Skip to main content
SpringerLink
Log in

Fuzzy-based hybrid filter for Rician noise removal

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Magnetic resonance images tend to be contaminated with random unwanted signals called noise, due to various reasons. Noise treatment of magnetic resonance brain images is considered as an important and challenging task for proper clinical and research investigations. In this manuscript, fuzzy logic-based hybrid Rician noise filter has been proposed. Proposed filtering technique uses estimated noise variance along with local and global statistics for the construction of a robust fuzzy membership function. Constructed fuzzy membership function assigns appropriate weights to the statistical estimates, based on their noise removal and detail preservation capability. Fuzzy weighted local and non-local estimators are then used for the restoration of a noisy pixel. Detailed simulations are performed, and restoration results are computed based on well-known performance measures. Numerical and visual results show that the proposed technique gives much better restored images than the existing methodologies.

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

Similar content being viewed by others

References

  1. Ahmed, O.A.: New denoising scheme for magnetic resonance spectroscopy signals. Med. Imaging IEEE Trans. 24(6), 809–816 (2005)

    Article  Google Scholar 

  2. Aja-Fernandez, S., Alberola-Lopez, C., Westin, C.F.: Noise and signal estimation in magnitude mri and rician distributed images: a lmmse approach. Image Process. IEEE Trans. 17(8), 1383–1398 (2008)

    Article  MathSciNet  Google Scholar 

  3. Aja-Fernandez, S., Tristan-Vega, A., Alberola-Lopez, C.: Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models. Magn. Reson. Imaging 27, 1397–1409 (2009)

    Article  Google Scholar 

  4. Ashburner, J., Friston, K.J.: Voxel-based morphometry—the methods. NeuroImage 11(6), 805–821 (2000)

    Article  Google Scholar 

  5. Bloch, I.: Lattices of fuzzy sets and bipolar fuzzy sets, and mathematical morphology. Inf. Sci. 181(10), 2002–2015 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  6. BrainWeb, simulated brain database. http://www.bic.mni.mcgill.ca/brainweb/

  7. Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  8. Coupe, P., Manjon, J.V., Robles, M., Collins, D.L.: Adaptive multiresolution non-local means filter for 3d mr image denoising. IET Image Process. (2011)

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

    Article  MathSciNet  Google Scholar 

  10. Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of mri data. IEEE Trans. Med. Imaging 11(2), 221–232 (1992)

    Article  Google Scholar 

  11. Golshan, H.M., Hasanzadeh, R.P.R.: A modified rician LMMSE estimator for the restoration of magnitude MR images. Opt. Int. J. Light Electron Opt. 124(16), 2387–2392 (2013)

    Article  Google Scholar 

  12. Gonzalez, R.C., Woods, R.E., Boston: Digital image processing (Ch. 6), 2nd edn, pp. 519–566. Addison-Wesley Longman Publishing Co., Inc., Boston (1992)

    Google Scholar 

  13. Gudbjartsson, H., Patz, S.: The rician distribution of noisy mri data. Magn. Reson. Med. 34(6), 910–914 (1995)

    Article  Google Scholar 

  14. Hamid, M.S., Harvey, N.R., Marshall, S.: Genetic algorithm optimization of multidimensional grayscale soft morphological filters with applications in film archive restoration. IEEE Trans. Circuits Syst. Video Technol. 13(5), 406–416 (2003)

    Article  Google Scholar 

  15. Henkelman, R.M.: Measurement of signal intensities in the presence of noise in mr images. Med. Phys. 12(2), 232–233 (1985)

    Article  Google Scholar 

  16. Hussain, A., Bhatti, S.M., Jaffar, M.A.: Fuzzy based impulse noise reduction method. Multimed. Tools Appl. 60, 551–571 (2012)

    Article  Google Scholar 

  17. Hussain, A., Jaffar, M.A., Mirza, A.M.: A hybrid image restoration approach: fuzzy logic and directional weighted median based uniform impulse noise removal. Knowl. Inf. Syst. 24, 77–90 (2010)

  18. ISBR, Internet Brain Segmentation Repository. http://www.cma.mgh.harvard.edu/ibsr/

  19. Krissian, K., Aja-Fernandez, S.: Noise-driven anisotropic diffusion filtering of mri. IEEE Trans. Image Process. 18(10), 2265–2274 (2009)

