MRI denoising using Non-Local Means

Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter,...

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Bibliographic Details
Published inMedical image analysis Vol. 12; no. 4; pp. 514 - 523
Main Authors Manjón, José V., Carbonell-Caballero, José, Lull, Juan J., García-Martí, Gracián, Martí-Bonmatí, Luís, Robles, Montserrat
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.08.2008
Subjects
Algorithms
Brain - anatomy & histology
Denoising
Humans
Magnetic Resonance Imaging - methods
MRI filtering
Random noise
Spine - anatomy & histology
TV
MRI filtering
Random noise
ADF
SNR
BOLD
RMSE
UNLM
NSA
NLM
Denoising
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Summary:Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.
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ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2008.02.004