The EM method in a probabilistic wavelet-based MRI denoising
type: Item
instance of: scholarly article
title: The EM method in a probabilistic wavelet-based MRI denoising
zbMATH Open document ID: 1344.92093
DOI: 10.1155/2015/182659
author: Marcos Martín-Fernández
author: S. Villullas
published in: Computational \& Mathematical Methods in Medicine
publication date: 2016-09-06T00:00:00Z
review text: Summary: Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images.
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 6623969
zbMATH Keywords: wavelet-based MRI denoising
zbMATH Keywords: Gaussian distribution
zbMATH Keywords: expectation maximization
zbMATH Keywords: wavelet coefficients
Wikidata QID: Q40821085
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/182659
OpenAlex ID: W1495458911
cites work: A Context Quantization Approach to Universal Denoising
cites work: Applied mathematics meets signal processing.
cites work: Ideal spatial adaptation by wavelet shrinkage
cites work: Q4139463
cites work: Q4849992
cites work: Singularity detection and processing with wavelets
cites work: Smoothness estimates for soft-threshold denoising via translation-invariant wavelet transforms
description: scientific article; zbMATH DE number 6623969