Advertisement

Adaptive Vector Flow for Active Contour Model

  • Qi Zhang
  • Lixiong Liu
  • Bao Liu
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

A novel external force for active contours, called as adaptive vector flow (AVF), is proposed in this paper. Based on analyzing the diffusion mechanism of gradient vector flow (GVF), it is found that GVF is difficult to preserve weak edges and enter long and thin concavities. In AVF, we replace the isotropic smoothness term of GVF by an adaptive anisotropic one and adjust the diffusion speed in tangent and normal directions by the local features of the images. Experimental results on synthetic and real images show that, compared with the GVF snake, the AVF snake has better performance and properties.

Keywords

Adaptive Vector Flow Gradient Vector Flow Active Contour Image Segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aubert, G., Kornprobst, P.: Mathematical problems in image processing: partial differential equations and the calculus of variations. Spring, New York (2002)zbMATHGoogle Scholar
  2. 2.
    Kass, M., Withkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  3. 3.
    Xu, C., Prince, J.L.: Gradient vector flow: a new external force for snakes. In: Proceeding of IEEE Conference of Computer Vision and Pattern Recognition, pp. 66–71 (1993)Google Scholar
  4. 4.
    Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours. Signal Processing 71(2), 131–139 (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Park, H.K., Chung, M.J.: External force of snake: virtual electric field. Electronics Letters 38(24), 1500–1502 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contour. In: Proceeding of Fifth International Conference on Computer Vision, pp. 694–699 (1995)Google Scholar
  7. 7.
    Chan, T., Vese, L.A.: An Active Contour Model without Edges. In: Proceeding of Scale-Space Theories in Computer Vision, vol. 1682, pp. 141–151 (1999)Google Scholar
  8. 8.
    Ning, J.F., Wu, C.K., Liu, S., Yang, S.: GVF: An improved external force field for active contour model. Pattern Recognition Letters 28, 58–63 (2007)CrossRefGoogle Scholar
  9. 9.
    Yuan, D., Lu, S.: Simulated static electric field (SSEF) snake for deformable models. In: Proceeding of International Conference on Pattern Recognition, vol. 1, pp. 83–86 (2002)Google Scholar
  10. 10.
    Wang, Y., Jia, Y., Liu, L.: Harmonic gradient vector flow external force for snake model. Electronics Letters 44(2), 105–106 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rudin, L., Osher, S., Fatime, E.: Nonlinear total variation based noise removal algorithm. Physisca D 60, 259–268 (1992)zbMATHCrossRefGoogle Scholar
  12. 12.
    Blomgren, P.V., Chen, T.F.: Color TV: total variation method for restoration of vector valued images. IEEE, IP 7(3), 304–309 (1998)Google Scholar
  13. 13.
    Chalana, V., Linker, D.T., Haynor, D.R., Kim, Y.: A multiple active contour model for cardiac boundary detection on echocardiographic sequences. IEEE Trans. Med. Img. 15, 290–297 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qi Zhang
    • 1
  • Lixiong Liu
    • 1
  • Bao Liu
    • 1
  1. 1.Beijing Laboratory of Intelligent Information Technology, School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingP.R. China

Personalised recommendations