2.5D Extension of Neighborhood Filters for Noise Reduction in 3D Medical CT Images

  • Maria Storozhilova
  • Alexey Lukin
  • Dmitry Yurin
  • Valentin Sinitsyn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7870)


Noise in 3D computer tomography (CT) images is close to white and becomes large when patient radiation doses are reduced. We propose two methods for noise reduction in CT images: 3D extension of fast rank algorithms (Rank-2.5D) and 3D extension of a non-local means algorithm (NLM-2.5D). We call both our algorithms “2.5D” because the extended NLM algorithm is slightly asymmetric by slice axes, while our Rank algorithms, being fully symmetric mathematically and by results, have some implementation asymmetry. A comparison of the methods is presented. It is shown that NLM-2.5D method has the best quality, but is computationally expensive: its complexity quickly rises as a function of the neighborhood size, while Rank-2.5D only shows linear growth. Another contribution of this paper is a modified multiscale histogram representation in memory with a tree-like structure. This dramatically reduces memory requirements and makes it possible to process 16-bit DICOM data with full accuracy. Artificial test sequences are used for signal-to-noise performance measurements, while real CT scans are used for visual assessment of results. We also propose two new measures for no-reference denoising quality assessment based on the autocorrelation coefficient and entropy: both measures analyze randomness of the difference between noisy and filtered images.


medical imaging CT DICOM filtering enhancement noise reduction denoising 3D image processing image quality assessment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pratt, W.: Digital Image Processing: PIKS Scientific Inside. Wiley (2007)Google Scholar
  2. 2.
    Yaroslavsky, L., Kim, V.: Rank algorithms for picture processing. ACM Transactions on Graphics (TOG) 35, 234–258 (1986)Google Scholar
  3. 3.
    Huang, T., Yang, G., Tang, G.: A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust., Speech, Signal Proc. 27(1), 13–18 (1979)CrossRefGoogle Scholar
  4. 4.
    Weiss, B.: Fast median and bilateral filtering. ACM Transactions on Graphics (TOG) 25(3), 519–526 (2006)CrossRefGoogle Scholar
  5. 5.
    Perreault, S., Hebert, P.: Median filtering in constant time. IEEE Transactions on Image Processing 16, 2389–2394 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Storozhilova, M., Yurin, D.: Fast rank algorithms based on multiscale histograms. In: 21st International Conference on Computer Graphics GraphiCon 2011, Moscow, Russia, pp. 132–135 (September 2011)Google Scholar
  7. 7.
    Storozhilova, M., Yurin, D.: Fast rank algorithms with multiscale histograms lazy updating. In: 8th Open German-Russian Workshop “Pattern Recognition and Image Understanding” (OGRW-8-2011), Lobachevsky State University of Nizhny Novgorod, pp. 380–383 (November 2011)Google Scholar
  8. 8.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the IEEE Sixth International Conference on Computer Vision (ICCV 1998), pp. 839–846 (1998)Google Scholar
  9. 9.
    Buades, A., Morel, J.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)Google Scholar
  10. 10.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Reiter, M., Zauner, G.: Denoising of computed tomography images using multiresolution based methods. In: European Conference on Non-Destructive Testing poster (2006)Google Scholar
  12. 12.
    Trinh, D., Luong, M., Rocchisani, J.M., Pham, C., Pham, H., Dibos, F.: An optimal weight method for CT image denoising. Journal of Electronic Science and Technology 10(2), 124–129 (2012)Google Scholar
  13. 13.
    Kijewski, M., Judy, P.: The noise power spectrum of CT images. Phys. Med. Biol. 32(5), 565–575 (1987)CrossRefGoogle Scholar
  14. 14.
    Newton, T., Potts, D.: Radiology of the Skull and Brain: Technical Aspects of Computed Tomography. Radiology of the Skull and Brain. Mosby (1981)Google Scholar
  15. 15.
    Putilin, S., Lukin, A.: Non-local means method modification for noise suppression in video. In: 17th International Conference on Computer Graphics, GraphiCon 2007, pp. 257–259 (June 2007) (in Russian)Google Scholar
  16. 16.
    Storozhilova, M., Lukin, A., Yurin, D., Sinitsyn, V.: Two approaches for noise filtering in 3D medical CT-images. In: 22nd International Conference on Computer Graphics, GraphiCon 2012, Moscow, Russia, pp. 68–72 (October 2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maria Storozhilova
    • 1
  • Alexey Lukin
    • 1
  • Dmitry Yurin
    • 1
  • Valentin Sinitsyn
    • 2
  1. 1.Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia
  2. 2.Radiology DepartmentFederal Center of Medicine and RehabilitationMoscowRussia

Personalised recommendations