GPU-Based Active Contour Segmentation Using Gradient Vector Flow

  • Zhiyu He
  • Falko Kuester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


One fundamental step for image-related research is to obtain an accurate segmentation. Among the available techniques, the active contour algorithm has emerged as an efficient approach towards image segmentation. By progressively adjusting a reference curve using combination of external and internal force computed from the image, feature edges can be identified. The Gradient Vector Flow (GVF) is one efficient external force calculation for the active contour and a GPU-centric implementation of the algorithm is presented in this paper. Since the internal SIMD architecture of the GPU enables parallel computing, General Purpose GPU (GPGPU) based processing can be applied to improve the speed of the GVF active contour for large images. Results of our experiments show the potential of GPGPU in the area of image segmentation and the potential of the GPU as a powerful co-processor to traditional CPU computational tasks.


Active Contour Graphic Hardware Gradient Vector Flow Snake Algorithm Initial Circle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  2. 2.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Transaction on Image Proccessing 7, 359–369 (1998)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Kilgariff, E., Fernando, R.: The geforce 6 series gpu architecture. In: GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation, pp. 471–493 (2005)Google Scholar
  4. 4.
    Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. IEEE Transaction. on Medical Imaging 21, 1212–1221 (2002)CrossRefGoogle Scholar
  5. 5.
    Ding, F., Leow, W., Wang, S.: Segmentation of 3d ct volume images using a single 2d atlas. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 459–468. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Vidholm, E., Nystrom, I.: Haptic volume rendering based on gradient vector flow. In: Proceedings of Swedish symposium on image analysis (SSBA 2005), pp. 97–100 (2005)Google Scholar
  7. 7.
    Rumpf, M., Strzodka, R.: Level set segmentation in graphics hardware. In: Proceedings of the 2001 International Conference on Image Processing, vol. 3, pp. 1103–1106 (2001)Google Scholar
  8. 8.
    Kondratieva, P., Krüger, J., Westermann, R.: The application of gpu particle tracing to diffusion tensor field visualization. In: Proceedings IEEE Visualization 2005 (Vis 2005) (2005)Google Scholar
  9. 9.
    Fan, Z., Qiu, F., Kaufman, A., Yoakum-Stover, S.: Gpu cluster for high performance computing. In: Proceedings of the 2004 ACM/IEEE conference on Supercomputing (SC 2004), p. 47 (2004)Google Scholar
  10. 10.
    Kipfer, P., Segal, M., Westermann, R.: Uberflow: a gpu-based particle engine. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware (HWWS 2004), pp. 115–122 (2004)Google Scholar
  11. 11.
    Fatahalian, K., Sugerman, J., Hanrahan, P.: Understanding the efficiency of gpu algorithms for matrix-matrix multiplication. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware (HWWS 2004), pp. 133–137 (2004)Google Scholar
  12. 12.
    Lefohn, A.E., Kniss, J.M., Hansen, C.D., Whitaker, R.T.: Interactive deformation and visualization of level set surfaces using graphics hardware. In: Proceedings of the 14th IEEE Visualization (VIS 2003), pp. 75–82 (2003)Google Scholar
  13. 13.
    Yang, R., Welch, G., Bishop, G.: Real-time consensus-based scene reconstruction using commodity graphics hardware. In: Proceedings of the 10th Pacific Conference on Computer Graphics and Applications (PG 2002), p. 225 (2002)Google Scholar
  14. 14.
    Galoppo, N., Govindaraju, N., Henson, M., Manocha, D.: Lu-gpu: Efficient algorithms for solving dense linear systems on graphics hardware. In: Gschwind, T., Aßmann, U., Nierstrasz, O. (eds.) SC 2005. LNCS, vol. 3628, pp. 3–3. Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Lefohn, A.: Gpu memory model overview. In: Proceedings of the ACM Siggraph (2004)Google Scholar
  16. 16.
    Govindaraju, N., Raghuvanshi, N., Henson, M., Manocha, D.: A cache-efficient sorting algorithm for database and data mining computations using graphics processors. In: UNC Tech. Report (2005)Google Scholar
  17. 17.
    NVidia: The cg toolkit. In: NVidia Corp (2005),
  18. 18. General-purpose computation on gpus (2005)Google Scholar
  19. 19.
    Microsoft: Hlsl shaders. Microsoft Inc (2004),
  20. 20.
    OpenGL: Opengl shading language (2005),
  21. 21.
    Xu, C., Prince, J.L.: Generalized gradient vector flow external forces for active contours. Signal Processing — An International Journal 71, 131–139 (1998)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiyu He
    • 1
  • Falko Kuester
    • 1
  1. 1.Calit2 Center of GRAVITYUniversity of CaliforniaIrvine

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