An efficient local Chan-Vese model for image segmentation

Pattern Recognition

Volumn 43, Issue 3, 2010, Pages 603-618

  Wang, Xiao Feng ^a,b,c   Huang, De Shuang ^a   Xu, Huan ^a,b  

^a INSTITUTE OF INTELLIGENT MACHINES   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c HEFEI UNIVERSITY   (China)

Author keywords

Extended structure tensor; Image segmentation; Intensity inhomogeneity; Level set method; Local Chan Vese model

Indexed keywords

CHAN-VESE MODEL; CLASSICAL STRUCTURES; CURVE EVOLUTION; ENERGY FUNCTIONALS; EXTENDED STRUCTURES; GOVERNING PARAMETERS; IMAGE INFORMATION; INITIAL CONTOUR; INTENSITY INFORMATION; INTENSITY INHOMOGENEITY; ITERATION PROCESS; ITERATION TIME; LEVEL SET METHOD; LEVEL-SET EVOLUTION; LOCAL TERMS; REAL IMAGES; REINITIALIZATION; STATISTICAL FUNCTIONS; TERMINATION CRITERIA; TEXTURE IMAGE; TEXTURE IMAGE SEGMENTATION; THREE-TERM;

DIGITAL IMAGE STORAGE; DROP BREAKUP; EVOLUTIONARY ALGORITHMS; LEVEL MEASUREMENT; LIGHT WEIGHT VEHICLES; OPTICAL TOMOGRAPHY; TENSORS; TEXTURES;

IMAGE SEGMENTATION;

EID: 70449699693     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.08.002     Document Type: Article

References (43)

