A finite mixture model for image segmentation

Statistics and Computing

Volumn 18, Issue 2, 2008, Pages 137-150

  Alfò, Marco ^a   Nieddu, Luciano ^b   Vicari, Donatella ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b University 'S Pio V'   (Italy)

Author keywords

Finite mixtures; Gibbs distribution; Image segmentation; Maximum likelihood; Mean Field approximation

Indexed keywords

EID: 41549139266     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-007-9044-9     Document Type: Article

References (31)

1. Aykroyd, R.G., Zimeras, S.: Inhomogeneous prior models for image reconstruction. J. Am. Stat. Assoc. 94, 934-946 (1999)
2. Besag, J.: On statistical analysis of dirty pictures. J. R. Stat. Soc. B 48(3), 259-302 (1986)
3. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
4. Blekas, K., Likas, A., Galatsanos, N.P., Lagaris, I.E.: A spatially constrained mixture model for image segmentation. IEEE Trans. Neural Netw. 16(2), 494-498 (2005)
5. Cadez, I., Smyth, P.: Modeling of inhomogeneous Markov random fields with applications to cloud screening. Technical Report, Information and Computer Science Department, University of California, Irvine (1999)
6. Celeux, G., Forbes, F., Peyrard, N.: EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognit. 36(1), 131-144 (2003)
7. Chen, K., Wang, D., Liu, X.: Weight adaptation and oscillatory correlation for image segmentation. IEEE Trans. Neural Netw. 11, 1106-1123 (2000)
8. Clark, M.C., Hall, L.O., Golgof, D.B., Clark, L.P., Velthuizen, R.P., Silbiger, M.S.: MRI segmentation using fuzzy clustering techniques. IEEE Eng. Med. Biol. 13(5), 730-742 (1994)
9. Clausi, D.A.: K-means iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation. Pattern Recognit. 35, 1959-1972 (2002)
10. Diplaros, A., Vlassis, N., Gevers, T.: A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Trans. Neural Netw. 18(3), 798-808 (2007)
11. Dunmur, A.P., Titterington, D.M.: Mean fields and two-dimensional Markov random fields in image analysis. Pattern Anal. Appl. 1, 248-260 (1998)
12. Forbes, F., Peyrard, N.: Hidden Markov random field model selection criteria based on mean field-like approximations. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1089-1101 (2003)
13. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721-741 (1984)
14. Green, P.J., Richardson, S.: Hidden Markov models and disease mapping. J. Am. Stat. Assoc. 97(460), 1-16 (2002)
15. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 1(2), 193-218 (1985)
16. Jain, A.K., Duin, R.P.W., Mao, J.C.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22, 4-37 (2000)
17. Kim, S., McLachlan, G.J., Ng, S., Wang, D.: Segmentation of brain MR images with bias field correction. In: APRS Workshop on Digital Image Computing, University of Queensland, Australia (2003)
18. Kindermann, R., Snell, J.L.: Markov Random Fields and Their Applications, vol. 1. American Mathematical Society, Providence (1999)
19. McLachlan, G.J., Peel, D.: Finite Mixture Models. Wiley, New York (2000)
20. Ng, S., McLachlan, G.J.: Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognit. 37, 1573-1589 (2004)
21. Pal, N., Pal, S.: A review of image segmentation techniques. Pattern Recognit. 26, 1277-1294 (1993)
22. Pappas, T.N.: An adaptive clustering algorithm for image segmentation. IEEE Trans. Signal Process. 40(4), 901-914 (1992)
23. Qian, W., Titterington, D.M.: Estimation of parameters in hidden Markov models. Philos. Trans. R. Stat. Soc. Ser. A 337, 407-428 (1991)
24. Raftery, A.E.: Approximate Bayes factors for image segmentation: the pseudo-likelihood information criterion (PLIC). IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1517-1520 (2002)
25. Sahoo, P.K., Soltani, S., Wong, A., Chen, Y.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41, 233-260 (1988)
26. Sanjay-Gopal, S., Hebert, T.J.: Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm. IEEE Trans. Image Process. 7, 1014-1028 (1998)
27. Strauss, D.J.: Clustering on coloured lattices. J. Appl. Probab. 14(1), 135-143 (1977)
28. Wezka, J.S.: A survey of threshold selection techniques. Comput. Vis. Graph. Image Process. 7, 259-265 (1978)
29. Zhang, J.: The mean field theory in EM procedure for Markov random fields. IEEE Trans. Signal Process. 40(10), 2570-2583 (1992)
30. Zhang, J.: The mean field theory in EM procedure for blind Markov random field image restoration. IEEE Trans. Image Process. 2(1), 27-40 (1993)
31. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20 (2001)