New methods for MRI denoising based on sparseness and self-similarity.

José Manjón 1 Pierrick Coupé 2, * Antonio Buades 3, 4 D. Louis Collins 2 Montserrat Robles 1
* Corresponding author
1 ITACA
2 MNI - McConnell Brain Imaging Centre
3 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
4 Dpt Matemàtiques i Informàtica
Abstract : This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.
Keywords : self-similarity filtering DCT sparseness denoising MRI nonlocal means
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Journal articles
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https://www.hal.inserm.fr/inserm-00601866
Contributor : Pierrick Coupé <>
Submitted on : Monday, June 20, 2011 - 11:46:34 PM
Last modification on : Thursday, April 11, 2019 - 4:02:08 PM
Long-term archiving on : Wednesday, September 21, 2011 - 2:26:56 AM

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José Manjón, Pierrick Coupé, Antonio Buades, D. Louis Collins, Montserrat Robles. New methods for MRI denoising based on sparseness and self-similarity.. Medical Image Analysis, Elsevier, 2012, 16 (1), pp.18-27. ⟨10.1016/j.media.2011.04.003⟩. ⟨inserm-00601866⟩

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