1. Linda G.S., and George C.S. Computer Vision (2001), Prentice-Hall, New Jersey
2. Kass M., Witkin A., and Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vision 1 4 (1987) 321-331
3. Osher S., and Sethian J.A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79 1 (1988) 12-49
4. Gelas A., Bernard O., Friboulet D., and Prost R. Compactly supported radial basis functions based collocation method for level-set evolution in image segmentation. IEEE Trans. Image Process. 16 7 (2007) 1873-1887
5. Y.H. Tsai, S. Osher, Total variation and level set based methods in image science, Acta Numer. (2005) 1-61.
6. Caselles V., Catte F., Coll T., and Dibos F. A geometric model for active contours in image processing. Numer. Math. 66 1 (1993) 1-31
7. Caselles V., Kimmel R., and Sapiro G. Geodesic active contours. Int. J. Comput. Vision 22 1 (1997) 61-79
8. Malladi R., Sethian J.A., and Vemuri B.C. Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17 2 (1995) 158-175
9. Mumford D., and Shah J. Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42 (1989) 577-685
10. Chan T.F., and Vese L.A. Active contours without edges. IEEE Trans. Image Process. 10 2 (2001) 266-277
11. Tsai A., Yezzi A., and Willsky A.S. Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Trans. Image Process. 10 8 (2001) 1169-1186
12. Paragios N., and Deriche R. Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comput. Vision 46 3 (2002) 223-247
13. Gao S., and Bui T.D. Image segmentation and selective smoothing by using Mumford-Shah model. IEEE Trans. Image Process. 14 10 (2005) 1537-1549
14. Vese L.A., and Chan T.F. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vision 50 3 (2002) 271-293
15. J.E. Solem, N.C. Overgaard, A. Heyden, Initialization techniques for segmentation with the Chan-Vese model, in: Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), vol. 2, 2006, pp. 171-174.
16. R.B. Xia, W.J. Liu, J.B. Zhao, L. Li, An optimal initialization technique for improving the segmentation performance of Chan-Vese model, in: Proceedings of the IEEE International Conference on Automation and Logistics, 2007, pp. 411-415.
17. Y. Shi, W. Karl, A fast level set method without solving PDES, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'05), vol. 2, 2005, pp. 97-100.
18. Y. Pan, J.D. Birdwell, S.M. Djouadi, Efficient implementation of the Chan-Vese models without solving PDEs, in: Proceedings of the International Workshop On Multimedia Signal Processing, 2006, pp. 350-354.
19. C.M. Li, C.Y. Xu, C.F. Gui, M.D. Fox, Level set formulation without Re-initialization: a new variational formulation, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2005, vol. 1, 2005, pp. 430-436.
20. Pi L., Shen C.M., Li F., and Fan J.S. A variational formulation for segmenting desired objects in color images. Image Vision Comput. 25 9 (2007) 1414-1421
21. Sum K., and Cheung P. Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans. Biomed. Eng. 55 1 (2008) 358-360
22. Brox T., and Cremers D. On the statistical interpretation of the piecewise smooth Mumford-Shah functional. Proc. Scale Space Var. Met. Comput. Vision 4485 (2007) 203-213
23. Piovano J., Rousson M., and Papadopoulo T. Efficient segmentation of piecewise smooth images. Proc. Scale Space Var. Met. Comput. Vision 4485 (2007) 709-720
24. An J., Rousson M., and Xu C. γ-Convergence approximation to piecewise smooth medical image segmentation. Proc. Med. Imag. Comput. Comp. Assist. Interven. 4792 (2007) 495-502
25. S. Lankton, D. Nain, A. Yezzi, . Tannenbaum, Hybrid geodesic region-based curve evolutions for image segmentation, in: Proceedings of the SPIE: Medical Imagining, vol. 6510, 2007, pp. 65104U.
26. Lankton S., and Tannenbaum A. Localizing region-based active contours. IEEE Trans. Image Process. 17 11 (2008) 2029-2039
27. C.M. Li, C.Y. Kao, J.C. Gore, Z.H. Ding, Implicit active contours driven by local binary fitting energy, in: Proceedings of the CVPR'07, 2007, pp. 1-7.
28. G. Aubert, M. Barlaud, O. Faugeras, S. Jehan-Besson, Image segmentation using active contours: calculus of variations of shape gradients, Research Report, INRIA, July 2002.
29. B. Sandberg, T. Chan, L. Vese, A level-set and gabor-based active contour algorithm for segmenting textured images, Technical Report 39, Mathematical Department, UCLA, Los Angeles, USA, July 2002.
30. M. Rousson, T. Brox, R. Deriche, Active unsupervised texture segmentation on a diffusion based feature space, in: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03), vol. 2, 2003, pp. 699-7-4.
31. J. Bigun, G.H. Grandlund, Optimal orientation detection of linear symmetry, in: Proceedings of the IEEE First International Conference on Computer Vision (ICCV), 1987, pp. 433-438.
32. Bigun J., Grandlund G.H., and Wiklund J. Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 13 8 (1991) 775-790
33. C. Feddern, J. Weickert, B. Burgeth, Level-set methods for tensor-valued images, in: Proceedings of the Second IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, 2003, pp. 65-72.
34. Z. Wang, B. Vemuri, Tensor field segmentation using region based active contour model, in: Proceedings of the Eighth European Conference on Computer Vision (ECCV'04), Springer Lecture Notes in Computer Science, vol. 3024, 2004, pp. 304-315.
35. S.M. Lee, A.L. Abott, N.A. Clark, P.A. Araman, Active contours on statistical manifolds and texture segmentation, in: Proceedings of the IEEE International Conference on Image Processing (ICIP), vol. 3, 2005, pp. 828-831.
36. Maroulis D.E., Savelonas M.A., Iakovidis D.K., Karkanis S.A., and Dimitropoulos N. Variable background active contour model for computer-aided delineation of nodules in thyroid ultrasound images. IEEE Trans. Inf. Technol. Biomed. 11 5 (2007) 537-543
37. Vovk U., Pernuš F., and Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26 3 (2007) 405-421
38. Hou Z.J. A review on MR image intensity inhomogeneity correction. Int. J. Biomed. Imaging 2006 (2006) 1-11
39. Sussman M., and Fatemi E. An efficient, interface preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM J. Sci. Comput. 20 (1999) 1165-1191
40. Gomes J., and Faugeras O. Reconciling distance functions and level sets. J. Visual Commun. Imaging Representation 1 11 (2000) 209-222
41. Adalsteinsson D., and Sethian J.A. A fast level set method for propagating interface. J. Comput. Phys. 118 (1995) 269-277
42. Sethian J.A. Level Set Methods and Fast Marching Methods. second ed. (1999), Cambridge University Press, Cambridge
43. Brox T., Weickert J., Burgeth B., and Mrázek P. Nonlinear structure tensors. Image Vis. Comput. 24 1 (2006) 41-55