Skip to main content
SpringerLink
Log in

GPU-accelerated level-set segmentation

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

Abstract

The level-set method, a technique for the computation of evolving interfaces, is a solution commonly used to segment images and volumes in medical applications. GPUs have become a commodity hardware with hundreds of cores that can execute thousands of threads in parallel, and they are nowadays ideal platforms to execute computational intensive tasks, such as the 3D level-set-based segmentation, in real time. In this paper, we propose two GPU implementations of the level-set-based segmentation method called Fast Two-Cycle. Our proposals perform computations in independent domains called tiles and modify the structure of the original algorithm to better exploit the features of the GPU. The implementations were tested with real images of brain vessels and a synthetic MRI image of the brain. Results show that they execute faster than a CPU-sequential implementation of the same method, without any significant loss of the segmentation quality and without requiring distributed parallel computer infrastructures.

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
Listing 1
Listing 2
Listing 3
Fig. 4
Fig. 5
Listing 4
Listing 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Adalsteinsson, D., Sethian, J.A.: A fast level set method for propagating interfaces. J Computational Phys 118(2), 269–277 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  2. BrainWeb simulated brain database. http://www.bic.mni.mcgill.ca/brainweb/

  3. Bridson, R.E. (ed.): Computational aspects of dynamic surfaces. Ph.D. thesis, Standford University, Standford (2003)

  4. BrookGPU. http://graphics.stanford.edu/projects/brookgpu/

  5. Courant, R., Isaacson, E., Rees, M.: On the solution of nonlinear hyperbolic differential equations by finite differences. Commun Pure Appl Math 5(3), 243–255 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  6. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  7. GeForce GTX 280 specifications. http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-280/specifications

  8. GeForce GTX 295 specifications. http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-295/specifications

  9. GPGPU. http://gpgpu.org/

  10. Harris, M., Segupta, S., Owens, J.D.: GPU Gems 3, chap. Parallel prefix sum (scan) with CUDA. Addison Wesley (2007)

  11. Intel Corporation, Santa Clara, California, USA. Intel 64 and IA-32 Architectures Software Developer’s Manual, System Programming Guide (2011)

  12. Jeong, W.K., Beyer, J., Hadwiger, M., Vazquez, A., Pfister, H., Whitaker, R.T.: Scalable and interactive segmentation and visualization of neural processes in EM datasets. IEEE Trans Visual Comp Graph 15(6), 1505–1514 (2009)

    Article  Google Scholar 

  13. Kirk, D.B., Hwu, W.M.W.: Programming massively parallel processors: a hands-on approach. Elsevier, Massachussets (2010)

  14. Jalba, A.C., van der Laan, W.J., Roerdink, J.B.T.M.: Fast sparse level-set on graphics hardware. IEEE Trans Visual Comp Graph 99 (2012). PrePrints

  15. van der Laan W.J., Jalba A.C., Roerdink J.B.T.M.: A memory and computation efficient sparse level-set method. J Sci Comp 46(2), 1–22 (2011)

    MathSciNet  MATH  Google Scholar 

  16. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  17. Lefohn, A.E., Cates, J.E., Whitaker, R.T.: Interactive, GPU-based level sets for 3D brain tumor segmentation. Tech. rep., University of Utah, School of Computing (2003)

  18. Lefohn, A.E., Kniss, J.M., Hansen, C.D., Whitaker, R.T.: A streaming narrow-band algorithm: Interactive computation and visualization of level sets. IEEE Trans Visual Comp Graph 10(4), 422–433 (2004)

    Article  Google Scholar 

  19. Lindholm, E., Nickolls, J., Oberman, S., Montrym, J.: NVIDIA Tesla: a unified graphics and computing architecture. IEEE Micro 28(2), 39–55 (2008)

    Article  Google Scholar 

  20. Lucas, B.C., Kazhdan, M., Taylor, R.H.: Multi-object geodesic active contours (MOGAC): a parallel sparse-field algorithm for image segmentation. Tech. rep., Johns Hopkins University, Department of Computer Science (2012)

  21. Min C.: Local level set method in high dimension and codimension. J Comput Phys 200(1), 368–382 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  22. Nickolls, J., Dally, W.J.: The GPU computing era. IEEE Micro 30(2), 56–69 (2010)

    Article  Google Scholar 

  23. NVIDIA GeForce GTX 580 GPU datasheet, NVIDIA, Santa Clara (2010)

  24. CUDA C best practices guide (version 4.0), NVIDIA, Santa Clara (2011)

  25. CUDA C programming guide (version 4.0), NVIDIA, Santa Clara (2011)

  26. OpenCL: the open standard for parallel programming of heterogeneous systems. http://www.khronos.org/opencl/

  27. Osher, S., Paragios, N.: Geometric level set methods in imaging, vision, and graphics. Springer-Verlag New York, Inc., Secaucus (2003)

  28. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79, 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  29. Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proc IEEE 96(5):879–899 (2008)

    Google Scholar 

  30. Parallel programming and computing platform, CUDA, NVIDIA. http://www.nvidia.com/object/cuda_home_new.html

  31. Peng, D., Merriman, B., Osher, S., Zhao, H., Kang, M.: A PDE-based fast local level set method. J Comput Phys 155(2), 410–438 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  32. Roberts, M., Packer, J., Sousa, M.C., Mitchell, J.R.: A work-efficient GPU algorithm for level set segmentation. In: Proceedings of the Conference on High Performance Graphics, HPG ’10. pp. 123–132. Eurographics Association, Saarbrucken (2010)

  33. Rumpf, M., Strzodka, R.: Level set segmentation in graphics hardware. In: Proceedings of IEEE International Conference on Image Processing, ICIP’ 01. pp. 1103–1106. IEEE, Thessaloníki (2001)

  34. Sethian, J.A.: Level set methods and fast marching methods: evolving interfaces in computational fluid mechanics, computer vision, and materials science. Cambridge University Press, Cambridge (1996)

  35. Shi, Y., Karl, W.C.: A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Trans Image Process 17(5), 645–656 (2008)

    Article  MathSciNet  Google Scholar 

  36. Strain, J.: Semi-Lagrangian methods for level set equations. J Comput Phys 151(2), 498–533 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  37. Strain, J.: Tree methods for moving interfaces. J Comput Phys 151(2), 616–648 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  38. Strain, J.: A fast modular semi-Lagrangian method for moving interfaces. J Comput Phys 161(2), 512–536 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  39. The Stanford 3D scanning repository. http://graphics.stanford.edu/data/3Dscanrep/

  40. Thrust. http://code.google.com/p/thrust/

  41. Tornai, G.J., Cserey, G.: 2D and 3D level-set algorithms on GPU. In: Proceedings of the 12th International Workshop on Cellular Nanoscale and their Applications, pp. 1–5. CNNA ’10. IEEE, Berkeley (2010)

  42. Whitaker, R.T.: Volumetric deformable models: active blobs. In: Visualization in Biomedical Computing 1994, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. pp. 122–134. SPIE, Rochester (1994)

  43. Whitaker, R.T.: A level-set approach to 3D reconstruction from range data. Intern J Comp Vis 29(3), 203–231 (1998)

    Article  Google Scholar 

  44. ZibAmira: Amira for research partners. http://amira.zib.de/

  45. Zijdenbos, A.P., Dawant, B.M., Mangolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imag 13(4), 716–724 (1994)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Ministry of Science and Innovation, Government of Spain, and FEDER funds under contract TIN 2010-17541, and by the Xunta de Galicia under contracts 08TIC001206PR and 2010/28. Julián Lamas-Rodríguez acknowledges financial support from the Ministry of Science and Innovation, Government of Spain, under a MICINN-FPI grant.

Author information

Authors and Affiliations

  1. Centro de Investigación en Tecnoloxías da Información da Universidade de Santiago de Compostela (CITIUS), Santiago de Compostela, Spain

    Julián Lamas-Rodríguez & Dora B. Heras

  2. Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain

    Francisco Argüello & Montserrat Bóo

  3. Department of Visualization and Data Analysis, Konrad-Zuse-Zentrum für Informationstechnik, Berlin, Germany

    Dagmar Kainmueller & Stefan Zachow

Authors
  1. Julián Lamas-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dora B. Heras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Argüello
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dagmar Kainmueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Zachow
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Montserrat Bóo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julián Lamas-Rodríguez.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lamas-Rodríguez, J., Heras, D.B., Argüello, F. et al. GPU-accelerated level-set segmentation. J Real-Time Image Proc 12, 15–29 (2016). https://doi.org/10.1007/s11554-013-0378-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-013-0378-6

Keywords

Search

Navigation