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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421746/ https://www.ncbi.nlm.nih.gov/pubmed/30944576 http://dx.doi.org/10.1155/2019/3503267 |
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author | Tan, Ling Yu, Xin |
author_facet | Tan, Ling Yu, Xin |
author_sort | Tan, Ling |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6421746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-64217462019-04-03 Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation Tan, Ling Yu, Xin Comput Math Methods Med Research Article 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. Hindawi 2019-03-03 /pmc/articles/PMC6421746/ /pubmed/30944576 http://dx.doi.org/10.1155/2019/3503267 Text en Copyright © 2019 Ling Tan and Xin Yu. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Tan, Ling Yu, Xin Medical Image Fusion Based on Fast Finite Shearlet Transform and Sparse Representation |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421746/ https://www.ncbi.nlm.nih.gov/pubmed/30944576 http://dx.doi.org/10.1155/2019/3503267 |
work_keys_str_mv | AT tanling medicalimagefusionbasedonfastfiniteshearlettransformandsparserepresentation AT yuxin medicalimagefusionbasedonfastfiniteshearlettransformandsparserepresentation |