Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Rician noise attenuation in the wavelet packet transformed domain for brain MRI

Rician noise attenuation in the wavelet packet transformed domain for brain MRI Preprocessing stage for denoising is a crucial task in image analysis in general, and in computer-aided diagnosis using medical images in particular. Standard acquisition of Magnetic Resonance Images (MRI) presents statistical Rician noise which degrades the performance of the image analysis. This paper presents a new technique to reduce Rician noise of brain MRI. The new method for noise filtering is achieved in the discrete Wavelet Packets Transform (WPT) domain. Four methodologies for thresholding the detail coefficients in the same 2D WPT domain have been experimented considering two scenarios (with and without a previous adaptive Wiener filtering in the spatial domain). Best quantitative and qualitative results have been obtained by the new method presented in this work (specifically tailored for brain MRI), which is adaptive to each subband and dependent on the data. It has been compared with other traditional methods considering the Signal to Noise Ratio (SNR), Normalized Cross Correlation (NCC) and execution time (∼ 0.1 s/slice). A complete dataset of structural (T1-w) brain MRI of the BrainWeb database has been used for experiments. An important aspect is that these experiments with synthetic images proved that the common prior adaptive Wiener filtering often used by many authors is a dispensable procedure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Integrated Computer-Aided Engineering IOS Press

Rician noise attenuation in the wavelet packet transformed domain for brain MRI

Loading next page...
 
/lp/ios-press/rician-noise-attenuation-in-the-wavelet-packet-transformed-domain-for-AI07f8D8hF

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
IOS Press
Copyright
Copyright © 2014 by IOS Press, Inc
ISSN
1069-2509
eISSN
1875-8835
DOI
10.3233/ICA-130457
Publisher site
See Article on Publisher Site

Abstract

Preprocessing stage for denoising is a crucial task in image analysis in general, and in computer-aided diagnosis using medical images in particular. Standard acquisition of Magnetic Resonance Images (MRI) presents statistical Rician noise which degrades the performance of the image analysis. This paper presents a new technique to reduce Rician noise of brain MRI. The new method for noise filtering is achieved in the discrete Wavelet Packets Transform (WPT) domain. Four methodologies for thresholding the detail coefficients in the same 2D WPT domain have been experimented considering two scenarios (with and without a previous adaptive Wiener filtering in the spatial domain). Best quantitative and qualitative results have been obtained by the new method presented in this work (specifically tailored for brain MRI), which is adaptive to each subband and dependent on the data. It has been compared with other traditional methods considering the Signal to Noise Ratio (SNR), Normalized Cross Correlation (NCC) and execution time (∼ 0.1 s/slice). A complete dataset of structural (T1-w) brain MRI of the BrainWeb database has been used for experiments. An important aspect is that these experiments with synthetic images proved that the common prior adaptive Wiener filtering often used by many authors is a dispensable procedure.

Journal

Integrated Computer-Aided EngineeringIOS Press

Published: Jan 1, 2014

There are no references for this article.