Advertisement

Multimedia Tools and Applications

, Volume 72, Issue 1, pp 1–19 | Cite as

Rician noise removal from MR images using novel adapted selective non-local means filter

  • Sultan Zia
  • M. Arfan Jaffar
  • Anwar M. Mirza
  • Tae-Sun Choi
Article

Abstract

The reduction of rician noise from MR images without degradation of the underlying image features has attracted much attention and has a strong potential in several application domains including medical image processing. Interpretation of MR images is difficult due to their tendency to gain rician noise during acquisition. In this work, we proposed a novel selective non-local means algorithm for noise suppression of MR images while preserving the image features as much as possible. We have used morphological gradient operators that separate the image high frequency areas from smooth areas. Later, we have applied novel selective NLM filter with optimal parameter values for different frequency regions of image to remove the noise. A method of selective weight matrix is also proposed to preserve the image features against smoothing. The results of experimentation performed using proposed adapted selective filter prove the soundness of the method. We compared results with the results of many well known techniques presented in literature like NLM with optimized parameters, wavelet based de-noising and anisotropic diffusion filter and discussed the improvements achieved.

Keywords

De-noising Magnetic resonance image Adapted nonlocal means Morphological gradients 

Notes

Acknowledgement

The authors would like to thank Higher Education Commission (HEC), Govt. of Pakistan and Bio Imaging Research Center at GIST, Korea for providing funds and required resources to complete this work.

