Skip to main content
SpringerLink
Log in

Multigrid gradient vector flow computation on the GPU

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Gradient vector flow (GVF) is a feature-preserving spatial diffusion of image gradients. It was introduced to overcome the limited capture range in traditional active contour segmentation. However, the original iterative solver for GVF, using Euler’s method, converges very slowly. Thus, many iterations are needed to achieve the desired capture range. Several groups have investigated the use of graphic processing units (GPUs) to accelerate the GVF computation. Still, this does not reduce the number of iterations needed. Multigrid methods, on the other hand, have been shown to provide a much better capture range using considerable less iterations. However, non-GPU implementations of the multigrid method are not as fast as the Euler method when executed on the GPU. In this paper, a novel GPU implementation of a multigrid solver for GVF written in OpenCL is presented. The results show that this implementation converges and provides a better capture range about 2–5 times faster than the conventional iterative GVF solver on the GPU.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://github.com/smistad/GPU-Multigrid-Gradient-Vector-Flow/.

  2. http://github.com/smistad/Gradient-Vector-Flow/.

References

  1. Advanced Micro Devices. AMD Accelerated Parallel Processing OpenCL Programming Guide. Technical Report November, 2013. http://developer.amd.com/wordpress/media/2013/07/AMD_Accelerated_Parallel_Processing_OpenCL_Programming_Guide-rev-2.7.pdf. Accessed 8 Aug 2013

  2. Alvarado, R., Tapia, J.J., Rolón, J.C.: Medical image segmentation with deformable models on graphics processing units. J. Supercomput. 68(1), 339–364 (2013)

    Article  Google Scholar 

  3. Bauer, C., Bischof, H.: A novel approach for detection of tubular objects and its application to medical image analysis. In: Proceedings of the 30th DAGM Symposium on Pattern Recognition, pp. 163–172. Springer, New York (2008)

  4. Bauer, C., Bischof, H.: Extracting curve skeletons from gray value images for virtual endoscopy. In: Proceedings of the 4th International Workshop on Medical Imaging and Augmented Reality, pp. 393–402. Springer, New York (2008)

  5. Bolz, J., Farmer, I., Grinspun, E., Schröder, P.: Sparse matrix solvers on the GPU: conjugate gradients and multigrid. In: Proceedings of ACM Transactions on Graphics (TOG), Proceedings of ACM SIGGRAPH 2003, vol. 22, issue 3, pp. 917–924 (2003)

  6. Eidheim, O.C., Skjermo, J., Aurdal, L.: Real-time analysis of ultrasound images using GPU. Int. Congr. Ser. 1281, 284–289 (2005)

    Article  Google Scholar 

  7. Grossauer, H., Thoman, P.: GPU-based multigrid: real-time performance in high resolution nonlinear image processing. Comput. Vis. Syst. 5008, 141–150 (2008)

    Article  Google Scholar 

  8. Guo, Y., Lu, C.-C.: Multi-modality image registration using mutual information based on gradient vector flow. In: Proceedings of 18th International Conference on Pattern Recognition (ICPR’06), IEEE, pp. 697–700 (2006)

  9. Han, X., Xu, C., Prince, J.L.: Fast numerical scheme for gradient vector flow computation using a multigrid method. Image Process. IET 1(1):48–55 (2007)

  10. Hassouna, M.S., Farag, A.A.: On the extraction of curve skeletons using gradient vector flow. In: Proceedings of IEEE 11th International Conference on Computer Vision, IEEE, pp. 1–8 (2007)

  11. He, Z., Kuester, F.: GPU-based active contour segmentation using gradient vector flow. In: Proceedings of Advances in Visual Computing, pp. 191–201. (2006)

  12. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  13. NVIDIA. OpenCL Best Practices Guide. Technical report, 2010. http://www.nvidia.com/content/cudazone/CUDABrowser/downloads/papers/NVIDIA_OpenCL_BestPracticesGuide.pdf. Accessed 8 Aug 2013

  14. Ray, N., Acton, S.T.: Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours. IEEE Trans. Med. Imaging 23(12):1466–1478 (2004)

  15. Smistad, E., Elster, A.C., Lindseth, F.: GPU-based airway segmentation and centerline extraction for image guided bronchoscopy. In: Proceedings of Norsk Informatikkonferanse, pp. 129–140. Akademika forlag (2012)

  16. Smistad, E., Elster, A.C., Lindseth, F.: Real-time gradient vector flow on GPUs using OpenCL. J. Real-Time Image Process. pp. 1–8 (2012)

  17. Smistad, E., Elster, A.C., Lindseth, F.: GPU accelerated segmentation and centerline extraction of tubular structures from medical images. Int. J. Comput. Assist. Radiol. Surg. 9(4), 561–575 (2014)

    Article  Google Scholar 

  18. Smistad, E., Lindseth, F.: A new tube detection filter for abdominal aortic aneurysms. In: Proceedings of MICCAI 2014 Workshop on Abdominal Imaging: Computational and Clinical Applications (2014)

  19. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  20. Zheng, Z., Zhang, R.: A fast GVF snake algorithm on the GPU. Res. J. Appl. Sci. Eng. Technol. 4(24), 5565–5571 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Saelandsvei 7-9, 7491, Trondheim, Norway

    Erik Smistad & Frank Lindseth

  2. SINTEF Medical Technology, Trondheim, Norway

    Erik Smistad & Frank Lindseth

Authors
  1. Erik Smistad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Lindseth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Smistad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smistad, E., Lindseth, F. Multigrid gradient vector flow computation on the GPU. J Real-Time Image Proc 12, 593–601 (2016). https://doi.org/10.1007/s11554-014-0466-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-014-0466-2

Keywords

Search

Navigation