A wavelet multiscale denoising algorithm for magnetic resonance (MR) images

Xiaofeng Yang, Baowei Fei

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR 'sinogram' into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities.

Original languageEnglish (US)
Article number025803
JournalMeasurement Science and Technology
Volume22
Issue number2
DOIs
StatePublished - Feb 2011

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Keywords

  • Magnetic resonance imaging (MRI)
  • Multiscale denoising
  • Radon transform
  • Rician distribution
  • Translation invariant
  • Wavelet transform

ASJC Scopus subject areas

  • Instrumentation
  • Engineering (miscellaneous)
  • Applied Mathematics

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