Title
An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images
Author
Faculty/Department
Faculty of Sciences. Physics
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
bookPart
Publication
S.l. :IEEE, [*]
Subject
Physics
Biology
Human medicine
Computer. Automation
Source (book)
IEEE International Symposium on Biomedical Imaging (ISBI 2012)
ISBN
978-1-4577-1858-8
ISI
000312384100292
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
Effective denoising is vital for proper analysis and accurate quantitative measurements from Magnetic Resonance (MR) images. Apart from following the general criteria for denoising, the algorithms that deal with MR images should also take into account the bias generated due to the Rician nature of the noise in the magnitude MR images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising MR images. However, one drawback of the existing non local ML estimation method is the usage of a fixed sample size for ML estimation. As a result, optimal results cannot be achieved because of over or under smoothing. In this work, we propose an adaptive non local ML estimation method for denoising MR images in which the samples are selected in an adaptive way for the ML estimation of the true underlying signal. The method has been tested both on simulated and real data, showing its effectiveness.
E-info
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