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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 902143, 8 pages
http://dx.doi.org/10.1155/2013/902143
Research Article

Improving Spatial Adaptivity of Nonlocal Means in Low-Dosed CT Imaging Using Pointwise Fractal Dimension

1College of Computer Science, Sichuan University, No. 29 Jiuyanqiao Wangjiang Road, Chengdu 610064, Sichuan, China
2School of Computer Science, Sichuan Normal University, No. 1819 Section 2 of Chenglong Road, Chengdu 610101, Sichuan, China
3School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, Sichuan, China
4School of Information Science and Technology, East China Normal University, No. 500, Dong-Chuan Road, Shanghai 200241, China

Received 25 January 2013; Accepted 6 March 2013

Academic Editor: Shengyong Chen

Copyright © 2013 Xiuqing Zheng 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. D. J. Brenner and E. J. Hall, “Computed tomography-an increasing source of radiation exposure,” New England Journal of Medicine, vol. 357, no. 22, pp. 2277–2284, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Hansen and A. G. Jurik, “Survival and radiation risk in patients obtaining more than six CT examinations during one year,” Acta Oncologica, vol. 48, no. 2, pp. 302–307, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. H. J. Brisse, J. Brenot, N. Pierrat et al., “The relevance of image quality indices for dose optimization in abdominal multi-detector row CT in children: experimental assessment with pediatric phantoms,” Physics in Medicine and Biology, vol. 54, no. 7, pp. 1871–1892, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Yu, “Radiation dose reduction in computed tomography: techniques and future perspective,” Imaging in Medicine, vol. 1, no. 1, pp. 65–84, 2009. View at Publisher · View at Google Scholar
  5. J. Weidemann, G. Stamm, M. Galanski, and M. Keberle, “Comparison of the image quality of various fixed and dose modulated protocols for soft tissue neck CT on a GE Lightspeed scanner,” European Journal of Radiology, vol. 69, no. 3, pp. 473–477, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Qi, J. Li, and X. Du, “Method for automatic tube current selection for obtaining a consistent image quality and dose optimization in a cardiac multidetector CT,” Korean Journal of Radiology, vol. 10, no. 6, pp. 568–574, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Kuettner, B. Gehann, J. Spolnik et al., “Strategies for dose-optimized imaging in pediatric cardiac dual source CT,” RoFo, vol. 181, no. 4, pp. 339–348, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Kropil, R. S. Lanzman, C. Walther et al., “Dose reduction and image quality in MDCT of the upper abdomen: potential of an adaptive post-processing filter,” RoFo, vol. 182, no. 3, pp. 248–253, 2009. View at Google Scholar
  9. H. B. Lu, X. Li, L. Li et al., “Adaptive noise reduction toward low-dose computed tomography,” in Proceedings of the Medical Imaging 2003: Physics of Medical Imaging, parts 1 and 2, vol. 5030, pp. 759–766, February 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. J. C. Giraldo, Z. S. Kelm, L. S. Guimaraes et al., “Comparative study of two image space noise reduction methods for computed tomography: bilateral filter and nonlocal means,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 3529–3532, 2009. View at Scopus
  11. H. B. Lu, I. T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose CT projections and noise treatment by scale transformations,” in Proceedings of the IEEE Nuclear Science Symposium Conference Record, vol. 1–4, pp. 1662–1666, November 2002. View at Scopus
  12. P. J. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose CT,” Medical Physics, vol. 32, no. 6, pp. 1676–1683, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Hu, Z. Liao, and W. Chen, “Reducing noises and artifacts simultaneously of low-dosed X-ray computed tomography using bilateral filter weighted by Gaussian filtered sinogram,” Mathematical Problems in Engineering, vol. 2012, Article ID 138581, 14 pages, 2012. View at Publisher · View at Google Scholar
  14. S. Hu, Z. Liao, and W. Chen, “Sinogram restoration for low-dosed X-ray computed tomography using fractional-order Perona-Malik diffusion,” Mathematical Problems in Engineering, vol. 2012, Article ID 391050, 13 pages, 2012. View at Publisher · View at Google Scholar
  15. 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 Scopus
  16. 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
  17. A. Buades, B. Coll, and J. M. Morel, “Nonlocal image and movie denoising,” International Journal of Computer Vision, vol. 76, no. 2, pp. 123–139, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Yang, C. Wufan, Y. Xindao et al., “Improving low-dose abdominal CT images by weighted intensity averaging over large-scale neighborhoods,” European Journal of Radiology, vol. 80, no. 2, pp. e42–e49, 2011. View at Publisher · View at Google Scholar
  19. Y. Chen, Z. Yang, W. Chen et al., “Thoracic low-dose CT image processing using an artifact suppressed largescale nonlocal means,” Physics in Medicine and Biology, vol. 57, no. 9, pp. 2667–2688, 2012. View at Publisher · View at Google Scholar
  20. Y. Chen, D. Gao, C. Nie et al., “Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior,” Computerized Medical Imaging and Graphics, vol. 33, no. 7, pp. 495–500, 2009. View at Google Scholar · View at Scopus
  21. Z. Liao, S. Hu, and W. Chen, “Determining neighborhoods of image pixels automatically for adaptive image denoising using nonlinear time series analysis,” Mathematical Problems in Engineering, vol. 2010, Article ID 914564, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Liao, S. Hu, M. Li, and W. Chen, “Noise estimation for single- slice sinogram of low-dose X-ray computed tomography using homogenous patch,” Mathematical Problems in Engineering, vol. 2012, Article ID 696212, 16 pages, 2012. View at Publisher · View at Google Scholar
  23. T. Thaipanich, B. T. Oh, P.-H. Wu, and C.-J. Kuo, “Adaptive nonlocal means algorithm for image denoising,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE '10), 2010.
  24. T. Thaipanich and C.-C. J. Kuo, “An adaptive nonlocal means scheme for medical image denoising,” in Proceedings of the SPIE Medical Imaging 2010: Image Processing, vol. 7623, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Yan, L. Shao, S. D. Cvetkovic, and J. Klijn, “Improved nonlocal means based on pre-classification and invariant block matching,” Journal of Display Technology, vol. 8, no. 4, pp. 212–218, 2012. View at Publisher · View at Google Scholar
  26. A. K. Bisoi and J. Mishra, “On calculation of fractal dimension of images,” Pattern Recognition Letters, vol. 22, no. 6-7, pp. 631–637, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Creutzberg and E. Ivanov, “Computing fractal dimension of image segments,” in Proceedings of the 3rd International Conference of Computer Analysis of Images and Patterns (CAIP '89), 1989.
  28. M. Ghazel, G. H. Freeman, and E. R. Vrscay, “Fractal image denoising,” IEEE Transactions on Image Processing, vol. 12, no. 12, pp. 1560–1578, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Ghazel, G. H. Freeman, and E. R. Vrscay, “Fractal-wavelet image denoising revisited,” IEEE Transactions on Image Processing, vol. 15, no. 9, pp. 2669–2675, 2006. View at Publisher · View at Google Scholar · View at Scopus
  30. B. Pesquet-Popescu and J. L. Vehel, “Stochastic fractal models for image processing,” IEEE Signal Processing Magazine, vol. 19, no. 5, pp. 48–62, 2002. View at Publisher · View at Google Scholar