Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 952819, 16 pages
http://dx.doi.org/10.1155/2011/952819
Research Article

True 4D Image Denoising on the GPU

1Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
2Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

Received 31 March 2011; Revised 23 June 2011; Accepted 24 June 2011

Academic Editor: Khaled Z. Abd-Elmoniem

Copyright © 2011 Anders Eklund et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J.-S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 2, pp. 165–168, 1980. View at Google Scholar · View at Scopus
  2. H. E. Knutsson, R. Wilson, and G. H. Granlund, “Anisotropic non-stationary image estimation and its applications—part I: restoration of noisy images,” IEEE Transactions on Communications, vol. 31, no. 3, pp. 388–397, 1983. View at Google Scholar · View at Scopus
  3. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Knutsson, L. Haglund, H. Bårman, and G. Granlund, “A framework for anisotropic adaptive filtering and analysis of image sequences and volumes,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP), pp. 469–472, 1992.
  5. G. Granlund and H. Knutsson, Signal Processing for Computer Vision, Kluwer Academic, Boston, Mass, USA, 1995.
  6. C.-F. Westin, L. Wigström, T. Loock, L. Sjöqvist, R. Kikinis, and H. Knutsson, “Three-dimensional adaptive filtering in magnetic resonance angiography,” Journal of Magnetic Resonance Imaging, vol. 14, pp. 63–71, 2001. View at Google Scholar
  7. J. Montagnat, M. Sermesant, H. Delingette, G. Malandain, and N. Ayache, “Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images,” Pattern Recognition Letters, vol. 24, no. 4-5, pp. 815–825, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Jahanian, A. Yazdan-Shahmorad, and H. Soltanian-Zadeh, “4D wavelet noise suppression of MR diffusion tensor data,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP), pp. 509–512, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Pauwels and M. M. Van Hulle, “Realtime phase-based optical flow on the GPU,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (CVPR), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Muyan-Özcelik, J. D. Owens, J. Xia, and S. S. Samant, “Fast deformable registration on the GPU: a CUDA implementation of demons,” in Proceedings of the International Conference on Computational Sciences and its Applications, (ICCSA), pp. 223–233, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Bui and J. Brockman, “Performance analysis of accelerated image registration using GPGPU,” in Proceedings of the 2nd Workshop on General Purpose Processing on Graphics Processing Units, (GPGPU-2), pp. 38–45, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Eklund, M. Andersson, and H. Knutsson, “Phase based volume registration using CUDA,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP), pp. 658–661, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Shams, P. Sadeghi, R. Kennedy, and R. Hartley, “A survey of medical image registration on multicore and the GPU,” IEEE Signal Processing Magazine, vol. 27, no. 2, Article ID 5438962, pp. 50–60, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. A. E. Lefohn, J. E. Cates, and R. T. Whitaker, “Interactive, GPU-based level sets for 3D segmentation,” Lecture Notes in Computer Science, vol. 2878, pp. 564–572, 2003. View at Google Scholar · View at Scopus
  15. V. Vineet and P. J. Narayanan, “CUDA cuts: fast graph cuts on the GPU,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (CVPR), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Abramov, T. Kulvicius, F. Wörgötter, and B. Dellen, “Real-time image segmentation on a GPU,” in Proceedings of Facing the Multicore-Challenge, vol. 6310 of Lecture Notes in Computer Science, pp. 131–142, Springer, 2011.
  17. D. Gembris, M. Neeb, M. Gipp, A. Kugel, and R. Männer, “Correlation analysis on GPU systems using NVIDIA's CUDA,” Journal of Real-Time Image Processing, pp. 1–6, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Eklund, O. Friman, M. Andersson, and H. Knutsson, “A GPU accelerated interactive interface for exploratory functional connectivity analysis of fMRI data,” in Proceedings of the IEEE International Conference on Image Processing, (ICIP), pp. 1621–1624, 2011.
  19. A. Eklund, M. Andersson, and H. Knutsson, “fMRI analysis on the GPU—possibilities and challenges,” Computer Methods and Programs in Biomedicine. In press. View at Publisher · View at Google Scholar
  20. A. Eklund, M. Andersson, and H. Knutsson, “Fast random permutation tests enable objective evaluation of methods for single subject fMRI analysis,” International Journal of Biomedical Imaging, vol. 2011, Article ID 627947, 2011. View at Publisher · View at Google Scholar
  21. M. Rumpf and R. Strzodka, “Nonlinear diffusion in graphics hardware,” in Proceedings of the EG/IEEE TCVG Symposium on Visualization, pp. 75–84, 2001.