    Article  MathSciNet  Google Scholar 

  20. Lim, J.S.: Two-dimensional signal and image processing, pp. 469–476. Prentice Hall, Englewood Cliffs (1990)

    Google Scholar 

  21. Lim, J.S.: Two-dimensional signal and image processing, p. 548. Prentice Hall, Englewood Cliffs (1990)

  22. Macovski, A.: Noise in mri. Magn. Reson. Med. 36(3), 494–497 (1996)

  23. Manjon, J.V., Carbonell-Caballero, J., Lull, J.J., Garcia-Marti, G., Marti-Bonmati, L., Robles, M.: Mri denoising using non-local means. Med. Image Anal. 12(4), 514–523 (2008)

  24. Manjon, J.V., Coupe, P., Buades, A., Collins, D.L., Robles, M.: New methods for mri denoising based on sparseness and self-similarity. Med. Image Anal. 16(1), 18–27 (2012)

    Article  Google Scholar 

  25. Matlab: version 7.9.0 (R2009b). The MathWorks Inc., Natick (2009)

  26. McGibney, G., Smith, M.R.: An unbiased signal-to-noise ratio measure for magnetic resonance images. Med. Phys. 20(4), 1077–1078 (1993)

  27. Muresan, D.D., Parks, T.W.: Adaptive principal components and image denoising. In: Image processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on. Vol. 1. pp. 101–104 (2003)

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

    Article  Google Scholar 

  29. Otsu, N.: A threshold selection method from gray-level histograms. Syst. Man Cybern. IEEE Trans. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  31. Pizurica, A., Philips, W., Lemahieu, I., Acheroy, M.: A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans. Med. Imaging 22(3), 323–331 (2003)

    Article  Google Scholar 

  32. Samsonov, A.A., Johnson, C.R.: Noise-adaptive nonlinear diffusion filtering of mr images with spatially varying noise levels. Magn. Reson. Med. 52, 798–806 (2004)

    Article  Google Scholar 

  33. Schulte, S., Witte, V.D., Nachtegael, M., Melange, T., Kerre, E.E.: A new fuzzy additive noise reduction method. In: Image Analysis and Recognition, vol. 4633, pp. 12–23. Springer, Berlin (2007)

  34. Sharif, M., Jaffar, M.A., Mahmood, M.T.: Rician noise reduction by combining mathematical morphological operators through genetic programming. Opt. Rev. 20(4), 289–292 (2013)

    Article  Google Scholar 

  35. Sijbers, J., den Dekker, A.J.: Maximum likelihood estimation of signal amplitude and noise variance from mr data. Magn. Reson. Med. 51(3), 586–594 (2004)

    Article  Google Scholar 

  36. Sijbers, J., Poot, D., den Dekker, A.J., Pintjens, W.: Automatic estimation of the noise variance from the histogram of a magnetic resonance image. Phys. Med. Biol. 52(5), 1335 (2007)

    Article  Google Scholar 

  37. Weaver, J.B., Xu, Y.S., Healy Jr, D.M., Cromwell, L.D.: Filtering noise from images with wavelet transforms. Magn. Reson. Med. 21(2), 288–295 (1991)

    Article  Google Scholar 

  38. Yaroslavsky, L.P., Yaroslavskij, L.P.: Digital Picture Processing. An Introduction, vol. 9. Springer, Berlin (1985)

    Book  MATH  Google Scholar 

  39. Yaroslavsky, L.P., Egiazarian, K.O., Astola, J.T.: Transform domain image restoration methods: review, comparison, and interpretation. In: Proceedings of SPIE 4304, nonlinear image processing and pattern analysis. pp. 155–169 (2001)

Download references

Acknowledgments

This work (2012-0005542) was supported by Mid-career Researcher Program through NRF grant funded by the MEST. Authors would also like to thank the Higher Education Commission (HEC) of Pakistan, for financial support.

Author information

Authors and Affiliations

  1. Department of Computer Science, National University of Computer and Emerging Sciences (FAST-NU), Islamabad, Pakistan

    Muhammad Sharif & Muhammad Arfan Jaffar

  2. Signal and Image Processing Laboratory, Department of Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea

    Ayyaz Hussain & Tae-Sun Choi

  3. International Islamic University, Islamabad, Pakistan

    Ayyaz Hussain

  4. CCIS, Al-Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Muhammad Arfan Jaffar

Authors
  1. Muhammad Sharif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayyaz Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Arfan Jaffar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tae-Sun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayyaz Hussain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharif, M., Hussain, A., Jaffar, M.A. et al. Fuzzy-based hybrid filter for Rician noise removal. SIViP 10, 215–224 (2016). https://doi.org/10.1007/s11760-014-0729-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0729-1

Keywords

Search

Navigation