C-Means with Fuzzy Local Information
This paper presents a variation of Fuzzy c-Means(FCM )algorithm that provides image clustering .The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way.The new algorithm is called C-Means with Fuzzy Local Information (CMFLI). CMFLI can overcome the disadvantages of known fuzzy c-means algorithm and at the same time enhances the clustering performance.The major characteristic of CMFLI is the use of fuzzy local information(both spatial and gray level)similarity measure, aiming to guarantee noise insensitiveness and image detail preservation .Furthermore, the proposed algorithm is fully free of empirically adjusted parameters (a, λg , λs etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature.Experiments performed on synthetic and real world images show that CMFLI algorithm is efficient,providing robustness to noisy images.
X. Munoz, J. Freixenet, X. Cufi, and J. Marti, “Strategies for image segmentation combining region and boundary information,” PatternRecognit. Lett., vol. 24, no. 1, pp. 375–392, 2003.
D. Pham, C. Xu, and J. Prince, “A survey of current methods in medicalimage segmentation,”Annu. Rev. Biomed. Eng., vol. 2, pp. 315–337,2000.
J.Udupa and S.Samarasekara,”Fuzzy connectedness and object definition:Theory,algorithm and applications in image segmentation,”Graph Model Image Process.,vol.58,no.3,pp.246-261,1996.
Y. Tolias and S. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,”IEEE Trans. Syst., Man, Cybern., vol. 28, no. 3, pp. 359–369, Mar.1998.
J.Noordam,W.Van den Broek, and L.Buydens,”Geometrically Guided fuzzy C-means clustering for multivariate image segmentation,” in Proc.Int.Conf.Pattern Recognition,2000,vol.1,pp.462-465.
M. Yang, Y. J. Hu, K. Lin, and C. C. Lin, “Segmentation techniques for issue differentiation in MRI of ophthalmology using fuzzy clustering algorithms,” Magn. Res. Imag., vol. 20, no. 2, pp. 173–179, 2002.
G. Karmakar and L. Dooley, “A generic fuzzy rule based image segmentation algorithm,” Pattern Recognit. Lett., vol. 23, no. 10, pp.1215–1227, 2002.
M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriarty, “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI Data ,” IEEE Trans. Med. Imag., vol. 21, pp. 193–199,2002.
J.Bezdek, Pattern Recognition With FuzzyObjective Function Algorithms.New York: Plenum, 1981.
D. Pham, “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognit.Lett., vol. 20, pp. 57–68, 1999.
S. Chen and D. Zhang, “Robust image segmentation using FCM withspatial constraints based on new kernel-induced distance measure,”IEEE Trans. Syst., Man, Cybern., vol. 34, pp. 1907–1916, 2004.
L. Szilagyi, Z. Benyo, S. Szilagyii, and H. Adam, “MR brain imagesegmentation using an enhanced fuzzy C-means algorithm,” in Proc.25th Annu. Int. Conf. IEEE EMBS, 2003, pp. 17–21.
M. Krinidis and I. Pitas, “Color texture segmentation based-on themodal energy of deformable surfaces,” IEEE Trans. Image Process.,vol. 18, no. 7, pp. 1613–1622, Jul. 2009.
D.Pham,“Fuzzy clustering with spatial constraints,” in Proc. Int. Conf.Image Processing, New York, 2002, vol. II, pp. 65–68.
W. Cai, S. Chen, and D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,”Pattern Recognit., vol. 40, no. 3, pp. 825–838, Mar. 2007.
J.Dunn,”A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters,”J.Cybern.,.vol.3,pp.32-57,1974.
R. Hathaway, J. Bezdek, and Y. Hu, “Generalized fuzzy c-means clusteringstrategies using L norm distance,” IEEE Trans. Fuzzy Syst., vol.8, pp. 576–582, Oct. 2000.
K.Wu and M. Yang, “Alternative c-means clustering algorithms,” PatternRecognit., vol. 35, no. 10, pp. 2267–2278, 2002.
J.Leski,”Toward a robust fuzzy clustering,”Fuzzy sets Syst,.vol.137,no.2,pp.215-233,2003.
J.MacQueen,”some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Mathematical Statisticsand Probability, 1967, vol. 1, pp. 281–297.
R. Sibson, “SLINK: An optimally efficient algorithm for the single-link cluster method,” The Comput. J., vol. 16, no. 1, pp. 30–34, 1973.
M.Mathworks and Natick,Image Processing Toolbox[Online].Available:http://www.mathworks.com.
F.Masulli and A.Schnone,”A fuzzy clustering based segmentation system as support to diagnosis in medical imaging,”Artif.Intell.Med.,vol.16.no.2,pp.129-147,1999.
D.Martin,C.Fowlkes,D.Tal,and J.Malik,”A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc.8th Int. Conf.computer vision,Jul.2001,vol.2,pp.416-423.
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