTE E,PHILIPS W.Spatially coherent fuzzy clustering for accurate and noise-robust image segmentation[J].IEEE Signal Processing Letters,2013,20(4):295-298.
[2] 李凯,曹喆.一种基于神经网络的广义熵模糊聚类算法[J].电子学报,2016,44(8):1881-1886. LI Kai,CAO Zhe.A fuzzy clustering algorithm with generalized entropy based on neural network[J].Acta Electronica Sinica,2016,44(8):1881-1886.(in Chinese)
[3] GUO F F,WANG X X,SHEN J.Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation[J].Iet Image Processing,2016,10(4):272-279.
[4] 李磊,董卓莉,张德贤.基于自适应区域限制FCM的图像分割方法[J].电子学报,2018,46(6):1312-1318. LI Lei,DONG Zhuo-li,ZHANG De-xian.Adaptive region constrained FCM algorithm for image segmentation[J].Acta Electronica Sinica,2018,46(6):1312-1318.(in Chinese)
[5] 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.
[6] CHEN S K,ZHANG D Q.Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J].IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics,2004,34(4):1907-1916.
[7] ZHAO F,JIAO L,LIU H.Fuzzy c-means clustering with non local spatial information for noisy image segmentation[J].Frontiers of Computer Science in China,2011,5(1):45-56.
[8] ZHAO F,JIAO L,LIU H,et al.A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation[J].Signal Processing,2011,91(4):988-999.
[9] CAI W,CHEN S,ZHANG D.Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J].Pattern Recognition,2007,40(3):825-838.
[10] DING Y,FU X.Kernel-based fuzzy c-means clustering algorithm based on genetic Algorithm[J].Neurocomputing,2015,188:233-238.
[11] BENAICHOUCHE A N,OULHADJ H,SIARRY P.Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization,Mahalanobis distance and post-segmentation correction[J].Digital Signal Processing,2013,23(5):1390-1400.
[12] MUKHOPADHYAY A,MAULIK U.A multi-objective approach to MR brain image segmentation[J].Applied Soft Computing,2011,11(1):872-880.
[13] 赵凤,刘汉强,范九伦.基于互补空间信息的多目标进化聚类图像分割[J].电子与信息学报,2015,37(03):672-678.(in Chinese) ZHAO Feng,LIU Hanqiang,FAN Jiulun.Multi-objective evolutionary clustering with complementary spatial information for image segmentation[J].Journal of Electronics and Information Technology,2015,37(03):672-678.
[14] ZHU L,CHUNG F,WANG S.Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions[J].IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics,2009,39(3):578-591.
[15] ZHAO F,JIAO L.Spatial improved fuzzy c-means clustering for image segmentation[A].International Conference on Electronic and Mechanical Engineering and Information Technology[C].Harbin,China,2011.4791-4794.
[16] NOORDAM J C,VAN DEN BROEK W H A M,BUYDENS L M C.Multivariate image segmentation with cluster size insensitive Fuzzy C-means[J].Chemometrics and Intelligent Laboratory Systems,2002,64(1):65-78.
[17] LIU Y,HOU T,LIU F.Improving fuzzy c-means method for unbalanced dataset[J].Electronics Letters,2015,51(23):1880-1881.
[18] LIN P L,HUANG P W,KUO C H,et al.A size-insensitive integrity-based fuzzy c-means method for data clustering[J].Pattern Recognition,2014,47(5):2042-2056.
[19] 文传军,詹永照,柯佳.广义均衡模糊C均值聚类算法[J].系统工程理论与实践,2012,32(12):2751-2755. WEN Chuanjun,ZHAN Yongzhao,KE Jia.General equalization fuzzy c-means clustering algorithm[J].Systems Engineering-Theory and Practice,2012,32(12):2751-2755.(in Chinese)
[20] LUO Y,TANG G,ZHOU L.Hybrid approach for solving systems of nonlinear equations using chaos optimization and quasi-newton method[J].Applied Soft Computing,2008,8(2):1068-1073.
[21] ALATAS B,AKIN E,OZER A B.Chaos embedded particle swarm optimization algorithms[J].Chaos Solitons & Fractals,2009,40(4):1715-1734.
[22] 王文波,晋云雨,王斌,等.混沌信号的自适应阈值同步挤压小波变换消噪[J].电子学报,2018,46(7):1652-1657. WANG Wen-bo,JIN Yun-yu,WANG Bin,et al.Chaotic signal de-noising based on adaptive threshold synchrosqueezed wavelet transform[J].Acta Electronica Sinica,2018,46(7):1652-1657.(in Chinese)
[23] 范九伦,张雪锋.分段Logistic混沌映射及其性能分析[J].电子学报,2009,37(4):720-725. FAN Jiu-lun,ZHANG Xue-feng.Piecewise logistic chaotic map and its performance analysis[J].Acta Electronica Sinica,2009,37(4):720-725.(in Chinese)
[24] Sezgin M[OL].http://mehmetsezgin.net/.2019-3-6.