MR brain tissue classification using an edge-preserving spatially variant bayesian mixture model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5241 LNCS, Issue PART 1, 2008, Pages 43-50

  Sfikas, Giorgos ^a,c   Nikou, Christophoros ^a   Galatsanos, Nikolaos ^b   Heinrich, Christian ^c  

^a UNIVERSITY OF IOANNINA   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c Faculté de Médecine   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MIXTURE MODEL; BAYESIAN MODEL; BERNOULLI RANDOM VARIABLES; BRAIN IMAGES; BRAIN SEGMENTATION; BRAIN TISSUE; CLOSED FORM; EDGE PRESERVING; GAUSSIAN MIXTURE MODEL; LINE PROCESS; MODEL PARAMETERS; NUMERICAL EXPERIMENTS; SPATIALLY CONSTRAINED MIXTURE MODEL; STATE OF THE ART; STATE-OF-THE-ART METHODS;

BAYESIAN NETWORKS; BLIND SOURCE SEPARATION; COMPUTER SCIENCE; IMAGE SEGMENTATION; LABELS; MIXTURES; NUMERICAL METHODS; RANDOM VARIABLES;

MEDICAL COMPUTING;

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

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

EID: 58849123436     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85988-8_6     Document Type: Conference Paper

References (24)

1. Kapur, T., Grimson, W.E.L., Wells III, W.M., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Medical Image Analysis 1, 109-127 (1996)
2. Wells III, W., Grimson, W., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15, 429-442 (1996)
3. Marroquin, J., Vemuri, B., Botello, S., Calderon, F., Fernandez-Bouzas, A.: An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Transactions on Medical Imaging 21, 934-945 (2002)
4. Prastawa, M., Gilmore, J., Lin, W., Gerig, G.: Automatic segmentation of neonatal brain MRI. In: Barillot, C. Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 10-17. Springer, Heidelberg (2004)
5. Tasdizen, T., Awate, S., Whitaker, R., Foster, N.: MRI tissue classification with neighborhood statistics: a nonparametric entropy-minimizing approach. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 517-525. Springer, Heidelberg (2005)
6. Pohl, K., Bouix, S., Nakamura, M., Rohfling, T., McCarley, R., Kikinis, R., Grimson, L., Shenton, M., Wells, W.: A hierarchical algorithm for MR brain image parcellation. IEEE Transactions on Medical Imaging 26, 1201-1212 (2007)
7. Niessen, W.J., Vincken, K.L., Weickert, J., Viergever, M.A.: Three dimensional MR brain segmentation. In: Proceedings of the 6th International Conference on Computer Vision (ICCV 1998), pp. 53-58 (1998)
8. Laidlaw, D.H., Fleischer, K.W., Barr, A.H.: Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging 17(1), 74-86 (1998)
9. Kapur, T., Grimson, W.E.L., Kikinis, R., Wells III, W.M.: Enhanced spatial priors for segmentation of magnetic resonance imagery. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 457-468. Springer, Heidelberg (1998)
10. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18, 897-908 (1999)
11. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Transactions on Medical Imaging 20, 677-688 (2001)
12. Greenspan, H., Ruf, A., Goldberger, J.: Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Transactions on Medical Imaging 25, 1233-1245 (2006)
13. Peng, Z., Wee, W., Lee, J.H.: Automatic segmentation of MR brain images using spatial-varying Gaussian mixture and Markov random field approach. In: Proceedings of the Computer Vision and Pattern Recognition Workshop (CVPRW 2006) (2006)
14. Awate, S.P., Tasdizen, T., Foster, N., Whitaker, R.: Adaptive Markov modeling for mutual information based, unsupervised MRI brain tissue classification. Medical Image Analysis 10, 726-739 (2006)
15. Awate, S.P., Zhang, H., Gee, J.C.: A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI tract extraction. IEEE Transactions on Medical Imaging 26, 1525-1536 (2007)
16. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
17. Nikou, C., Galatsanos, N., Likas, A.: A class-adaptive spatially variant mixture model for image segmentation. IEEE Transactions on Image Processing 16(4), 1121-1130 (2007)
18. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 721-741 (1984)
19. Molina, R., Mateos, J., Katsaggelos, A., Vega, M.: Bayesian multichannel image restoration using compound Gauss-Markov random fields. IEEE Transactions on Image Processing 12, 1642-1654 (2003)
20. Kanemura, A., Maeda, S., Ishii, S.: Hyperparameter estimation in Bayesian image superresolution with a compound Markov random field prior. In: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2007), Thessaloniki, Greece (2007)
21. Blekas, K., Likas, A., Galatsanos, N., Lagaris, I.: A spatially constrained mixture model for image segmentation. IEEE Transactions on Neural Networks 16(2), 494-498 (2005)
22. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
23. Collins, D., Zijdenbos, A., Kollokian, V., Sled, J., Kabani, N., Holmes, C., Evans, A.: Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17(3), 463-468 (1998)
24. Kwan, R., Evans, A., Pike, G.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18, 1085-1097 (1999)