Variational bayes inference of spatial mixture models for segmentation

IEEE Transactions on Medical Imaging

Volumn 25, Issue 10, 2006, Pages 1380-1391

  Woolrich, Mark W ^a   Behrens, Timothy E ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MEDICAL IMAGING; MONTE CARLO METHODS; STATISTICAL METHODS;

BAYES INFERENCE; HISTOGRAM INFORMATION; SPATIAL CHARACTERISTICS,;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 33947193059     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2006.880682     Document Type: Article

References (28)

1. W. Wells, W. Crimson, R. Kikinis, and F. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429-442, Aug. 1996.
2. R. Guillemaud and M. Brady, "Estimating the bias field of MR images," IEEE Trans. Med. Imag., vol. 16, no. 3, pp. 238-251, Jun. 1997.
3. K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, and H.-W. Müller-Gärtner, "Markov random field segmentation of brain MR images," IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 878-886, Dec. 1997.
4. Y. Zhang, M. Brady, and S. Smith, "Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm," IEEE Trans. Med. Imag., vol. 20, no. 1, pp. 45-57, Jan. 2001.
5. S. Rattanasiri, D. Bohning, P. Rojanavipart, and S. Athipanyakom, "A mixture model application in disease mapping of malaria," Southeast Asian J. Trop. Med. Public Health, vol. 35, no. 1, pp. 38-47, 2004.
6. E. Salli, A. Visa, H. Aronen, A. Korvenoja, and T. Katila, "Statistical segmentation of FMRI activations using contextual clustering," in P roc. Med. Image Comput. Computer-Assisted Intervention, 1999, pp. 481-488.
7. M. Svensén, F. Kruggel, and D. von Cramon, "Probabilistic modeling of single-trial fmri data," IEEE Trans. Med. Imag., vol. 19, no. 1, pp. 25-35, Jan. 2000.
8. J. Marroquin, E. Arce, and S. Botello, "Hidden markov measure field models for image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 11, pp. 1380-1387, Nov. 2003.
9. M. Woolrich, T. Behrens, C. Beckmann, and S. Smith, "Mixture models with adaptive spatial regularisation for segmentation with an application to FMRI data," IEEE Trans. Med. Imag., vol. 24, no. 1, pp. 1-11, Jan. 2005.
10. J. Besag, "On the statistical analysis of dirty pictures," J. Roy. Statist. Soc., vol. 3, pp. 259-302, 1986.
11. S. Geman and D. Geman, "Stochastic relaxation, gibbs distributions, and the bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Inlell., vol. PAMI-6, no. 6, pp. 721-741, 1984.
12. N. Cressie, Statistics for Spatial Data. New York: Wiley, 1993.
13. P. Santago and H. Gage, "Statistical models of partial volume effect," IEEE Trans. Med. Imag., vol. 11, no. 4, pp. 1531-1540, Nov. 1995.
14. S. Ruan, C. Jaggi, J. Xue, J. Fadili, and D. Bloyet, "Brain tissue classification of magnetic resonance images using partial volume modeling," IEEE Trans. Med. Imag., vol. 19, no. 12, pp. 1179-1187, Dec. 2000.
15. H. Caillol, W. Pieczynski, and A. Million, "Estimation of fuzzy gaussian mixture and unsupervised statistical image segmentation," IEEE Trans. Med. Imag., vol. 6, no. 3, pp. 425-440, Mar. 1997.
16. K. Friston, A. Holmes, K. Worsley, J.-B. Poline, C. Frith, and R. Frackowiak, "Statistical parametric maps in functional imaging: A general linear approach," Hum. Brain Mapp., vol. 2, pp. 189-210, 1995.
17. M. Woolrich, B. Ripley, J. Brady, and S. Smith, "Temporal autocorrelation in univariate linear modelling of FMRI data," NeuroImage, vol. 14, no. 6, pp. 1370-1386, 2001.
18. B. Everitt and E. Bullmore, "Mixture model mapping of brain activation in functional magnetic resonance images," Hum. Brain Mapp., vol. 7, pp. 1-14, 1999.
19. N. Hartvig and J. Jensen, "Spatial mixture modelling of fMRI data," Hum. Brain Mapp., vol. 11, no. 4, pp. 233-248, 2000.
20. W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London, U.K.: Chapman & Hall, 1996.
21. D. Gamerman, Markov Chain Monte Carlo. London, U.K.: Chapman and Hall, 1997.
22. M. Jordan, Learning in Graphical Models. Cambridge, MA: MIT Press, 1999.
23. and H. Attias, A Variational Bayesian framework for graphical models 2000 [Online]. Available: citeseer.ist.psu.edu/altias00variational.html
24. W. Penny, S. Kiebel, and K. Friston, "Variational Bayesian inference for fMRI time series," NeuroImage, vol. 19, no. 3, pp. 1477-1491, 2003.
25. M. Woolrich, T. Behrens, and S. Smith, "Constrained linear basis sets for HRF modelling using variational Bayes," NeuroImage, vol. 21, no. 4, pp. 1748-1761, 2004.
26. W. Penny, N. Trujillo-Barreto, and K. Friston, "Bayesian fMRI time series analysis with spatial priors," NeuroImage, vol. 24, no. 2, pp. 350-362, 2005.
27. C. Beckmann, M. Woolrich, and S. Smith, "Gaussian/Gamma mixture modelling of ICA/GLM spatial maps," in Proc. 9th Int. Conf. Functional Map. Hum. Brain, 2003, vol. 19, no. 2, p. S985.
28. M. Jenkinson, P. Bannister, J. Brady, and S. Smith, "Improved optimisation for the robust and accurate linear registration and motion correction of brain images," NeuroImage, vol. 17, no. 2, pp. 825-841, 2002.