International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.4, No.7, Jun. 2012
Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques
Full Text (PDF, 1994KB), PP.50-60
This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along with Gaussian and salt and pepper noise for better review and comparison. Based on outputs best algorithm is suggested.
Cite This Paper
Prabhjot Kaur, Pallavi Gupta, Poonam Sharma,"Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.7, pp.50-60, 2012. DOI: 10.5815/ijisa.2012.07.07
L.A. Zadeh (1265), ―Fuzzy Sets, Inform. and Control, 1,331-353.
A novel intuitionistic fuzzy C means clustering algorithm and its application tomedical images, Tamalika Chaira
Applied Soft Computing 11 (2011) 1711–1717, Fuzzy Clustering Using Kernel Method, Daoqiang Zhang Songcan Chen Proceedings of 2012 international conference on control and automation ,Xiamen,china,june 2002
K.T. Atanassov’s, Intuitionistic fuzzy sets, VIII TKR’s Session, Sofia, 213 (Deposed in Central Science – Technology Library of Bulgaria Academy of Science –1627/14).
J.C. Bezdek (1211), ―Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum, NY.
Y.A. Tolias and S.M. Panas(1221), On applying spatial constraints in fuzzy image clustering using a fuzzy rulebased system., IEEE Signal Processing Letters 5, 245.247 (1221).
S. T. Acton and D. P. Mukherjee(2000), ―Scale space classification using area morphology, IEEE Trans. Image Process., vol. 2, no. 4, pp. 623–635, Apr. 2000.
R.N. Dave and R. Krishnapuram (1227), ―Robust Clustering Methods: A Unified View, IEEE Transactions on Fuzzy Systems, May, Vol 5, No. 2.
R. Krishnapuram and J. Keller (1223), "A Possibilistic Approach to Clustering", IEEE Trans. on Fuzzy Systems, vol .1. No. 2, pp.21-1 10.
M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty(2002), ―A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imag., vol. 21, no. 3, pp. 123–122, Mar. 2002.
Yang Zhang, Chung Fu-Lai, et al,(2002), Robust fuzzy clustering-based image segmentation, Applied Soft Computing, vol(2), no. 1, Jan (2002), pp. 10-14.
Kang Jiayin, Min Lequan et al. (2002), ―Novel modified fuzzy c-means algorithm with applications, Digital Signal Processing. March (2002),vol 12, no. 2, pp. 302-312.
Yang Y., Zheng Ch., and Lin P. Fuzzy c-means clustering algorithm with a novel penalty term for image segmentation. Opto-electronic review, Vol.13, Issue 4, 2005, pp. 302-315.
C. Ambroise and G. Govaert,(1221), Convergence of an EM-type algorithm for spatial clustering., Pattern Recognition Letters 12, 212.227(1221).
Shen Shan, Sandham W., and Sterr A.(2005), MRI fuzzy segmentation of brain tissue using neighbourhood attraction with neural network optimization, IEEE transactions on information technology in biomedicine,Vol. 2, issue 3, September 2005, pp. 452-467.
Chuang Keh-Shih, Tzeng Hong-Long, Chen Sharon et al.(2006), Fuzzy C-means clustering with spatial information for image segmentation, Computeried Medical Imaging and graphics,30(2006) 2-15.
 F.C.H. Rhee, C. Hwang, A Type-2 fuzzy c means clustering algorithm, in: Proc. in Joint 2th IFSA World Congress and 20th NAFIPS International Conference 4,2001, pp. 1226–1222.