①(College of Science and Technology, Hunan University of Technology, Zhuzhou 412008, China) ②(School of Computer Science, Hunan University of Technology, Zhuzhou 412008, China)
Concerning the problem that general Fuzzy C-Means (FCM) and its improved algorithm are sensitive to noise in the samples clustering and clustering boundary is not accurate enough, an improved FCM clustering algorithm based on spatial correlation is proposed. Firstly, it can improve the method of clustering center calculation and the function of distance calculation, through analyzing spatial distribution characteristics, interaction and influence value of the samples. Then, it redefines the fuzzy membership matrix through introducing a control parameter during summing membership of the samples with neighborhood information, thus realizing smoothing membership of neighborhood samples. Theoretical analysis and experimental results show that the improved algorithm has a better effect for samples with a lot of noise, and that the regional boundary value can process the image better.
HE L H, WEN Y, WAN M, et al. Multi-channel features based automated segmentation of diffusion tensor imaging using an improved FCM with spatial constraints[J]. Neurocomputing, 2014, 137: 107-114. doi: 10.1016/j.neucom. 2013.09.051.
XIAO Mansheng, WEN Zhicheng, ZHANG Juwu, et al. An FCM clustering algorithm with improved membership function[J]. Control and Decision, 2015, 30(12): 2270-2274. doi: 10.13195/j.kzyjc.2014.1716.
SHEN Xuanjing, HE Yue, ZHANG Bo, et al. FCM with spatial information and membership constrains for image segmentation[J]. Journal of Beijing University of Technology, 2012, 38(7): 1073-1078.
YANG Zhangjing. Feature extraction based on neighborhood structure and its applications to face recognition[D]. [Ph.D. dissertation], Nanjing University of Science & Engineering, 2014.
ZHONG Chongfeng, LIU Zhi, YANG Yang, et al. Improved FCM algorithm based on neighboring membership constraint for image segmentation[J]. Journal of Jilin University (Information Science Edition), 2013, 31(6): 627-633. doi: 10.3969/j.issn.1671-5896.2013.06.012.
ZHOU Wengang, SUN Ting, and ZHU Hai. Image segmentation algorithm based on FCM optimized by adaptive spatial information[J]. Application Research of Computers, 2015, 32(7): 2205-2208. doi: 10.3969/j.issn.1001- 3695.2015.07.070.
WANG Yuanyuan. The study of Fuzzy C-means algorithm incorporating spatial information for brain MR image segmentation[D]. [Master dissertation], Xidian University, 2012.
[8]
KANNAN S R, DEVI R, RAMATHILAGAM S, et al. Effective FCM noise clustering algorithms in medical images[J]. Computers in Biology and Medicine, 2013, 43(2): 73-83. doi: 10.1016/j.compbiomed.2012.10.002.
[9]
ZHU C J, YANG S Z, ZHAO Q, et al. Robust semi-supervised kernel-FCM algorithm incorporating local spatial information for remote sensing image classification[J]. Journal of the Indian Society of Remote Sensing, 2014, 42(1): 35-49. doi: 10.1007/s12524-013-0296-x.
[10]
QAMAR U. A dissimilarity measure based fuzzy c-means (FCM) clustering algorithm[J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2014, 26(1): 229-238. doi: 10.3233/IFS-120730.
MENG Haidong, MA Nana, SONG Yuchen, et al. Research on Fuzzy C-Means clustering algorithm based on density function weighted[J]. Computer Engineering and Applications, 2012, 48(27): 123-127. doi: 10.3778/j.issn.1002-8331.2012.27. 026.
[12]
FALLAHI A, KHOTANLOUT H, POOYAN M, et al. Segmentation of uterine using neighborhood information affected possibilistic FCM and gaussian mixture model in uterine fibroid patients MRI[J]. Biomedical Engineering: Applications, Basis and Communications, 2014, 26(1): 1450010. doi: 10.4015/S1016237214500100.
[13]
CUI W C, WANG Y, FAN Y Y, et al. Localized FCM clustering with spatial information for medical image segmentation and bias field estimation[OL]. http://dx.doi. org/10.1155/2013/930301, 2013.
[14]
HUANG H, TANG Q F, and LIU Z. Adaptive correction forecasting approach for urban traffic flow based on Fuzzy C-Mean clustering and advanced neural network[EB/OL]. http://dx.doi.org/10.1155/2013/195824, 2013.
ZHOU Shaoguang, JIA Kaihua, and YIN Nan. An improved fuzzy C-means algorithm using pixel’s neighborhood information in image segmentation[J]. Science of Surveying and Mapping, 2013, 38(1): 153-155. doi: 10.3969/j.issn. 1671-0428.2008.09.006.
YANG Hui and YIN Fengjie. The brain image segmentation based on FCM with spatial information[J]. Journal of Liaoning University (Natural Sciences Edition), 2014, 41(3): 235-239. doi: 10.3969/j.issn.1000-5846.2014.03.008.
[17]
杨同峰. 基于空间关系的图像检索与分类研究[D]. [博士论文], 山东大学, 2013.
YANG Tongfeng. Research on image retrieval and classification based on spatial relational ships[D]. [Ph.D. dissertation], Shandong University, 2013.
