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Medical Image fusion with Stationary Wavelet Transform and Genetic Algorithm

Published on September 2016 by Amandeep Kaur, Reecha Sharma
International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 10
September 2016
Authors: Amandeep Kaur, Reecha Sharma

Amandeep Kaur, Reecha Sharma . Medical Image fusion with Stationary Wavelet Transform and Genetic Algorithm. International Conference on Advances in Emerging Technology. ICAET2016, 10 (September 2016), 1-4.

author = { Amandeep Kaur, Reecha Sharma },
title = { Medical Image fusion with Stationary Wavelet Transform and Genetic Algorithm },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 10 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/icaet2016/number10/25938-t153/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Amandeep Kaur
%A Reecha Sharma
%T Medical Image fusion with Stationary Wavelet Transform and Genetic Algorithm
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 10
%P 1-4
%D 2016
%I International Journal of Computer Applications

The complementary nature of medical imaging sensors of different modalities, (X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT)), all brought a great need of image fusion to extract relevant information from medical images. Medical image fusion using Stationary wavelet transform (SWT) and optimize result using genetic algorithm (GA) has been implemented and demonstrated in PC MATLAB. In this paper medical CT and MRI images are fused. To overcomes the discrete wavelet transform (DWT) problems that suffers from translation variant property which may extract different feature from two source images taken from same sensor with only slight movement. This paper utilizes SWT instead of DWT to get rid of these restrictions and performance of purposed algorithm is measured by peak signal to noise ratio (PSNR), entropy, root mean square error (RMSE), standard deviation.

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Index Terms

Computer Science
Information Sciences


Medical Image Fusion Stationary Wavelet Transform Genetic Algorithm Psnr