Abstract
In this paper, a robust image segmentation method is proposed. The relationship between pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number, the K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori(MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is compared with other segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results shows that the proposed algorithm is the better choice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Basak, J., Chanda, B., Manjumder, D.D.: On edge and line linking with connectionist models. IEEE Trans. Systems, Man Cybernet. 24(3), 413–428 (1994)
Hojjatoleslami, S.A., Kittler, J.: Region growing: a new approach. IEEE Trans. Image Process. 7(7), 1079–1084 (1998)
Tu, Z., Zho, S.-C.: Image Segmentation By Data-Driven Markov Chain Monte Carlo. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 657–673 (2002)
Won, C.S., Derin, H.: Unsupervised segmentation of noisy and textured images using Markov random fields. CVGIP: Graphical Models Image Process. 54(4), 308–328 (1992)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
Lei, T.: Gibbs ringing artifact spatial correlation and spatial correlation in MRI. In: SPIE Proceedings, vol. 5368, pp. 837–847 (2004)
Besag, J.: Towards Bayesian image analysis. Journal of Applied Statistics 16, 395–407 (1989)
H urn, M.A., Mardia, K.V., et al.: Bayesian fused classification of medical images. IEEE Trans. Med. Imag. 15(6), 850–858 (1996)
Gath, I., Geva, A.B.: Fuzzy clustering for the estimation of the parameters of the components of mixtures of normal distributions. Pattern Recognition Letters 9(3), 77–78 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hou, Y., Guo, L., Lun, X. (2006). An Efficient Unsupervised MRF Image Clustering Method. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_164
Download citation
DOI: https://doi.org/10.1007/978-3-540-36668-3_164
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
eBook Packages: Computer ScienceComputer Science (R0)