Analysis and Comparison of Wavelet Transforms For Denoising MRI Image

Medical imaging plays a dominant role in clinical practice like diagnosis, therapy, etc. and research related findings. Medical images are usually contaminated or distorted while acquiring and transmitting the image due to several types of noises, misfocus of camera, disturbance due to blood flow, a...

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Bibliographic Details
Published inBiomedical & pharmacology journal Vol. 10; no. 2; pp. 831 - 836
Main Authors Agarwal, Sugandha, P. Singh, O., Nagaria, Deepak
Format Journal Article
LanguageEnglish
Published Bhopal Biomedical and Pharmacology Journal 2017
Subjects
Atmospheric turbulence
Blood flow
Computer science
Decomposition
Engineering
Image retrieval
Information theory
Magnetic resonance imaging
Medical imaging
Neuroimaging
NMR
Noise
Nuclear magnetic resonance
Researchers
Signal processing
Statistical analysis
Wavelet transforms
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Summary:Medical imaging plays a dominant role in clinical practice like diagnosis, therapy, etc. and research related findings. Medical images are usually contaminated or distorted while acquiring and transmitting the image due to several types of noises, misfocus of camera, disturbance due to blood flow, atmospheric turbulence. So it becomes necessary to apply image denoising processing to improve the quality of image. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper compares the efficiency of wavelet based thresholding techniques in the presence of speckle noise for various wavelet family i.e. Haar, Morlet, Symlet, Daubechies in denoising a medical imaging resonance of brain. Performance estimation and analysis is done using SNR (Signal to Noise Ratio), PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error). Based on the performance evaluation, it is inferred that wavelet transform is more effective as it has an ability to capture the energy of a signal in a few energy transform values usually known as wavelet coefficients.
ISSN:0974-6242
2456-2610
DOI:10.13005/bpj/1174