A robust medical image segmentation method using KL distance and local neighborhood information

Computers in Biology and Medicine

Volumn 43, Issue 5, 2013, Pages 459-470

  Zheng, Qian ^a   Lu, Zhentai ^a   Yang, Wei ^a   Zhang, Minghui ^a   Feng, Qianjin ^a   Chen, Wufan ^a  

^a SOUTHERN MEDICAL UNIVERSITY   (China)

Author keywords

Chan Vese model; Kullback Leibler distance; Local neighborhood information; Medical image segmentation

Indexed keywords

CHAN-VESE MODEL; CONTROLLING PARAMETERS; KULLBACK-LEIBLER DISTANCE; LEVEL SET SEGMENTATION; LOCAL NEIGHBORHOODS; MEDICAL IMAGE SEGMENTATION; SEGMENTATION ACCURACY; STATE-OF-THE-ART METHODS;

IMAGE SEGMENTATION;

MEDICAL IMAGE PROCESSING;

ACCURACY; ADULT; ARTICLE; BRAIN TUMOR; CHAN VESE MODEL; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER MODEL; DIAGNOSTIC IMAGING; HUMAN; IMAGE ANALYSIS; KULLBACK LEIBLER DISTANCE; LOCAL NEIGHBORHOOD INFORMATION; PERFORMANCE; PRIORITY JOURNAL;

ALGORITHMS; CLUSTER ANALYSIS; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MEDICAL INFORMATICS APPLICATIONS; MODELS, THEORETICAL;

EID: 84875941528     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.01.002     Document Type: Article

References (26)

1. Kanungo T., Mount D.M., Netanyahu N.S., et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:881-892.
2. Gath I., Geva A.B. Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11:773-781.
3. Rohlfing T., Russakoff D.B., Maurer C.R. Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans. Med. Imaging 2004, 23:983-994.
4. C. Schillo, G. Herrmann, F. Ackermann, et al., Statistical classification and segmentation of biomolecular surfaces, in: Proceedings of the International Conference on Image Processing, 3 (1995) 560-563.
5. Singh V., Marinescu D.C., Baker T.S. Image segmentation for automatic particle identification in electron micrographs based on hidden Markov and expectation maximization. J. Struct. Biol. 2004, 145:123-141.
6. Woods J.W., Dravida S., Mediavilla S.R. Image estimation using doubly stochastic Gaussian random field models. IEEE Trans. Pattern Anal. Mach. Intell. 1987, 9:245-253.
7. Kichenassamy S., Kumar A., Olver P., Tannenbaum A., Yezzi A. Conformal curvature flows: from phase transitions to active vision. Arch. Rational Mech. Anal. 1996, 134:275-301.
8. Caselles V., Kimmel R., Sapiro G. Geodesic active contours. Int. J. Comput. Vision 1997, 22:61-79.
9. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vision 1987, 1:321-331.
10. Mumford D., Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 1989, 42:577-685.
11. Chan T.F., Vese L.A. Active contours without edges. IEEE Trans. Image Process. 2001, 10:266-277.
12. S. Lankton, D. Nain, A. Yezzi, et al., Hybrid geodesic region-based curve evolutions for image segmentation, in: Proceedings of SPIE: Medical Imaging (2007).
13. Besag J. On the statistical analysis of dirty pictures. J. R. Stat. Soc. B. 1986, 48:259-302.
14. Cerny V. Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 1985, 45:41-51.
15. T. Zhang, D. Treedman, Tracking objects using density matching and shape priors, in: Proceedings of the International Conference on Computer Vision, Nice, France, IEEE (2003).
16. Paragios N., Chen,and Y., Faugeras O. Handbook of Mathematical Models in Computer Vision 2005, Springer, New York.
17. K.W. Sum, P.Y.S. Cheung, A novel active contour model using local and global statistics for vessel extraction, in: Proceedings of the English Medicine Biological Society (2006) 3126-3129.
18. Sum K.W., Cheung P.Y.S. Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans. Biomed. Eng. 2008, 55:358-360.
19. Li C.M., Kao C., Gore J., et al. Implicit Active Contours Driven by Local Binary Fitting Energy. IEEE Conference on Computer Vision and Pattern Recognition 2007, IEEE, Minneapolis, MN.
20. Lankton S., Tannenbaum A. Localizing region-based active contours. IEEE Trans. Image Process. 2008, 17(11):2029-2039.
21. Wang X.F., Huang D.S., Xu H. An efficient local Chan-Vese Model for image segmentation. Pattern Recognition 2010, 43:603-618.
22. Freedman D., Zhang T. Active contours for tracking distributions. IEEE Trans. Image Process. 2004, 13:518-526.
23. M.S. Allili, D. Ziou, A robust video object tracking by using active contours, in: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, New York, USA, IEEE (2006).
24. C. Liu, H.Y. Shum, Kullback-Leibler boosting, in: Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Wisconsin, USA, IEEE (2003).
25. Luo X., Tian J., Lin Y. An algorithm for segmentation of medical image series based on active contour model. J. Software 2002, 13:1050-1058.
26. Osher S., Sethian J.A. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation. J. Comput. Phys. 1988, 79:12-49.