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 in:Medical image analysis 2008-08, Vol.12 (4), p.514-523
Main Authors: Manjón, José V., Carbonell-Caballero, José, Lull, Juan J., García-Martí, Gracián, Martí-Bonmatí, Luís, Robles, Montserrat
Format: Article
Language:English
Subjects:
Algorithms
Brain - anatomy & histology
Denoising
Humans
Magnetic Resonance Imaging - methods
MRI filtering
Random noise
Spine - anatomy & histology
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Description
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.
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2008.02.004