Fast Chan-Vese segmentation algorithm based on image entropy

Tongji Daxue Xuebao/Journal of Tongji University

Volumn 39, Issue 5, 2011, Pages 738-744

  Chen, Yufei ^a   Wu, Qidi ^a   Zhao, Weidong ^a   Wang, Zhicheng ^a  

^a TONGJI UNIVERSITY   (China)

Author keywords

Chan Vese model; Image entropy; Image segmentation; Level set method

Indexed keywords

CHAN-VESE MODEL; EVOLUTIONARY STABILITY; IMAGE DETECTION; IMAGE ENTROPY; INFRARED IMAGES; LEVEL SET METHOD; SEGMENTATION ALGORITHMS; SPEED-UPS;

ALGORITHMS; ENTROPY; INFRARED IMAGING; NUMERICAL METHODS;

IMAGE SEGMENTATION;

EID: 79959287902     PISSN: 0253374X     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.0253-374x.2011.05.020     Document Type: Article

References (8)

1. Raut S, Raqhuvanshi M, Dharaskar R, et al. Image segmentation-a state-of-art survey for prediction [C]//Proceedings of International Conference on Advanced Computer Control. Singapore: IEEE Computer Society, 2009: 420-424.
2. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models[J]. International Journal of Computer Vision, 1988, 4(1): 321.
3. Osher S, Sethian J A. Fronts propagating with curvature-dependent speed[J]. Journal of Computational Physics, 1988, 79(1): 12.
4. GONG Yongyi, LUO Xiaonan, HUANG Hui, et al. Multi-objects extracted based on single level set[J]. Chinese Journal of Computers, 2007, 30(1): 120.
5. Chan T F, Vese L A. Active contours without edges[J]. IEEE transactions on Image Processing, 2001, 10(2): 266.
6. Chen Y, Zhao W, Wang Z. Level set segmentation algorithm based on image entropy and simulated annealing [C]//Proceedings of International Conference on Bioinformatics and Biomedical Engineering. Wuhan: IEEE Computer Society, 2007: 999-1003.
7. Li C, Xu C, Gui C. et al. Level set evolution without re-initialization: a new variational formulation[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE Computer Society, 2005: 430-436.
8. Li C, Kao C Y, Gore J C, et al. Implicit active contours driven by local binary fitting energy [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE Computer Society, 2007: 1-7.