Email this article Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation
Abstract
FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results showd that KSFCM has the better performance.
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References
[1]Dunn J C, J.Cybernet, “A fuzzy relative of the ISODTA TA process and its use in detecting compact well separated clusters,” pp.32-57,March 1973.
[2]“Pattern Recognition with Fuzzy Objective Function Algorithms,” New York: Plenum Press, 1981.
[3]Dozing L.Pham, “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognition letters,pp.57-67,March,1999.
http://dx.doi.org/10.1016/S0167-8655(98)00121-4
[4] Balazs Balasko, Janos Abonyi and Balazs Feil, “Fuzzy clustering and Data Analysis Toolbox”(www.fmt.vein.hu/ softcom)
[5]Songcan Chen Daoqiang Zhang, “Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure,” IEEE Transactions on systems, man, and cybernetcs-part b:cybernetics,vol.34, No.4, August 2004.
[6]Cover TM, “Geometrical and statistical properties of systems of linear inequalities in pattern recognition,” IEEE Trans Electron Comput,Vol. 4, pp.26—34, 1965.
[7]GATH and A.B.GEVA, “GG_unsupervised optimal fuzzy cluster,” IEE Transactions on pattern analysis and machine intelligence, Vol,11.No.7,pp.773-781, July 1989.
[8]A.F.Gomez-Skarmeta,M.Delgado,M.A.Vila, “About the use of fuzzy clustering techniques for fuzzy model identification,” Fuzzy Sets and Systems,Vol.106,pp.179-188,1999.
http://dx.doi.org/10.1016/S0165-0114(97)00276-5
[9]A.F.Gomez-Skarmeta,M.Delgado, “About the use of fuzzy clustering techniques for fuzzy model identification,” Fuzzy sets and. Systems, Vol.106 pp:179-188, 1999.
http://dx.doi.org/10.1016/S0165-0114(97)00276-5
[10] GATH and A.B.GEVA, “Unsupervised Optimal Fuzzy Clustering” IEEE Transactions on patter analysis machine intelligence, Vol.11,No.7,July 1989.
[11]Songcan Chen and Daoqiang Zhang, “Robust Image Segmentation Using FCM With Spatial Constraints Based on New Kernel-Induced Distance Measure,” IEEE transactions on system, Vol.l34,No.4,pp.907-1917,2004.
[12]Ahmed, “A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data,” IEEE transaction on medical imaging, Vol.21,No.3,March 2002.
[13]Weiling Cai, Songcan Cheng, “Fast and roubst fuzzy c-means clustering algorithm incorporating local information for image segmentation,” Pattern Recognition, Vol.40, pp.825-838, 2007.
http://dx.doi.org/10.1016/j.patcog.2006.07.011
[14]Songcan Chen and Daoqiang Zhang, “Robust Image Segmentation Using FCM with Spatial Constraints Based on New Kernel-Induced Distance Measure,” IEEE transactions on system, Vol.134,No.4,pp.907-1917,2004.
[15]DzungL.Pham,Jerry L.Prince, “An adaptive fuzzy C-menas algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognition Letters. Vol.20,pp.57-68,1999.
http://dx.doi.org/10.1016/S0167-8655(98)00121-4
[16]Dao-Qiang Zhang,Song-Can Chen, “A novel kernelized fuzzy C-means algorithm with application in medical imge segmentation,” artificial intelligence in medicine. Vol.32, pp.37-50,2004.
[17]Miin-Shen Yang, “A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction,” Pattern Recognition Letters, Vol.29,pp.1713-1725, 2008.
http://dx.doi.org/10.1016/j.patrec.2008.04.016
PMCid:3150581
[18]Keh-Shih Chuang, Hong-Long Tzeng, “Fuzzy c-means clustering with spatial information for image segmentation,” computerized medical imaging and graphics, Vol.30, pp. 9-15, 2006.
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