Abstract
On the basis of Markov Random Field (MRF), which uses context information, in this paper, a robust image segmentation method is proposed. The relationship between observed pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function, which described the probability of pixels being classified into one class. 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 adopted with K-means, traditional Expectation-Maximization (EM) and MRF image segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results and the histogram of signal to noise ratio (SNR)-miss classification ratio (MCR) showed that the proposed algorithm is the better choice.
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References
Wezka, J.S.: A survey of threshold selection techniques. Comput. Vision, Graphics Image Process. 7, 259–265 (1978)
Sahoo, P.K., Soltani, S., Wong, A., Chen, Y.: A survey of thresholding techniques. Comput. Vision, Graphics Image Process 41, 233–260 (1988)
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)
Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, New York (2001)
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)
Barker, S.A.: Image segmentation using Markov random field models, Ph.D. Thesis, University of Cambridge (1998)
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)
Hurn, 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)
Lei, T., Udupa, J.: Performance evaluation of finite normal mixture model-based image seg-mentation Techniques. IEEE Trans. Image Processing 12(10), 1153–1169 (2003)
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Hou, Y., Guo, L., Lun, X. (2006). An Efficient Unsupervised MRF Image Clustering Method. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_3
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DOI: https://doi.org/10.1007/11893004_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
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