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DICTA
2003

Adaptive Magnetic Resonance Image Denoising Using Mixture Model and Wavelet Shrinkage

13 years 5 months ago
Adaptive Magnetic Resonance Image Denoising Using Mixture Model and Wavelet Shrinkage
Abstract. This paper proposes a new adaptive wavelet-based Magnetic Resonance images denoising algorithm. A Rician distribution for background-noise modelling is introduced and a Maximum-Likelihood method for the parameter estimation procedure is used. Further discrimination between edge- and noise-related coefficients is achieved by updating the shrinkage function along consecutive scales and applying spatial constraints. The efficacy of the algorithm is demonstrated on both simulated and real Magnetic Resonance images. The results is shown to be promising and outperform other denoising approaches.
Lei Jiang, Wenhui Yang
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where DICTA
Authors Lei Jiang, Wenhui Yang
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