Abstract : In this paper, an adaptive multiresolution version of the Blockwise Non-Local (NL-) means filter is presented for 3D Magnetic Resonance (MR) images. Based on an adaptive soft wavelet coefficient mixing, the proposed filter implicitly adapts the amount of denoising according to the spatial and frequency information contained in the image. Two versions of the filter are described for Gaussian and Rician noise. Quantitative validation was carried out on Brainweb datasets by using several quality metrics. The results show that the proposed multiresolution filter obtained competitive performance compared to recently proposed Rician NL-means filters. Finally, qualitative experiments on anatomical and Diffusion-Weighted MR images show that the proposed filter efficiently removes noise while preserving fine structures in classical and very noisy cases. The impact of the proposed denoising method on fiber tracking is also presented on a HARDI dataset.
Type de document :
Article dans une revue
IET Image Processing, Institution of Engineering and Technology, 2011
https://hal.archives-ouvertes.fr/hal-00645538
Contributeur : Pierrick Coupé
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Soumis le : lundi 28 novembre 2011 - 11:40:46
Dernière modification le : vendredi 13 avril 2018 - 19:54:13
Document(s) archivé(s) le : vendredi 16 novembre 2012 - 12:12:42
Pierrick Coupé, José Manjón, Montserrat Robles, Louis Collins. Adaptive Multiresolution Non-Local Means Filter for 3D MR Image Denoising. IET Image Processing, Institution of Engineering and Technology, 2011. 〈hal-00645538〉
