Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 232685, 14 pages
http://dx.doi.org/10.1155/2012/232685
Research Article

Improved DCT-Based Nonlocal Means Filter for MR Images Denoising

1College of Computer Science, Sichuan University, Chengdu 610064, China
2College of Computer Science, Sichuan University, Chengdu 610065, China
3College of Electronic and Information Engineering, Sichuan University, Chengdu 610064, China

Received 21 August 2011; Revised 22 November 2011; Accepted 9 December 2011

Academic Editor: Quan Long

Copyright © 2012 Jinrong Hu 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. G. A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, vol. 14, no. 1, pp. 56–66, 1997. View at Publisher · View at Google Scholar · View at Scopus
  2. 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
  3. G. Gerig, O. Kubler, R. Kikinis, and F. A. Jolesz, “Nonlinear anisotropic filtering of MRI data,” IEEE Transactions on Medical Imaging, vol. 11, no. 2, pp. 221–232, 1992. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  4. Z. Fang and M. Lihong, “MRI denoising using the anisotropic coupled diffusion equations,” in Proceedings of the 3rd International Conference on BioMedical Engineering and Informatics (BMEI '10), pp. 397–401, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. J. B. Weaver, Y. Xu, D. M. Healy, and L. D. Cromwell, “Communications. Filtering noise from images with wavelet transforms,” Magnetic Resonance in Medicine, vol. 21, no. 2, pp. 288–295, 1991. View at Publisher · View at Google Scholar · View at Scopus
  6. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  7. R. D. Nowak, “Wavelet-based Rician noise removal for magnetic resonance imaging,” IEEE Transactions on Image Processing, vol. 8, no. 10, pp. 1408–1419, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  8. S. Zaroubi and G. Goelman, “Complex denoising of MR data via wavelet analysis: application for functional MRI,” Magnetic Resonance Imaging, vol. 18, no. 1, pp. 59–68, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Alexander, R. Baumgartner, A. R. Summers et al., “A wavelet-based method for improving signal-to-noise ratio and contrast in MR images,” Magnetic Resonance Imaging, vol. 18, no. 2, pp. 169–180, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. C. S. Anand and J. S. Sahambi, “Wavelet domain non-linear filtering for MRI denoising,” Magnetic Resonance Imaging, vol. 28, no. 6, pp. 842–861, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  11. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  12. 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 '05), vol. 2, pp. 60–65, June 2005. View at Scopus
  13. P. Coupé, P. Yger, and C. Barillot, “Fast non local means denoising for 3D MR images,” in Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention (CMICCAI '06), pp. 33–40, 2006. View at Scopus
  14. P. Coupé, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, “An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 27, no. 4, pp. 425–441, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  15. J. V. Manjón, J. Carbonell-Caballero, J. J. Lull, G. García-Martí, L. Martí-Bonmatí, and M. Robles, “MRI denoising using non-local means,” Medical Image Analysis, vol. 12, no. 4, pp. 514–523, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  16. N. Wiest-Daesslé, S. Prima, P. Coupé et al., “Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: application to DT-MRI,” in Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, part 2, New York, NY, USA, 2008.
  17. L. He and I. R. Greenshields, “A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images,” IEEE Transactions on Medical Imaging, vol. 28, no. 2, pp. 165–172, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  18. J. V. Manjón, P. Coupé, L. Martí-Bonmatí, D. L. Collins, and M. Robles, “Adaptive non-local means denoising of MR images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192–203, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  19. H. Liu, C. Yang, N. Pan, E. Song, and R. Green, “Denoising 3D MR images by the enhanced non-local means filter for Rician noise,” Magnetic Resonance Imaging, vol. 28, no. 10, pp. 1485–1496, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  20. N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transfom,” Computers, IEEE Transactions, vol. 23, no. 1, pp. 90–93, 1974. View at Publisher · View at Google Scholar
  21. R. Clarke, “Relation between the Karhunen Loeve and cosine transforms,” Communications, Radar and Signal Processing, IEE Proceedings F, vol. 128, no. 6, pp. 359–360, 1981. View at Google Scholar · View at Scopus
  22. S. A. Khayam, The Discrete Cosine Transform (DCT): Theory and Application, Michigan State University, 2003.
  23. E. Bingham and H. Mannila, “Random projection in dimensionality reduction: applications to image and text data,” in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 245–250, August 2001. View at Scopus
  24. G. Yu and G. Sapiro, “DCT image denoising: a simple and effective image denoising algorithm,” http://www.ipol.im/pub/algo/ys_dct_denoising/.
  25. H. Gudbjartsson and S. Patz, “The Rician distribution of noisy MRI data,” Magnetic Resonance in Medicine, vol. 34, no. 6, pp. 910–914, 1995. View at Publisher · View at Google Scholar · View at Scopus
  26. A. H. Andersen, “On the Rician distribution of noisy MRI data,” Magnetic Resonance in Medicine, vol. 36, no. 2, pp. 331–333, 1996. View at Google Scholar · View at Scopus
  27. R. K. Kwan, A. C. Evans, and B. Pike, “MRI simulation-based evaluation of image-processing and classification methods,” IEEE Transactions on Medical Imaging, vol. 18, no. 11, pp. 1085–1097, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  28. D. L. Collins, A. P. Zijdenbos, V. Kollokian et al., “Design and construction of a realistic digital brain phantom,” IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 463–468, 1998. View at Google Scholar · View at Scopus
  29. BrianWeb, http://www.bic.mni.mcgill.ca/brainweb/.