基于核空间的加权邻域约束直觉模糊聚类算法

doi:10.11999/JEIT161317

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Abstract To overcome the shortcoming of Intuitionistic Fuzzy C-Means (IFCM) that it does not take into account the spatial information, a new Kernel-based algorithm with Weighted Spatial Information (KWSI_IFCM) is proposed. Firstly, the constraint of weighted spatial neighborhood information is added. Secondly, instead of Euclidean distance, kernel-induced function is used to measure the distance between pixels and cluster centers. Thirdly, a new clustering objective function is created and then the iterative expressions of new membership and clustering centers are obtained by optimizing the new function. The quantitative analysis of image segmentation results using the new algorithm, other similar methods and a binarization method based on salient transition region shows that the new algorithm can get the F-measure value with 0.9776. The experimental results demonstrate that the proposed algorithm can obtain higher stability and segmentation accuracy than similar fuzzy C-mean algorithm.

Key words： Fuzzy C-Means clustering Intuitionistic fuzzy set Spatial neighborhood information constraints Kernel space

Received: 08 December 2016 Published: 11 May 2017

PACS:

TP391.4

Fund:The National Natural Science Foundation of China (61501522), Fuzhou Science and Technology Planning Project (2016-S-116), The Program for New Century Excellent Talents in Fujian Province University (NCETFJ), The Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), The Fujian Provincial Leading Project (2017H0030)

Corresponding Authors: ZHANG Jieyu E-mail: zhjy_xx@126.com

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Cite this article:

ZHANG Jieyu,LI Zuoyong. Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means[J]. JEIT, 2017, 39(9): 2162-2168.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT161317 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I9/2162

[1]	BEZDEK J C, EHRLICH R, and FULL W. FCM: The fuzzy c-means clustering algorithm[J[. Computers & Geosciences, 1984, 10(2) : 191-203.
[2]	王新宁, 林相波, 袁珍. 基于FCM聚类算法的MRI脑组织图像分割方法比较研究[J]. 北京生物医学工程, 2015, 34(3): 221-228. doi: 10.3969/j.issn.1002-3208.2015.03.01.
	WANG Xinning, LIN Xiangbo, and YUAN Zhen. A comparative study for MRI brain image segmentation based on FCM clustering algorithm[J]. Beijing Biomedical Engineering, 2015, 34(3): 221-228. doi: 10.3969/j.issn.1002- 3208.2015.03.01.
[3]	孙权森, 纪则轩. 基于模糊聚类的脑磁共振图像分割算法综述[J]. 数据采集与处理, 2016, 31(1): 28-42. doi: 10.16337/ j.1004-9037.2016.01.003.
	SUN Quansen and JI Zexuan. Fuzzy clustering for brain MR image segmentation[J]. Journal of Data Acquisition Processing, 2016, 31(1): 28-42. doi: 10.16337/j.1004-9037. 2016.01.003.
[4]	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. doi: 10.1109/42. 996338.
[5]	CHEN S and ZHANG D. 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. doi: 10.1109/TSMCB.2004.831165.
[6]	SZILAGYI L, BENYO Z, SZILÁGYI S M, et al. MR brain image segmentation using an enhanced fuzzy c-means algorithm[C]. Proceedings of the 25th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, Cancún, Mexico, 2003, 1: 724-726. doi: 10.1109/ IEMBS.2003.1279866.
[7]	CAI W, CHEN S, and ZHANG D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation[J]. Pattern Recognition, 2007, 40(3): 825-838. doi: 10.1016/j.patcog.2006.07.011.
[8]	KRINIDIS S and CHATZIS V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337. doi: 10.1109/TIP.2010.2040763.
[9]	李艳灵, 沈轶. 基于空间邻域信息的FCM图像分割算法[J]. 华中科技大学学报(自然科学版), 2009, 37(6): 56-59. doi: 10.13245/j.hust.2009.06.023.
	LI Yanling and SHEN Yi. Fuzzy C-means algorithm based on the spatial information for image segmentation[J]. Journal Huzhong University of Science & Technology(Natural Science Edition), 2009, 37(6): 56-59. doi: 10.13245/j.hust.2009.06. 023.
[10]	申铉京, 何月, 张博, 等. 基于空间信息及隶属度约束的FCM图像分割算法[J]. 北京工业大学学报, 2012, 38(7): 1073-1078.
	SHEN Xuanjing, HE Yue, ZHANG Bo, et al. FCM with spatial information and membership constrains for image segmention[J]. Journal of Beijing University of Technology, 2012, 38(7): 1073-1078.
[11]	夏菁, 张彩明, 张小峰, 等. 结合边缘局部信息的FCM抗噪图像分割算法[J]. 计算机辅助设计与图形学学报, 2014, 26(12): 2203-2213.
	XIA Jing, ZHANG Caiming, ZHANG Xiaofeng, et al. A novel robust FCM algorithm combining local information on edge for image segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(12): 2203-2213.
[12]	ATANASSOV K T. Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 1986, 20(1): 87-96. doi: 10.1016/S0165-0114(86) 80034-3.
[13]	ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
[14]	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. doi: 10.1016/j.asoc. 2010.05.005.
[15]	CHAIRA T. A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set[J]. Applied Soft Computing, 2012, 12(4): 1259-1266. doi: 10. 1016/j.asoc.2011.12.011.
[16]	HUANG C W, LIN K P, WU M C, et al. Intuitionistic fuzzy c-means clustering algorithm with neighborhood attraction in segmenting medical image[J]. Soft Computing, 2015, 19(2): 459-470. doi: 10.1007/s00500-014-1264-2.
[17]	王昭, 范九伦, 娄昊, 等. 一种融入局部信息的直觉模糊C-均值聚类图像分割算法[J]. 计算机应用研究, 2014, 31(9): 2864-2866. doi: 10.3969/j.issn.1001-3695.2014.09.073.
	WANG Zhao, FAN Jiulun, LOU Hao, et al. Intuitionistic fuzzy C-means clustering algorithm incorporating local information for image segmentation[J]. Application Research of Computers, 2014, 31(9): 2864-2866. doi: 10.3969/j.issn. 1001-3695.2014.09.073.
[18]	兰蓉, 马姣婷. 基于直觉模糊C-均值聚类算法的图像分割[J]. 西安邮电大学学报, 2016, 21(3): 1-4. doi: 10.13682/j.issn. 2095-6533.2016.04.010.
	LAN Rong and MA Jiaoting. Image segmentation based on intuitionstic fuzzy C-means clustering algorithm[J]. Journal of Xi,an University of Posts and Telecommunications, 2016, 21(3): 1-4. doi: 10.13682/j.issn.2095-6533.2016.04.010.
[19]	XU Z, CHEN J, and WU J. Clustering algorithm for intuitionistic fuzzy sets[J]. Information Sciences, 2008, 178(19): 3775-3790. doi: 10.1016/j.ins.2008.06.008.
[20]	LI Z, LIU G, ZHANG D, et al. Robust single-object image segmentation based on salient transition region[J]. Pattern Recognition, 2016, 52: 317-331. doi: org/10.1016/j.patcog. 2015.10.009.
[21]	GATOS B, NTIROGIANNIS K, and PRATIKAKIS I. ICDAR 2009 document image binarization contest (DIBCO 2009)[C]. 10th International Conference on Document Analysis and Recognition, Catalonia, Spain, 2009, 9: 1375-1382. doi: 10.1109/ICDAR.2009.246.