Skip to main content
SpringerLink
Log in

Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

  • New Trends in data pre-processing methods for signal and image classification
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Magnetic resonance images (MRIs) are sensitive to redundant Rician noise. The proposed adaptive hexagonal fuzzy hybrid filtering technique adapts itself to remove Rician noise variances. The removal of noise variance is performed by constructing a hexagonal membership function along with local and nonlocal filters. The statistical feature such as local mean (μ i) and global mean (μ g) is determined to find fuzzy weights by constructing a hexagonal membership function for nonlocal filter to preserve the structural information and for local filter to preserve edges. The restoration is performed by multiplying its corresponding fuzzy weight with the restored image of local and nonlocal filter in order to improve the quality of an image. Detailed simulation is performed for Brain Web database and real MRI images at various noise levels using the proposed adaptive hexagonal fuzzy hybrid filtering algorithm and existing algorithms. The visual and diagnostic qualities of the denoised image are well preserved for the proposed adaptive hexagonal fuzzy hybrid filter both at low and high densities of Rician noise.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Anand CS, Sahambi JS (2010) Wavelet domain non-linear filtering for MRI denoising. Magn Reson Imaging 28:842–861

    Article  Google Scholar 

  2. Bhujle HV, Chaudhuri S (2013) Laplacian based non-local means denoising of MR images with rician noise. Magn Reson Imaging 31:1599–1610

    Article  Google Scholar 

  3. BrainWeb, simulated brain database. http://www.bic.mni.mcgill.ca/brainweb. Accessed 16 Feb 2016

  4. Budak C, Tiirk M, Toprak A (2014) Reduction in impulse noise in digital images through a new adaptive artificial neural network model. Neural Comput Appl 26:835–843

    Article  Google Scholar 

  5. Chenyang Z, Wenrui H, Tianyu J, Zhonglei M (2015) Nonlocal image denoising via adaptive tensor nuclear norm minimization. Neural Comput Appl. doi:10.1007/s00521-015-2050-5

    Google Scholar 

  6. Coupé P, Manjón JV, Elias G, Douglas A, Robles M, Louis CD (2010) Robust rician noise estimation for MR images. Med Image Anal 14:483–493

    Article  Google Scholar 

  7. Golshan HM, Hasanzadeh RPR (2013) A modified rician LMMSE estimator for the restoration of magnitude MR images. Opt-Int J Light Electron Opt 124(16):2387–2392

    Article  Google Scholar 

  8. Liu B, Sang X, Xing S, Wang B (2015) Noise suppression in brain magnetic resonance imaging based on non-local means filter and fuzzy cluster. Opt-Int J Light Electron Opt 126(21):2955–2959

    Article  Google Scholar 

  9. Manjon JV, Carbonell-Caballero J, Lull JJ, Garcia-Marti G, Marti-Bonmati L, Robles M (2008) MRI denoising using non-local means. Med Image Anal 12:514–552

    Article  Google Scholar 

  10. Manjon JV, Coupe P, Buades A, Louis CD, Robles M (2012) New methods for MRI denoising based on sparseness and self-similarity. Med Image Anal 16(1):18–27

    Article  Google Scholar 

  11. Mariem BA, Jihene M, Ahmad TA, Hafedh B, Julio EM, Karl K (2015) Adaptive noise-reducing anisotropic diffusion filter. Neural Comput Appl. doi:10.1007/s00521-015-1933-9

    Google Scholar 

  12. Prashant A, Robert X, Perry R, Adrian N, Graham WA (2015) Multiscale Properties of weighted total variation flow with applications to denoising and registration. Med Image Anal 23(1):28–42

    Article  Google Scholar 

  13. Riji R, Jeny R, Jan S, Madhu NS (2014) Iterative bilateral filter for Rician noise reduction in MR images. Signal Video Image Process 9(7):1543–1548

    Article  Google Scholar 

  14. Sharif M, Hussain A, Jaffar MA, Choi TS (2015) Fuzzy similarity based non local means filter for rician noise removal. Multimed Tools Appl 74(15):5533–5556

    Article  Google Scholar 

  15. Sharif M, Hussain A, Jaffar MA, Choi TS (2016) Fuzzy-based hybrid filter for rician noise removal. SIViP 10(2):215–224

    Article  Google Scholar 

  16. Tuan PD (2012) Supervised restoration of degraded medical images using multiple-point geostatistics. Comput Methods Programs Biomed 106:201–209

    Article  Google Scholar 

  17. Zhang L, Bao P, Wu X (2005) Multiscale LMMSE based image denoising with optimal wavelet selection. IEEE Trans Circuits Syst Video Technol 15(4):69–481

    Google Scholar 

Download references

Acknowledgement

The authors are grateful for the financial support provided by University Grants Commission (UGC) under Rajiv Gandhi National Fellowship, New Delhi, India. Grant Number: F1-17.1/2016-17/RGNF-2015-17-SC-TAM-23661.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamilnadu, 641 013, India

    R. Kala & P. Deepa

Authors
  1. R. Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Deepa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Kala.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kala, R., Deepa, P. Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images. Neural Comput & Applic 29, 237–249 (2018). https://doi.org/10.1007/s00521-017-2953-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-2953-4

Keywords

Search

Navigation