  22. M. Howison, “Comparing GPU implementations of bilateral and anisotropic diffusion filters for 3D biomedical datasets,” Tech. Rep. LBNL-3425E, Lawrence Berkeley National Laboratory, Berkeley, Calif, USA.
  23. Y. Su and Z. Xu, “Parallel implementation of wavelet-based image denoising on programmable PC-grade graphics hardware,” Signal Processing, vol. 90, no. 8, pp. 2396–2411, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. Q. Zhang, R. Eagleson, and T. M. Peters, “GPU-based image manipulation and enhancement techniques for dynamic volumetric medical image visualization,” in Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (ISBI), pp. 1168–1171, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Chen, S. Paris, and F. Durand, “Real-time edge-aware image processing with the bilateral grid, ACM transactions on graphics,” in Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference, no. 103, p. 9, 2007.
  26. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 839–846, January 1998. View at Scopus
  27. F. Fontes, G. Barroso, P. Coupe, and P. Hellier, “Real time ultrasound image denoising,” Journal of Real-Time Image Processing, vol. 6, pp. 15–22, 2010. View at Google Scholar
  28. B. Goossens, H. Luong, J. Aelterman, A. Pizurica, and W. Philips, “A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences,” in Proceedings of the 12th International Conference on Advanced Concepts for Intelligent Vision Systems, (ACIVS), vol. 6475 of Lecture Notes in Computer Science, pp. 46–57, Springer, 2010.
  29. A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR), pp. 60–65, June 2005. View at Scopus
  30. H. Knutsson, “Representing local structure using tensors,” in Proceedings of the Scandinavian Conference on Image Analysis, (SCIA), pp. 244–251, 1989.
  31. F. Forsberg, V. Berghella, D. A. Merton, K. Rychlak, J. Meiers, and B. B. Goldberg, “Comparing image processing techniques for improved 3-dimensional ultrasound imaging,” Journal of Ultrasound in Medicine, vol. 29, no. 4, pp. 615–619, 2010. View at Google Scholar · View at Scopus
  32. H. Knutsson, C.-F. Westin, and M. Andersson, “Representing local structure using tensors II,” in Proceedings of the Scandinavian Conference on Image Analysis, (SCIA), vol. 6688 of Lecture Notes in Computer Science, pp. 545–556, Springer, 2011.
  33. H. Knutsson, M. Andersson, and J. Wiklund, “Advanced filter design,” in Proceedings of the Scandinavian Conference on Image Analysis, (SCIA), pp. 185–193, 1999.
  34. H. Knutsson and C. F. Westin, “Normalized and differential convolution: methods for interpolation and filtering of incomplete and uncertain data,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR), pp. 515–523, June 1993. View at Scopus
  35. Nvidia, CUDA Programming Guide, Version 4.0., 2011.
  36. D. Kirk and W. Hwu, Programming Massively Parallel Processors, A Handson Approach, Morgan Kaufmann, Waltham, Mass, USA, 2010.
  37. The Khronos Group & OpenCL, 2010, http://www.khronos.org/opencl/.
  38. The OpenMP API specification for parallel programming, 2011, http://www.openmp.org/.
  39. B. Chapman, G. Jost, and R. van der Pas, Using OpenMP, Portable Shared Memory Parallel Programming, MIT Press, Cambridge, Mass, USA, 2007.
  40. V. W. Lee, C. Kim, J. Chhugani et al., “Debunking the 100X GPU vs. CPU Myth: an evaluation of throughput computing on CPU and GPU,” in Proceedings of the 37th International Symposium on Computer Architecture, (ISCA), pp. 451–460, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Andersson, J. Wiklund, and H. Knutsson, “Filter networks,” in Proceedings of the Signal and Image Processing, (SIP), pp. 213–217, 1999.
  42. B. Svensson, M. Andersson, and H. Knutsson, “Filter networks for efficient estimation of local 3-D structure,” in Proceedings of the IEEE International Conference on Image Processing, (ICIP), pp. 573–576, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. M. Andersson, J. Wiklund, and H. Knutsson, “Sequential filter trees for efficient 2D, 3D and 4D orientation estimation,” Tech. Rep. LiTH-ISY-R-2070, Department of Electrical Engineering, Linköping University, Linköping, Sweden, 1998. View at Google Scholar
  44. A. Nukada, Y. Ogata, T. Endo, and S. Matsuoka, “Bandwidth intensive 3-D FFT kernel for GPUs using CUDA,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, (SC), pp. 1–11, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. A. Nukada and S. Matsuoka, “Auto-tuning 3-D FFT library for CUDA GPUs,” in Proceedings of the International Conference on High Performance Computing Networking, Storage and Analysis, (SC), pp. 1–10, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. A. Sigfridsson, J. P. E. Kvitting, H. Knutsson, and L. Wigström, “Five-dimensional MRI incorporating simultaneous resolution of cardiac and respiratory phases for volumetric imaging,” Journal of Magnetic Resonance Imaging, vol. 25, no. 1, pp. 113–121, 2007. View at Publisher · View at Google Scholar · View at Scopus