References

  1. 1.
    Abrar MRI &CT Center, Rawalpindi, Pakistan: http://www.abrarmrict.com/
  2. 2.
    Aelterman J, Bart G, Aleksandra P, Wilfried P (2008) Removal of correlated rician noise. In: Magnetic resonance imaging. European signal processing conferenceGoogle Scholar
  3. 3.
    Alexei AS, Chris RJ (2004) Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magn Reson Med 52:798–806CrossRefGoogle Scholar
  4. 4.
    Alhosainy AM, Badran EF (2009) Adapted non-local means filter using variable window size. Proceedings Int. Conf. on Information Technology (ICIT)Google Scholar
  5. 5.
    Angelino CV, Debreuve E , Barlaud M (2010) Patch confidence k-nearest neighbors denoising. IEEE International Conference on Image Processing (ICIP), Hong Kong, 17thGoogle Scholar
  6. 6.
    Awate SP, Whitaker RT (2007) Feature-preserving MRI denoising: a nonparametric Empirical-Bayes approach. IEEE Trans Med Imaging 26(9):1242–1255CrossRefGoogle Scholar
  7. 7.
    Bilcu RC, Vehvilainen M (2007) Fast nonlocal means for image denoising. Proc SPIE Digit Photogr III 6502Google Scholar
  8. 8.
    BrainWeb: Simulated brain database available at http://www.bic.mni.mcgill.ca/brainweb/
  9. 9.
    Brox T, Cremers D (2007) Iterated nonlocal means for texture restoration. Proc Int Conf Scale Space Variational Methods Comput Visions:13–24Google Scholar
  10. 10.
    Buades A, Coll B, Morel J (2005) A review of image denoising algorithms, with a new one. SIAM Interdiscip J Multiscale Model Simul 4(2):290–530MathSciNetGoogle Scholar
  11. 11.
    Buades A, Coll B, Morel J (2005) A non local algorithm for image denoising. Proc Int Conf Comput Vis Pattern Recognit (CVPR) 2:60–65Google Scholar
  12. 12.
    Coifman RR, Donoho D (1995) Translation-invariant de-noising in wavelets and statistics. Springer, New York, pp 125–150Google Scholar
  13. 13.
    Coupe P, Yger P, Barillot C (2006) Fast non local means denoising for 3D MR images. Med Image Comput Comput Assist Interv :33–40Google Scholar
  14. 14.
    Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C (2008) An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27(4):425–441CrossRefGoogle Scholar
  15. 15.
    Coupe P, Hellier P, Prima S, Kervrann C, Barillot C (2008) 3D wavelet subbands mixing for image denoising. Int J Biomed Imag 2008Google Scholar
  16. 16.
    Dauwe A, Goossens B, Luong H, Philips W (2008) A fast non-local image denoising algorithm. Proc SPIE Electron Imaging 6812:681210. doi: 10.1117/12.765505 CrossRefGoogle Scholar
  17. 17.
    Delakis I, Hammad O, Kitney RI (2007) Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI). Phys Med Biol 52:3741–3751CrossRefGoogle Scholar
  18. 18.
    Goossens B, Luong H, Pizurica A, Philips W (2008) An improved non-local means algorithm for image denoising. International workshop on local and nonlocal approximation in image processingGoogle Scholar
  19. 19.
    Jean S (1982) Image analysis and mathematical morphology. Academic, LondonGoogle Scholar
  20. 20.
    Kervrann C, Boulanger J (2006) Optimal spatial adaptation for patch-based image denoising. IEEE Trans Image Process 15(10):2866–2878CrossRefGoogle Scholar
  21. 21.
    Kervrann C, Boulanger J, Coupé P (2007) Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. Scale Space Variational Methods Comput Vis :520–532Google Scholar
  22. 22.
    Mahmoudi M, Sapiro G (2005) Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Process Lett 12(12):839–842CrossRefGoogle Scholar
  23. 23.
    Manjon JV, Caballero JC, Lull JJ, Marti GG, Bonmat LM, Robles M (2008) MRI denoising using non-local means. Med Image Anal 12:514–523CrossRefGoogle Scholar
  24. 24.
    Murase K, Yamazaki Y, Shinohara M, Kawakami K, Kikuchi K, Miki H, Mochizuki T, Ikezoe J (2001) An anisotropic diffusion method for denoising dynamic susceptibility contrast enhanced magnetic resonance images. Phys Med Biol 46:2713–2723CrossRefGoogle Scholar
  25. 25.
    Nowak RD (1999) Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans Image Process 8(10):1408–1419CrossRefGoogle Scholar
  26. 26.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man and Cybern 9(1):62–69CrossRefMathSciNetGoogle Scholar
  27. 27.
    Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629–639CrossRefGoogle Scholar
  28. 28.
    Petersson KM, Nichols TE, Poline JB, Holmes AP (1999) Statistical limitations in functional neuroimaging I. Non-inferential methods and statistical models. Phil Trans R Soc B Biol Sci 354:1239–1260CrossRefGoogle Scholar
  29. 29.
    Pizurica A, Philips W, Lemahieu I, Acheroy M (2003) A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imaging 22(3):323–331CrossRefGoogle Scholar
  30. 30.
    Rivest JF, Soille P, Beucher S (1993) Morphological gradients. J Electron Imag :326–336Google Scholar
  31. 31.
    Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D Nonlinear Phenom 60(1):259–268CrossRefzbMATHGoogle Scholar
  32. 32.
    Salmon J, Strozecki Y (2010) From patches to pixels in non-local methods: weighted-average reprojection. IEEE International Conference on Image Processing (ICIP), 17thGoogle Scholar
  33. 33.
    Shechtman E, Irani M (2007) Matching local self-similarities across images and videos. IEEE conference on computer vision and pattern recognition, JuneGoogle Scholar
  34. 34.
    Sijbers J, den Dekker AJ (2004) Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn Reson Med 51(3):586–594CrossRefGoogle Scholar
  35. 35.
    Sijbers J, den Dekker AJ, Scheunders P, Dyck DV (1998) Maximum-likelihood estimation of Rician distribution parameters. IEEE Trans Med Imaging 17:357–361CrossRefGoogle Scholar
  36. 36.
    Wink AM, Roerdink JBTM (2004) Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing. IEEE Trans Med Imaging 23:374–387CrossRefGoogle Scholar
  37. 37.
    Yaroslavsky LP (1985) Digital picture processing - an introduction. Springer VerlagGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Sultan Zia
    • 1
  • M. Arfan Jaffar
    • 1
    • 2
  • Anwar M. Mirza
    • 3
  • Tae-Sun Choi
    • 2
  1. 1.National University of Computer & Emerging SciencesIslamabadPakistan
  2. 2.Gwangju Institute of Science and TechnologyGwangjuSouth Korea
  3. 3.King Saud UniversityRiyadhSaudi Arabia

Personalised recommendations