ArticleComputational and mathematical methods in medicine2015
The EM Method in a Probabilistic Wavelet-Based MRI Denoising.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Continuum topological derivative - a novel application tool for denoising CT and MRI medical images.BMC medical imaging · 2024Article
- Speed Up of Volumetric Non-Local Transform-Domain Filter Utilising HPC Architecture.Journal of imaging · 2023Article
- Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold.Oxidative medicine and cellular longevity · 2021Article
- Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.Japanese journal of radiology · 2018Article
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2 authors.
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Abstract
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.
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