基于核空间的加权邻域约束直觉模糊聚类算法

张洁玉; 李佐勇

doi:10.11999/JEIT161317

基于核空间的加权邻域约束直觉模糊聚类算法

doi: 10.11999/JEIT161317

张洁玉¹,
李佐勇²

1.
(中国药科大学理学院南京 211198) ②(闽江学院计算机科学系福州 350108) ③(福建省信息处理与智能控制重点实验室福州 350108)

基金项目:

国家自然科学基金青年基金(61501522)，福州市科技计划项目(2016-S-116)，福建省新世纪优秀人才支持计划(NCETFJ)，福建省高校青年自然基金重点项目(JZ160467)，福建省引导性项目(2017H0030)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-12-08
- 修回日期: 2017-04-18
- 刊出日期: 2017-09-19

Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means

ZHANG Jieyu¹,
LI Zuoyong²

1.
(School of Science, China Pharmaceutical University, Nanjing 211198, China)

Funds:

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)

摘要

摘要: 该文针对直觉模糊聚类算法不考虑空间邻域信息的缺点，提出一种基于核空间和加权邻域约束的直觉模糊C均值聚类算法。该算法首先在直觉模糊C均值(Intuitionistic Fuzzy C-Means, IFCM)算法的基础上加入空间邻域约束关系，且赋予邻域内每个点不同的权重；接着采用核诱导函数代替欧氏距离计算各点到聚类中心的距离；然后创建包含邻域信息的新的目标函数，最优化该目标函数得到新的隶属度及聚类中心的迭代表达式。利用所提出的新算法与同类聚类算法及基于显著过渡区域的二值化算法进行图像分割，并对结果进行定量分析后可知，所提出的算法最高能够得到0.9776的F度量值。实验结果表明新算法性能稳定并且具有较高的分割精度。
- 模糊C均值聚类 /
- 直觉模糊集 /
- 邻域信息约束 /
- 核空间
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.
- Fuzzy C-Means clustering /
- Intuitionistic fuzzy set /
- Spatial neighborhood information constraints /
- Kernel space

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参考文献(28)

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.

BEZDEK J C, EHRLICH R, and FULL W. FCM: The fuzzy c-means clustering algorithm[J[. Computers Geosciences, 1984, 10(2) : 191-203.

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.

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.

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.

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.

SZILAGYI L, BENYO Z, SZILGYI 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, Cancn, Mexico, 2003, 1: 724-726. doi: 10.1109/ IEMBS.2003.1279866.

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.

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.

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.

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.

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.

ATANASSOV K T. Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 1986, 20(1): 87-96. doi: 10.1016/S0165-0114(86) 80034-3.

ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.

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.

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.

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.

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.

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.

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.

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.

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.

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