Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

IEEE Transactions on Medical Imaging

Volumn 25, Issue 9, 2006, Pages 1233-1245

  Greenspan, Hayit ^a   Ruf, Amit ^a   Goldberger, Jacob ^b  

^a TEL AVIV UNIVERSITY   (Israel)

^b BAR ILAN UNIVERSITY   (Israel)

Author keywords

Constrained model; Expectation maximization (EM); Gaussian mixture model (GMM); Image segmentation; Magnetic resonance imaging (MRI) brain segmentation; Mixture of gaussians

Indexed keywords

ALGORITHMS; BRAIN; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; MEDICAL PROBLEMS; TISSUE;

CONSTRAINED MODEL; EXPECTATION-MAXIMIZATION (EM); GAUSSIAN MIXTURE MODEL (GMM); MAGNETIC RESONANCE IMAGING (MRI) BRAIN SEGMENTATION; MIXTURE OF GAUSSIANS;

IMAGE SEGMENTATION;

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

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

EID: 33748123943     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2006.880668     Document Type: Article

References (28)

1. N. Aspert, D. Santa-Cruz, and T. Ebrahimi, "Mesh: Measuring errors between surfaces using the Hausdorff distance," in Proc. IEEE Int. Conf. Multimedia Expo (TCME'02), 2002, vol. 1, pp. 705-708.
2. C. A. Cocosco, V. Kollokian, R. K. Kwan, and A. C. Evans, "Brainweb: Online interface to a 3-D MRI simulated brain database," NeuroImage, vol. 5, p. 425, 1997.
3. Center for Morphometric Analysis, IBSR Internet Brain Segmentation Repository Charlestown, MA, 2000 [Online]. Available: http://neuro-www.mgh. harvard.edu/cma/ibsr/
4. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc., no. 39, pp. 1-38, 1977.
5. L. R. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.
6. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York: Wiley, 2001.
7. G. Dugas-Phocion, M. A. G. Ballester, G. Malandain, C. Lebrun, and N. Ayache, "Improved EM-based tissue segmentation and partial volume effect quantification in multi-sequence brain MRI," in Proc. Int. Conf. Med. Image Comput. Comput. Assisted Intervention (MICCAI), 2004, pp. 26-33.
8. G. Gerig, M. Jomier, and M. Chakos, "Valmet: A new validation tool for assessing and improving 3-D object segmentation," in Proc. Int. Conf. Med. Image Comput. Comput. Assisted Intervention (MICCAI), 2001, vol. 2208, pp. 516-528.
9. K. Held, E. R. Kops, B. J. Krause, W. M. Wells, III, R. Kikinis, and H. Müller-Gärtner, "Markov random field segmentation of brain MR images," IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 878-886, Dec. 1997.
10. K. V. Leemput, Expectation-Maximization Segmentation (EMS), Medical Image Computing, Leuven, Belgium, 2001 [Online]. Available: http://www. medicalimagecomputing.com/EMS/,
11. T. Kapur, W. E. Grimson, W. M. Wells, and R. Kikinis, "Segmentation of brain tissue from magnetic resonance images," Med. Image Anal., vol. 1, no. 2, pp. 109-127, 1996.
12. K. V. Leemput, F. Maes, D. Vandermeulen, A. C. F. Colchester, and P. Suetens, "Automated segmentation of multiple sclerosis lesions by model outlier detection," IEEE Trans. Med. Imag., vol. 20, no. 8, pp. 677-688, Aug. 2001.
13. K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based tissue classification of MR images of the brain," IEEE Trans. Med. Imag., vol. 18, no. 10, pp. 897-908, Oct. 1999.
14. _, "A unifying framework for partial volume segmentation of brain MR images," IEEE Trans. Med. Imag., vol 22, no. 1, pp. 105-119, Jan. 2003.
15. S. Z. Li, Markov Random Field Modeling in Computer Vision. New York: Springer-Verlag, 1995.
16. A. Macovski, "Noise in MRI," Magn. Reson. Med., vol. 36, no. 3, pp. 494-497, 1996.
17. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multimodality image registration by maximization of mutual information," IEEE Trans. Med. Imag., vol. 16, no. 2, pp. 187-198, Apr. 1997.
18. J. L. Marroquin, B. C. Vemuri, S. Botello, F. Calderon, and A. Fernandez-Bouzas, "An accurate and efficient bayesian method for automatic segmentation of brain MRI," IEEE Trans. Med. Imag., vol. 21, no. 8, pp. 934-945, Aug. 2002.
19. T. McInerney and D. Terzopoulos, "Medical image segmentation using topologically adaptable surfaces," in Proc. 1st Joint Conf. Comput. Vision Virtual Reality and Robotics in Medicine, and Medial Robotics and Computer-Assisted Surgery, 1997, pp. 23-32.
20. D. L. Pham, "Spatial models for fuzzy clustering," Comput. Vision Image Understand., vol. 84, no. 2, pp. 285-297, 2001.
21. D. L. Pham, C. Xu, and J. L. Prince, "Current methods in medical image segmentation," Ann. Rev. Biomed. Eng., vol. 2, pp. 315-337, 2000.
22. M. Prastawa, J. Gilmore, W. Lin, and G. Gerig, "Automatic segmentation of neonatal brain MRI," in Proc. Int. Conf. Med. Image Comput. Comput. Assisted Intervention (MICCAI), 2004, pp. 10-17.
23. T. Rohlfing and C. R. Maurer, Jr., "Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees," IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 1, pp. 16-25, Mar. 2003.
24. A. Ruf, H. Greenspan, and J. Goldberger, "Tissue classification of noisy MR brain images using constrained GMM," in Int. Conf. Med. Image Comput. Comput. Assisted Intervention (MICCAI), 2005, pp. 790-797.
25. W. Wells, R. Kikinis, W. Grimson, and F. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429-442, Aug. 1996.
26. Z. Xue, D. Shen, and C. Davatzikos, "Classic: Consistent longitudinal alignment and segmentation for serial image computing," Inf. Process. Med. Imag., vol. 3565, pp. 101-113, Jul. 2005.
27. 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.
28. A. P. Zijdenbos and B. M. Dawant, "Brain segmentation and white matter lesion detection in MR images," Crit. Rev. Biomed. Eng., vol. 22, no. 5-6, pp. 401-465, 1994.