Publication
Title
An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images
Author
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.
Language
English
Source (book)
IEEE International Symposium on Biomedical Imaging (ISBI 2012)
Publication
S.l. : IEEE, 2012
ISBN
978-1-4577-1858-8
Volume/pages
p. 1136-1139
ISI
000312384100292
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 14.05.2012
Last edited 26.04.2017
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