Skip to main content
SpringerLink
Log in

DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 463))

Abstract

Diffusion tensor imaging (DTI) is an imaging modality that has developed in recent years. It is a non-invasive technique and needn’t contrast medium. However, the SNR of DTI data is relatively low and clinically polluted by noise, which can bring serious impacts on tensor calculating, fiber tracking and other post-processing. In order to reduce the influence of noise on DTI images and improve the efficiency of diffusion tensor imaging effectively, a new DTI denoising scheme is proposed by combining the complex Shearlet transform and complex diffusion anisotropic filtering. The experiment results acquired from the simulated and real data prove the good performance of the presented algorithm.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nowak, R.D.: Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 8(10), 1408–1419 (1999)

    Google Scholar 

  2. Saurav, B., Thomas, F., Ross, W.: Rician noise removal in diffusion tensor MRI. In: MICCAI 2006, pp. 117–125. Springer (2006)

    Google Scholar 

  3. Xu, Q., Anderson, A.W., Gore, J.C., Ding, Z.H.: Diffusion tensor image smoothing using efficient and effective anisotropic filtering. In: IEEE International Conference on Computer Vision, pp. 134–145. IEEE Press (2007)

    Google Scholar 

  4. Zhang, X.F., Zhang, H.M., Tian, W.F.: Restoring DTI images based on complex diffusion process and fiber tracking. Jisuanji Yingyong Yu Ruanjian 26(6), 13–14 (2009)

    Google Scholar 

  5. Liu, F., Ruan, X.E.: Wavelet-based diffusion approaches for signal denoising. Sig. Process. 87(5), 1138–1146 (2007)

    Google Scholar 

  6. Chan, T.F., Zhou, H.M.: Total variation wavelet thresholding. J. Sci. Comput. 32(2), 315–341 (2007)

    Google Scholar 

  7. Do, M.N., Vetterli, M.: Contourlets: a directional multiresolution image representation. In: IEEE International Conference on Image Processing, pp. 357–360. IEEE Press (2002)

    Google Scholar 

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

    Google Scholar 

  9. Liu, S.Q., Hu, S.H., Xiao, Y.: Image separation using wavelet-complex shearlet dictionary. J. Syst. Eng. Electron. 25(2), 314–321 (2014)

    Google Scholar 

  10. Zhang, X., Lu, B.L., Ma, Y., et al.: Denoising diffusion tensor images with shearlet. In: International Conference on Signal Processing, pp. 962–965. IEEE Press (2012)

    Google Scholar 

  11. Zhang, X., Liu, X., Ma, Y.: A new DTI image denoising method based on shearlet shrinkage and complex diffusion. In: International Congress on Image and Signal Processing, pp. 229–233. IEEE Press (2014)

    Google Scholar 

  12. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Google Scholar 

  13. Gilboa, G., Sochen, N., Zeevi, Y.Y.: Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Trans. Image Process. 11, 689–703 (2002)

    Google Scholar 

  14. Kai, K., Menzel, M.I., Scharr, H.: A Riemannian Bayesian framework for estimating diffusion tensor images. Int. J. Comput. Vis. 120, 1–28 (2016)

    Google Scholar 

Download references

Acknowledgment

This work was supported by Natural Science Foundation of China under grant 61401308 and 61572063, Natural Science Foundation of Hebei Province under grant F2016201142 and F2016201187, Science research project of Hebei Province under grant QN2016085 and ZC2016040, Natural Science Foundation of Hebei University under grant 2014-303, Post-graduate’s Innovation Fund Project of Hebei University under grant X201710.

Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Hebei University, Baoding, China

    Shuaiqi Liu, Pengfei Li, Qi Hu & Jie Zhao

  2. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, China

    Shuaiqi Liu, Pengfei Li, Qi Hu & Jie Zhao

  3. Department of Personnel, Hebei University, Baoding, China

    Ming Liu

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Mingzhu Shi

Authors
  1. Shuaiqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengfei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mingzhu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuaiqi Liu .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Min Jia

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xuhong Feng

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, S., Li, P., Liu, M., Hu, Q., Shi, M., Zhao, J. (2019). DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_86

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6571-2_86

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6570-5

  • Online ISBN: 978-981-10-6571-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation