Independent Component Analysis based Denoising of Magnetic Resonance Images

Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to p...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer applications 2012-01, Vol.54 (2), p.13-18
Main Authors: Sukhatme, Neelabh, Shukla, Shailja
Format: Article
Language:English
Subjects:
Discrete Wavelet Transform
Independent component analysis
Magnetic resonance
Mathematical models
Noise
Preserves
Signal to noise ratio
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Denoising is an essential step for most of the digital image processing systems. Image denoising involves the manipulation of the image data to produce a visually high quality image. MRI images are always corrupted by random noises. In denoising of magnetic resonance images it is very important to preserve the useful details rather than just increasing its peak signal to noise ratio (PSNR) value. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise. This paper uses Independent component analysis (ICA) for denoising of noisy MRI's. A comparative analysis was also performed, the output obtained by independent component analysis were compared with that obtained from discrete wavelet transform (DWT). The comparative analysis shows that the independent component analysis is better than the discrete wavelet transform in terms of peak signal to noise ratio (PSNR), Mean square error (MSE) and Mean structural similarity index metric (MSSIM).
ISSN:0975-8887
0975-8887
DOI:10.5120/8537-2078