Finding image distributions on active curves

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 3225-3232

  Ayed, Ismail Ben ^a,b   Mitiche, Amar ^c   Salah, Mohamed Ben ^c   Li, Shuo ^a,b  

^a GE HEALTHCARE   (Canada)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

^c INRS EMT   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIAC MAGNETIC RESONANCE IMAGES; CURVE EVOLUTION EQUATIONS; EDGE-BASED; FUNCTIONALS; GEODESIC ACTIVE CONTOUR; IMAGE DISTRIBUTIONS; IMAGE FEATURES; LEFT VENTRICLES; PERFORMANCE EVALUATION; REGION-BASED; SEGMENTATION AND TRACKING; WEAK IMAGE;

COMPUTER VISION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; TRACKING (POSITION);

IMAGE SEGMENTATION;

EID: 77956006195     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540069     Document Type: Conference Paper

References (20)

1. T. F. Chan and L. A. Vese. Active Contours without Edges. IEEE Trans. on Image Processing, 10(2): 266-277, 2001.
2. D. Cremers, M. Rousson, and R. Deriche. A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape. International Journal of Computer Vision, 62(3): 249-265, 2007.
3. D. Freedman and T. Zhang. Active contours for tracking distributions. IEEE Trans. on Image Processing, 13(4): 518-526, 2004.
4. I. Ben Ayed, S. Li and I. Ross. A Statistical Overlap Prior for Variational Image Segmentation. International Journal of Computer Vision, 85(1): 115-132, 2009
5. M. Rousson, and D. Cremers. Efficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation. MICCAI (2) 2005: 757-764.
6. O. V. Michailovich, Y. Rathi, and A. Tannenbaum. Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow. IEEE Trans. on Image Processing, 16(11): 2787-2801, 2007. (Pubitemid 350141851)
7. X. Xie and M. Mirmehdi. MAC: Magnetostatic Active Contour Model. IEEE Trans. Pattern Anal. Mach. Intell., 30(4): 632-646, 2008.
8. V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. International Journal of Computer Vision, 22(1): 61-79, 1997.
9. C. Li, C. Xu, C. Gui, and M. D. Fox. Level Set Evolution Without Re-initialization: A New Variational Formulation. CVPR 2005.
10. N. Paragios, O. Mellina-Gottardo, and V. Ramesh. Gradient Vector Flow Fast Geometric Active Contours. IEEE Trans. Pattern Anal. Mach. Intell., 26(3):402-407, 2004.
11. S. Kichenassamy, A. Kumar, P.J. Olver, A. Tannenbaum, and A. J. Yezzi, Gradient flows and geometric active contour models. ICCV 1995, pp. 810-815.
12. N. Paragios and R. Deriche. Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. International Journal of Computer Vision, 46(3): 223-247, 2002.
13. X. Bresson, S. Esedoglu, P. Vandergheynst, J. Thiran and S. Osher Fast Global Minimization of the Active Contour/Snake Model. Journal of Mathematical Imaging and Vision, 28(2): 151-167, 2007
14. R. Kimmel, A. M. Bruckstein, Regularized Laplacian Zero Crossings as Optimal Edge Integrators. International Journal of Computer Vision, 53(3): 225-243, 2003
15. J.Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press, 1999.
16. Y. Boykov, and G. Funka-Lea. Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision, 70(2), 109-131, 2006. (Pubitemid 44127252)
17. I. Ben Ayed, Y. Lu, S. Li, I. Ross. Left Ventricle Tracking Using Overlap Priors. MICCAI (1) 2008: 1025-1033
18. M. Lynch, O. Ghita, and P. F.Whelan. Segmentation of the Left Ventricle of the Heart in 3-D+t MRI Data Using an Optimized Nonrigid Temporal Model. IEEE Trans. on Medical Imaging, 27(2): 195-203, 2008.
19. C. Pluempitiwiriyawej, J.M.F. Moura, Yi-Jen LinWu and Chien Ho. STACS: new active con- tour scheme for cardiac MR image segmentation. IEEE Trans. on Medical Imaging, 24 (5): 593-603, 2005.
20. X. Papademetris, A. Sinusas, D. Dione, R. Constable, and J. Duncan. Estimation of 3-D left ventricular deformation from medical images using biomechanical models. IEEE Trans. on Medical Imaging, 21(7): 786-800, 2002.