Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation
A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation...
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Hindawi Limited
2021-01-01
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Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2021/1544955 |
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doaj-4cd96595f4a04497b0fc72c96d6bc5f32021-10-11T00:39:19ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-67182021-01-01202110.1155/2021/1544955Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank RepresentationXi-Cheng Lou0Xin Feng1School of Mechanical EngineeringSchool of Mechanical EngineeringA multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.http://dx.doi.org/10.1155/2021/1544955 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xi-Cheng Lou Xin Feng |
spellingShingle |
Xi-Cheng Lou Xin Feng Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation Computational and Mathematical Methods in Medicine |
author_facet |
Xi-Cheng Lou Xin Feng |
author_sort |
Xi-Cheng Lou |
title |
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation |
title_short |
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation |
title_full |
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation |
title_fullStr |
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation |
title_full_unstemmed |
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation |
title_sort |
multimodal medical image fusion based on multiple latent low-rank representation |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-6718 |
publishDate |
2021-01-01 |
description |
A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes. |
url |
http://dx.doi.org/10.1155/2021/1544955 |
work_keys_str_mv |
AT xichenglou multimodalmedicalimagefusionbasedonmultiplelatentlowrankrepresentation AT xinfeng multimodalmedicalimagefusionbasedonmultiplelatentlowrankrepresentation |
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1716829188189585408 |