Integration of Gibbs Prior models and deformable models for 3D medical image segmentation

Proceedings - International Conference on Pattern Recognition

Volumn 16, Issue 1, 2002, Pages 719-722

  Chen, Ting ^a   Metaxas, Dimitris ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY CONDITIONS; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; PARAMETER ESTIMATION; DEFORMATION; MEDICAL IMAGE PROCESSING; MEDICAL IMAGING; PATTERN RECOGNITION;

DEFORMABLE MODELS; GIBBS PRIOR MODELS; GRADIENT THRESHOLD; MARKOV RANDOM FIELD THEORY;

MEDICAL IMAGING; IMAGE SEGMENTATION;

AUTOMATED SEGMENTATION; DEFORMABLE MODELING; DEFORMABLE MODELS; GIBBS PRIOR MODELS; GRADIENT INFORMATIONS; GRADIENT THRESHOLDS; MARKOV RANDOM FIELD MODEL; MARKOV RANDOM FIELD THEORY;

EID: 33751557424     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (11)

1. D. H. Ballard and C. M. Brown. Computer vision. Prentice Hall, 1982
2. M. Kass, A. Witkin, and D. Trerzopoulos. Snakes: Active contour models. Intl. J. of Computer Vision, 1(4):321-331, 1988.
3. M. Worring, A. W. M. Smeulders, L. H. Staib, and J. S. Duncan. Parameterized feasible boundaries in gradient vector fields. Computer Vision and Image Understanding, 63(1):135-144, 1996
4. T. N. Jones and D. N. Metaxas. Automated 3D segmentation using deformable models and fuzzy affinity. In J. Duncan and G. Gindi, editors, Lecture Notes in computer Science, Volume 1230: IPMI '97, pages 113-126. Springer, 1997
5. M. T. Chan, G. T. Herman, Emanuel Levitan. A Bayesian Approach to PET Reconstruction Using Image-Modeling Gibbs Prior. IEEE Transaction on Nuclear Science, Vol. 44, No. 3, June 1997
6. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721-741, 1984
7. D.N. Metaxas. Physics-Based Deformable Models: Application to Computer Vision, Graphics and Medical Imaging. 1996.
8. Gerhand Winkler Image Anlysis, random Fields and Dynamic Monte Carlo Methods, 1995 Springer
9. Hammersley J. M. and Clifford P.: Markov fields on finite graphs and lattices. Preprint University of California, Berkeley.
10. L. D. Cohen, ON active contour models and balloons. CVGIP: Image understanding, 53(2):211-218:1991.
11. T. Chen and D. Metaxas: Image Segmentation Based on the Integration of Markov Random Fields and Deformable models. MICCAI 00.