International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 1, No. 1, 8 Oct. 2009
Cover page and Table of Contents: PDF (size: 473KB)
Multiresolution Fuzzy C-Means Clustering Using Markov Random Field for Image Segmentation
Full Text (PDF, 473KB), PP.49-57
Views: 0 Downloads: 0
Author(s)
Xuchao Li
1,*
Suxuan Bian
2
Index Terms
Image segmentation, Markov random field, wavelet transform, fuzzy c-means, multiresolution, scale
Abstract
In this paper, an unsupervised multiresolution image segmentation algorithm is put forward, which combines interscale and intrascale Markov random field and fuzzy c-means clustering with spatial constraints. In the initial label determination of wavelet coefficient phase, the statistical distribution property of wavelet coefficients is characterized by Gaussian mixture model, the properties of intrascale clustering and interscale persistence of wavelet coefficients are captured by Markov prior probability model. According to maximum a posterior rule, the initial label of wavelet coefficient from coarse to fine scale is determined. In the image segmentation phase, in order to overcome the shortcomings of conventional fuzzy c-means clustering, such as being sensitive to noise and lacking of spatial constraints, we construct the novel fuzzy c-means objective function based on the property of intrascale clustering and interscale persistence of wavelet coefficients, taking advantage of Lagrange multipliers, the improved objective function with spatial constraints is optimized, the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on real magnetic resonance image and peppers image with noise show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.
Cite This Paper
Xuchao Li, Suxuan Bian, "Multiresolution Fuzzy C-Means Clustering Using Markov Random Field for Image Segmentation", International Journal of Information Technology and Computer Science(IJITCS), vol.1, no.1, pp.49-57, 2009. DOI: 10.5815/ijitcs.2009.01.07
Reference
[1] Kalyani Mali, Sushmita Mitra, and Tinku Acharya, “A multiresolution fuzzy clustering of images,” International journal of computational cognition. vol. 4, pp. 30-38, 2006.
[2] Nuno Vasconcelos, and Andrew Lippman, “Empirical Bayesian motion segmentation,” IEEE Transactions on pattern analysis and machine intelligence. vol. 23, pp. 217-221, 2001.
[3] Huawu Deng, David A. Clausi, “Unsupervised image segmentation using a simple MRF model with a new implementation scheme,” Pattern recognition. vol. 37, pp. 2323–2335, 2004.
[4] Xuchao Li, Shanan Zhu, “FGMM-MRF hierarchical model to image segmentation,” Journal of computer aided design & computer graphics. vol. 17, pp. 2659-2664, 2005.
[5] Jean-Marc Laferte, Patrick Perez, and Fabric Heitz, “Discrete Markov image modeling and inference on the quadtree,” IEEE Transactions on image processing. vol. 9, pp. 390-404, 2000.
[6] J. Sun, D. Gu, S. Zhang and Y. Chen, “Hidden Markov bayesian texture segmentation using complex wavelet transform,” IEE Proceedings on Vision Image Signal Process. vol. 151, pp. 215-223, 2004.
[7] M. L. Comer and E. J. Delp, “The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results,” IEEE Transactions on image processing. vol. 9, pp.1731-1744, 2000.
[8] Bouman C. A, Shapiro M. “A multiscale random field model for Bayesian image segmentation,” IEEE Transactions on image processing. vol. 9, pp. 162-177, 1994.
[9] Justin K. Romberg, Hyeokho Choi, and Richard G. Baraniuk, “Bayesian tree-structured image modeling using wavelet-domain hidden Markov models,” IEEE Transactions on image processing. vol. 10, pp. 1056-1068, 2001.
[10] Krishnapuram Raghu, and Keller James M., “The possibilistic C-means algorithm: insights and recommendations,” IEEE Transactions on fuzzy systems. vol. 4, pp. 385-393, 1996.
[11] Keh-Shih Chuang, Hong-Long Tzeng, Sharon Chen, Jay Wu, Tzong-Jer Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Computerized Medical Imaging and Graphics. vol. 30, pp. 9–15, 2006.
[12] Bezdek Jim, “Pattern recognition with fuzzy objective function algorithm,” Plenum, New York, 1981.
[13] Dzung L. Pham, “Spatial models for fuzzy clustering,” Computer Vision and Image understanding. vol. 84, pp. 285–297, 2001.
[14] Mahmoud Ramza Rezaee, Pieter M. J. van der Zwet, Boudewijn P. F. Lelieveldt, Johan H. C. Reiber “A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering,” IEEE Transaction on image processing. vol. 9, pp. 1238-1248, 2000.
[15] Zoran Zivkovic, Ferdinand van der Heijden, “Recursive unsupervised learning of finite mixture models,” IEEE Transactions on pattern analysis and machine intelligence. vol. 26, pp. 651-656, 2005.
[16] Matthew S. Crouse, Robert D. Nowak, and Richard G. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models,” IEEE Transactions on signal processing. vol. 46, pp. 886-902, 1998.
[17] M.T. Hanna, S.A. Mansoori, “The discrete time wavelet transform: its discrete time fourier transform and filter bank implementation,” IEEE Transactions on Circuits Systems II: Analog Digital Signal Process. vol. 48, pp. 180-183, 2001.
[18] Daubechies I, “Ten lectures on wavelets,” Philadelphia: Society for Industrial and Applied Mathematics, 1992.
[19] Djamal Boukerroui, Olivier Basset, Nicole Guerin, Attila Baskurt, “Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation,” European Journal of Ultrasound. vol. 8, pp. 135-144, 1998.
[20] A. P. Dempster, N. M. Lard, and D. B. Rubin, “Maximum likelihood from incomplete data via EM algorithm,” Journal Royal Statistics Society. vol. 39, pp. 1-37, 1977.
[21] S. Mallat, S. Zhong, “Characterization of signals from multiscale edges,” IEEE Transactions on pattern analysis and machine intelligence. vol. 14, pp. 710-732, 1992.