Skip to main content

Advertisement

Springer Nature Link
Log in

A Novel Denoising Method for Medical CT Images Based on Moving Decomposition Framework

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In the past decades, CT images have been widely used and played a critical role in medical diagnosis. However, low-dose CT images are often contaminated by noise, this being the most important factor affecting the quality of a CT image. This paper proposes a novel integrated framework and a denoising method for low-dose medical CT images to obtain a better denoising effect whilst at the same time preserving an image’s local structure information. First, an image moving decomposition is employed to decompose the CT image. The original CT noisy image is decomposed, and the components will be processed separately, so that the details and edges of the CT image can be better preserved. Next, the Shearlet Transformation-based denoising method is applied to the component which contains edges and detailed information of the CT image. The multi-directionality and multi-scale property of the Shearlet make it possible to obtain better effect in denoising the detail parts. BM3D filtering is used to remove noise in the component similar to the origin image, and obtain ideal denoising results in denoising the approximate components (mainly low frequency part) of the CT image. With the two processed components and the inverse decomposition, the denoised image is obtained. Finally, simulations and clinical experiments are conducted and comparisons made. The experimental results show the proposed denoising method can obtain better performances in terms of PSNR value, SSIM and FoM and thus have very competitive results compared with other existing CT denoising methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

We confirm that the data is available upon reasonable request.

References

  1. M. Aharon, M. Elad, A. Bruckstein, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  2. A. Buades, B. Coll, J.M. Morel, A Non-Local Algorithm for Image Denoising, Computer Vision and Pattern Recognition. CVPR 2005. IEEE Computer Society Conference (2005)

  3. M. Chen, Y.F. Pu, Y.C. Bai, A fractional-order variational residual CNN for low dose CT Image denoising, in Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science, vol. 11643, ed. by D.S. Huang, V. Bevilacqua, P. Premaratne (Springer, Cham, 2019)

    Google Scholar 

  4. Y. Chen, X. Yin, L. Shi et al., Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Phys. Med. Biol. 58(16), 5803–5820 (2013)

    Article  Google Scholar 

  5. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  6. Z. Dan, X. Chen, H. Gan, C. Gao, Locally Adaptive Shearlet Denoising Based on Bayesian MAP Estimate. in Sixth International Conference on Image and Graphics (IEEE Computer Society, HeFeihina, 2011)

  7. M. Diwakar, M. Kumar, A review on CT image noise and its denoising. Biomed. Signal Process. Control 42(1), 73–88 (2018)

    Article  Google Scholar 

  8. M. Diwakar, M. Kumar, CT image denoising using NLM and correlation-based wavelet packet thresholding. IEEE Image Process. 12(5), 708–715 (2018)

    Article  Google Scholar 

  9. G. Ghimpeteanu, T. Batard, M. Bertalmío, S. Levine, A decomposition framework for image denoising algorithms. IEEE Trans. Image Process. 25(1), 388–399 (2016)

    Article  MathSciNet  Google Scholar 

  10. K. Guo, D. Labate, Optimally sparse multidimensional representation using shearlets. SIAM J. Math. Anal. 39(1), 298–318 (2007)

    Article  MathSciNet  Google Scholar 

  11. M. Kumar, M. Diwakar, A new exponentially directional weighted function based CT image denoising using total variation. J. King Saud Univ. Comput. Inf. Sci. (2016). https://doi.org/10.1016/j.jksuci.2016.12.002

    Article  Google Scholar 

  12. G. Kutyniok, D. Labate et al., Shearlets: Multiscale Analysis for Multivariate Data (Birkhäuser, Boston, 2012)

    Book  Google Scholar 

  13. G. Kutyniok, W. Lim, G. Steidl, Shearlets: theory and applications. GAMM- Mitteilungen 37(2), 259–280 (2014)

    Article  MathSciNet  Google Scholar 

  14. Y.J. Li, J. Zhang, M. Wang, Improved BM3D denoising method. IET Image Proc. 11(12), 1197–1204 (2017)

    Article  Google Scholar 

  15. Z. Li, L. Yu, J.D. Trzasko et al., Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41(1), 011908 (2014)

    Article  Google Scholar 

  16. J. Ma, J. Huang, Q. Feng et al., Low-dose computed tomography image restoration using previous normal-dose scan. Med. Phys. 38(10), 5713–5731 (2011)

    Article  Google Scholar 

  17. S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, San Diego, 1998)

    MATH  Google Scholar 

  18. C. McLeavy, M. Chunara, R. Gravell, A. Rauf, A. Cushnie, C.S. Talbot et al., The future of CT: deep learning reconstruction. Clin. Radiol. 76, 407–15 (2021)

    Article  Google Scholar 

  19. J. Mohan, V. Krishnaveni, Y. Guo, A survey on the magnetic resonance image denoising methods. Biomed. Signal Process. Control 9(1), 56–69 (2014)

    Article  Google Scholar 

  20. C.W. Tian, Y. Xua, Z.Y. Li, W. Zuo, L. Fei, H. Liu, Attention-guided CNN for image denoising. Neural Netw. 124, 117–129 (2020)

    Article  Google Scholar 

  21. Q. Yang, P. Yan, Y. Zhang et al., Low dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

    Article  Google Scholar 

  22. S. Yi, D. Labate, G.R. Easley, H. Krim, A Shearlet approach to edge analysis and detection. IEEE Trans. Image Process. 18(5), 929–941 (2009)

    Article  MathSciNet  Google Scholar 

  23. K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–55 (2017)

    Article  MathSciNet  Google Scholar 

  24. K. Zhang, W. Zuo, L. Zhang, FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27, 4608–22 (2018)

    Article  MathSciNet  Google Scholar 

  25. J. Zhang, H.L. Zhou, Y. Niu, J.C. Lv, J. Chen, Y. Cheng, CNN and multi-feature extraction based denoising of medical CT images. Biomed. Signal Process. Control 67, 102545 (2021)

    Article  Google Scholar 

  26. H. Zhong, K. Ma, Y. Zhou, Modified BM3D algorithm for image denoising using nonlocal centralization prior. Signal Process. 106(2), 342–347 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (60974042).

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China

    Ju Zhang

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    JinCheng Lv

  3. Department of Ultrasound, Zhejiang Hospital, Hangzhou, China

    Yun Cheng

Authors
  1. Ju Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JinCheng Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Cheng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Lv, J. & Cheng, Y. A Novel Denoising Method for Medical CT Images Based on Moving Decomposition Framework. Circuits Syst Signal Process 41, 6885–6905 (2022). https://doi.org/10.1007/s00034-022-02084-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02084-6

Keywords