1.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine，Hefei University of Technology，Hefei，230009，China； 2．School of Computer and Information，Hefei University of Technology，Hefei，230009，China
Current image segmentation algorithm based on fuzzy clustering is sensitive to noise and cannot properly deal with the relationship between the gray features of the image and the neighborhood pixels.To solve this problem，the idea of multiple kernel clustering and the possibility of clustering are introduced to propose the EMKPFC(Enhanced Multiple Kernel Possibilistic Fuzzy Cmeans)algorithm for image segmentation.This algorithm can effectively combine the advantages of the fuzzy clustering method and the possibility clustering algorithm.Furthermore，the algorithm can avoid the uncertainty of the selection of kernel function and increase the ability of antitransformation.For a variety of chosen kernel functions，it can meet the different demand of images for nuclear functions and calculate the best match of the weight value.In the case of the data without any priori，it is not only able to accurately divide the leading data，but also can be done with the nonlinear classification of non group data.The results of experiments on synthetic images，real images and medical images show that the proposed algorithm has better performance than other image segmentation algorithms.
Tang Yiming1，2*，Zhao Genlu1，2，Ren Fuji1，2，Feng Gangyong1，2，Hu Xianghui1，2.
The EMKPFC algorithm of image segmentation[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(3): 569
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参考文献
[1] Perret B，Cousty J，Tankyevych O，et al.Directed connected operators：Asymmetric hierarchies for image filtering and segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37(6)：1162－1176. [2] Sikka K，Sinha N，Singh P K，et al.A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.Magnetic Resonance Imaging，2009，27(7)：994－1004. [3] Zhou J，Chen C L P，Chen L．Maximumentropybased multiple kernel fuzzy Cmeans clustering algorithm.In：Proceedings of the IEEE International Conference on Systems，Man and Cybernetics.San Diego，USA：IEEE，2014：1198－1203. [4] Nguyen D D，Ngo L T，Pham L T，et al.Towards hybrid clustering approach to data classification：Multiple kernels based intervalvalued fuzzy Cmeans algorithms.Fuzzy Sets and Systems，2015，279：17－39. [5] Dunn J C．A fuzzy relative of the ISO DATA process and its use in detecting compact wellseparated clusters.Journal of Cybernetics and Systems，1973，3(3)：33－57. [6] Bezdek J C．Pattern recognition with fuzzy objective function algorithms.New York：Plenum Press，1981，256. [7] Ahmed M N，Yamany S M，Mohamed N A，et al.A modified fuzzy Cmeans algorithm for bias field estimation and segmentation of MRI data.IEEE Transactions on Medical Imaging，2002，21(3)：193－199. [8] Szilagyi L，Benyo Z，Szilagyi S M，et al.MR brain image segmentation using an enhanced fuzzy Cmeans algorithm.In：Proceedings of 25th Annual International Conference of IEEE EMBS.Cancun，Mexico：IEEE，2003：17－21. [9] Chen S C，Zhang D Q．Robust image segmentation using FCM with spatial constraints based on new kernelinduced distance measure.IEEE Transactions on System Man and Cybernetics－Part B，2004，34(4)：1907－1916. [10] Krinidis S，Chatzis V．A robust fuzzy local information Cmeans clustering algorithm.IEEE Transactions on Image Processing，2010，19(5)：1328－1337. [11] Krishnapuram R，Keller J M．A possibilistic approach to clustering.IEEE Transaction on Fuzzy Systems，1993，1(2)：98－110. [12] Zhu L，Chung F L，Wang S T．Generalized fuzzy Cmeans clustering algorithm with improved fuzzy partitions.IEEE Transactions on Systems，Man，and Cybernetics－Part B：Cybernetics，2009，39(3)：578－591. [13] Zhao F，Liu H Q，Fan J L．A multiobjective spatial fuzzy clustering algorithm for image segmentation.Applied Soft Computing，2015，30：48－57. [14] Huang H C，Chuang Y Y．Multiple kernel fuzzy clustering.IEEE Transaction on Fuzzy Systems，2012，20(1)：120－134. [15] Doring C，Timm H，Borgelt C．An extension to possibilistic fuzzy cluster analysis.Fuzzy Sets and Systems，2004，147：3－16. [16] Tang Y M，Liu X P．Differently implicational universal triple I method of(1，2，2)type.Computers & Mathematics with Applications，2010，59(6)：1965－1984. [17] Tang Y M，Ren F J．Universal triple I method for fuzzy reasoning and fuzzy controller.Iranian Journal of Fuzzy Systems，2013，10(5)：1－24. [18] Tang Y M，Yang X Z．Symmetric implicational method of fuzzy reasoning.International Journal of Approximate Reasoning，2013，54(8)：1034－1048. [19] Tang Y M，Ren F J．Variable differently implicational algorithm of fuzzy inference.Journal of Intelligent and Fuzzy Systems，2015，28(4)：1885－1897.
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