Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation
Clinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the probl...
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Online Access: | http://dx.doi.org/10.1155/2019/3503267 |
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doaj-112199f9b80b46c0a33c13ad55b00fff2020-11-25T01:38:09ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182019-01-01201910.1155/2019/35032673503267Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse RepresentationLing Tan0Xin Yu1School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaClinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the problem of poor clarity of edge details that is conducive to maintaining the details of source image in current algorithms. Firstly, the source image is decomposed into low-frequency coefficients and high-frequency coefficients by FFST. Secondly, the K-SVD method is used to train the low-frequency coefficients to obtain the overcomplete dictionary D, and then the OMP algorithm sparsely encodes the low-frequency coefficients to complete the fusion of the low-frequency coefficients. Then, a high-frequency coefficient is applied to excite a pulse-coupled neural network, and the fusion coefficient of the high-frequency coefficient is selected according to the number of ignitions. Finally, the fused low-frequency coefficient and high-frequency coefficient are reconstructed into the fused medical image by FFST inverse transform. The experimental results show that the image fusion result of the proposed algorithm is about 35% higher than the comparison algorithms for the edge information transfer factor QAB/F index and has achieved good results in both subjective visual effects and objective evaluation indicators.http://dx.doi.org/10.1155/2019/3503267 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ling Tan Xin Yu |
spellingShingle |
Ling Tan Xin Yu Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation Computational and Mathematical Methods in Medicine |
author_facet |
Ling Tan Xin Yu |
author_sort |
Ling Tan |
title |
Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title_short |
Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title_full |
Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title_fullStr |
Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title_full_unstemmed |
Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title_sort |
medical image fusion based on fast finite shearlet transform and sparse representation |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2019-01-01 |
description |
Clinical diagnosis has high requirements for the visual effect of medical images. To obtain rich detail features and clear edges for fusion medical images, an image fusion algorithm FFST-SR-PCNN based on fast finite shearlet transform (FFST) and sparse representation is proposed, aiming at the problem of poor clarity of edge details that is conducive to maintaining the details of source image in current algorithms. Firstly, the source image is decomposed into low-frequency coefficients and high-frequency coefficients by FFST. Secondly, the K-SVD method is used to train the low-frequency coefficients to obtain the overcomplete dictionary D, and then the OMP algorithm sparsely encodes the low-frequency coefficients to complete the fusion of the low-frequency coefficients. Then, a high-frequency coefficient is applied to excite a pulse-coupled neural network, and the fusion coefficient of the high-frequency coefficient is selected according to the number of ignitions. Finally, the fused low-frequency coefficient and high-frequency coefficient are reconstructed into the fused medical image by FFST inverse transform. The experimental results show that the image fusion result of the proposed algorithm is about 35% higher than the comparison algorithms for the edge information transfer factor QAB/F index and has achieved good results in both subjective visual effects and objective evaluation indicators. |
url |
http://dx.doi.org/10.1155/2019/3503267 |
work_keys_str_mv |
AT lingtan medicalimagefusionbasedonfastfiniteshearlettransformandsparserepresentation AT xinyu medicalimagefusionbasedonfastfiniteshearlettransformandsparserepresentation |
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1725054787771170816 |