Robust Rician noise estimation for MR images. - Inserm - Institut national de la santé et de la recherche médicale Accéder directement au contenu
Article Dans Une Revue Medical Image Analysis Année : 2010

Robust Rician noise estimation for MR images.

Résumé

In this paper, a new object-based method to estimate noise in magnitude MR images is proposed. The main advantage of this object-based method is its robustness to background artefacts such as ghosting. The proposed method is based on the adaptation of the Median Absolute Deviation (MAD) estimator in the wavelet domain for Rician noise. The MAD is a robust and efficient estimator initially proposed to estimate Gaussian noise. In this work, the adaptation of MAD operator for Rician noise is performed by using only the wavelet coefficients corresponding to the object and by correcting the estimation with an iterative scheme based on the SNR of the image. During the evaluation, a comparison of the proposed method with several state-of-the-art methods is performed. A quantitative validation on synthetic phantom with and without artefacts is presented. A new validation framework is proposed to perform quantitative validation on real data. The impact of the accuracy of noise estimation on the performance of a denoising filter is also studied. The results obtained on synthetic images show the accuracy and the robustness of the proposed method. Within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.

Mots clés

Fichier principal
Vignette du fichier
FinalVersionResub_last.pdf (7.59 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inserm-00486495 , version 1 (25-05-2010)

Identifiants

Citer

Pierrick Coupé, José V. Manjón, Elias Gedamu, Douglas L. Arnold, Montserrat Robles, et al.. Robust Rician noise estimation for MR images.. Medical Image Analysis, 2010, 14 (4), pp.483-93. ⟨10.1016/j.media.2010.03.001⟩. ⟨inserm-00486495⟩

Collections

INSERM
827 Consultations
2402 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More