Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Fast and Regularization less Active Contour

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 6
Year of Publication: 2012
Rahul Patel
Hiren Mewada
Suprava Patnaik

Rahul Patel, Hiren Mewada and Suprava Patnaik. Article: Fast and Regularization less Active Contour. International Journal of Computer Applications 47(6):26-31, June 2012. Full text available. BibTeX

	author = {Rahul Patel and Hiren Mewada and Suprava Patnaik},
	title = {Article: Fast and Regularization less Active Contour},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {6},
	pages = {26-31},
	month = {June},
	note = {Full text available}


The application of the level set method in image segmentation has been very popular due to its capability of automatically handling changes in topology. However, a re-initialization procedure, which leads to expensive computation, is required in the traditional level set method to keep the level set function as a signed distance function to its interface. A method based on Gaussian filtering and binary level set is proposed for the level set function of region based active contour model (ACM). The proposed level set method is integrated with the global region based Chan-Vese (C-V) ACM for image segmentation. The proposed method can, not only ensure the smoothness of the level set function by Gaussian filtering, but also eliminate the requirement of re-initialization, which is very computationally expensive task. The level set function can also be easily initialized as a binary function, which is more efficient to construct practically than the widely used signed distance function (SDF). Moreover, as the proposed scheme allows using larger time step than what can be used with the standard C-V model, it is tremendously faster than standard C-V model. Finally, the proposed algorithm can be efficiently implemented by the simple finite difference scheme. Experimental results on synthetic and real images shows that the proposed method is more efficient in terms of computational time and accuracy than global region based C-V active contour model.


  • Bakoš, M. 2007. Active Contours and their Utilization at Image Segmentation. In Proc. 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics, Poprad, Slovakia, pp. 313-317.
  • Blake, A. , and Isard, M. 1998. Active Contours, Cambridge, MA: Springer.
  • Caselles, V. , Catte, F. , Coll, T. , and Dibos, F. 1993. A geometric model for active contours in image processing. Numerische Mathematik, Vol. 66, No. 1, pp. 1-31.
  • Caselles, V. , Kimmel, R. , and Sapiro, G. 1997. Geodesic active contour. International Journal of Computer Vision, Vol. 22, No. 1, pp. 61-79.
  • Chan, T. , and Vese, L. 2001. Active contours without edges. IEEE Transaction on Image Processing, Vol. 10, No. 2, pp. 266- 277.
  • Kass, M. , Witkin, A. , and Terzopoulos, D. 1988. Snakes: Active contour models. International Journal of Computer Vision, Vol. 1, No. 4, pp. 321-331.
  • Lankton, S. , and Tannenbaum, A. 2008. Localizing region based active contour. IEEE Transactions on Image Processing, Vol. 17, No. 11, pp. 2029-2039.
  • Li, C. M. , Xu, C. Y. , Gui, C. F. , and Fox, M. D. 2005. Level set evolution without re-initialization: a new variational formulation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, pp. 430–436.
  • Mumford, D. , and Shah, J. 1989. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, Vol. 42, No. 5, pp. 577-685.
  • Osher, S. , and Sethian, J. A. 1988. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, Vol. 79, No. 1, pp. 12-49.
  • Osher, S. , and Fedkiw, R. 2002. Level Set Methods and Dynamic Implicit Surfaces. New York, Springer-Verlag.
  • Peng, D. , Merriman, B. , Osher, S. , Zhao, H. , and Kang, M. 1999. A PDE-based fast local level set method. Journal of Computational Physics, Vol. 155, pp. 410-438.
  • Perona, P. , and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629–640.
  • Shi, Y. , and Karl, W. C. 2005. Real-time tracking using level sets. IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 34–41.
  • Sussman, M. , and Fatemi, E. 1999. An efficient, interface preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM J. Sci. Comput. , Vol. 20, No. 4, pp. 1165–1191.
  • Zhu, G. P. , Zhang, Sh. Q. , Zeng, Q. SH. , and Wang, Ch. H. 2007. Boundary-based image segmentation using binary level set method. SPIE, OE Letters, Vol. 46, No. 5, pp. 1-3.