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


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.


De-noising Magnetic resonance image Adapted nonlocal means Morphological gradients 



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.


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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

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