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
* Auteur correspondant
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
Type de document :
Article dans une revue
Medical Image Analysis, Elsevier, 2012, 16 (1), pp.18-27. 〈10.1016/j.media.2011.04.003〉
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https://www.hal.inserm.fr/inserm-00601866
Contributeur : Pierrick Coupé <>
Soumis le : lundi 20 juin 2011 - 23:46:34
Dernière modification le : jeudi 7 février 2019 - 16:38:59
Document(s) archivé(s) le : mercredi 21 septembre 2011 - 02:26:56

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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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