Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN

In order to better preserve the anatomical structure information of Computed Tomography (CT) source images and highlight the metabolic information of lesion regions in Positron Emission Tomography (PET) source images, a hybrid multimodal medical image fusion method (LatLRR-GAN) based on Latent low-r...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tao Zhou [verfasserIn] Qi Li [verfasserIn] Huiling Lu [verfasserIn] Xiangxiang Zhang [verfasserIn] Qianru Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	multimodal medical image fusion LatLRR GAN deep learning

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 24, p 12758
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:24, p 12758

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app122412758

Katalog-ID:	DOAJ083239642

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callnumber-first	T - Technology
author	Tao Zhou
spellingShingle	Tao Zhou misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc multimodal misc medical image fusion misc LatLRR misc GAN misc deep learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN multimodal medical image fusion LatLRR GAN deep learning
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc multimodal misc medical image fusion misc LatLRR misc GAN misc deep learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN
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title_sort	hybrid multimodal medical image fusion method based on latlrr and ed-d<sup<2</sup<gan
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title_auth	Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN
abstract	In order to better preserve the anatomical structure information of Computed Tomography (CT) source images and highlight the metabolic information of lesion regions in Positron Emission Tomography (PET) source images, a hybrid multimodal medical image fusion method (LatLRR-GAN) based on Latent low-rank representation (LatLRR) and the dual discriminators Generative Adversarial Network (ED-D<sup<2</sup<GAN) is proposed. Firstly, considering the denoising capability of LatLRR, source images were decomposed by LatLRR. Secondly, the ED-D<sup<2</sup<GAN model was put forward as the low-rank region fusion method, which can fully extract the information contained by the low-rank region images. Among them, encoder and decoder networks were used in the generator; convolutional neural networks were also used in dual discriminators. Thirdly, a threshold adaptive weighting algorithm based on the region energy ratio is proposed as the salient region fusion rule, which can improve the overall sharpness of the fused image. The experimental results show that compared with the best methods of the other six methods, this paper is effective in multiple objective evaluation metrics, including the average gradient, edge intensity, information entropy, spatial frequency and standard deviation. The results of the two experiments are improved by 35.03%, 42.42%, 4.66%, 8.59% and 11.49% on average.
abstractGer	In order to better preserve the anatomical structure information of Computed Tomography (CT) source images and highlight the metabolic information of lesion regions in Positron Emission Tomography (PET) source images, a hybrid multimodal medical image fusion method (LatLRR-GAN) based on Latent low-rank representation (LatLRR) and the dual discriminators Generative Adversarial Network (ED-D<sup<2</sup<GAN) is proposed. Firstly, considering the denoising capability of LatLRR, source images were decomposed by LatLRR. Secondly, the ED-D<sup<2</sup<GAN model was put forward as the low-rank region fusion method, which can fully extract the information contained by the low-rank region images. Among them, encoder and decoder networks were used in the generator; convolutional neural networks were also used in dual discriminators. Thirdly, a threshold adaptive weighting algorithm based on the region energy ratio is proposed as the salient region fusion rule, which can improve the overall sharpness of the fused image. The experimental results show that compared with the best methods of the other six methods, this paper is effective in multiple objective evaluation metrics, including the average gradient, edge intensity, information entropy, spatial frequency and standard deviation. The results of the two experiments are improved by 35.03%, 42.42%, 4.66%, 8.59% and 11.49% on average.
abstract_unstemmed	In order to better preserve the anatomical structure information of Computed Tomography (CT) source images and highlight the metabolic information of lesion regions in Positron Emission Tomography (PET) source images, a hybrid multimodal medical image fusion method (LatLRR-GAN) based on Latent low-rank representation (LatLRR) and the dual discriminators Generative Adversarial Network (ED-D<sup<2</sup<GAN) is proposed. Firstly, considering the denoising capability of LatLRR, source images were decomposed by LatLRR. Secondly, the ED-D<sup<2</sup<GAN model was put forward as the low-rank region fusion method, which can fully extract the information contained by the low-rank region images. Among them, encoder and decoder networks were used in the generator; convolutional neural networks were also used in dual discriminators. Thirdly, a threshold adaptive weighting algorithm based on the region energy ratio is proposed as the salient region fusion rule, which can improve the overall sharpness of the fused image. The experimental results show that compared with the best methods of the other six methods, this paper is effective in multiple objective evaluation metrics, including the average gradient, edge intensity, information entropy, spatial frequency and standard deviation. The results of the two experiments are improved by 35.03%, 42.42%, 4.66%, 8.59% and 11.49% on average.
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title_short	Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN
url	https://doi.org/10.3390/app122412758 https://doaj.org/article/efa6368722924bf28e77627d8cf00776 https://www.mdpi.com/2076-3417/12/24/12758 https://doaj.org/toc/2076-3417
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Firstly, considering the denoising capability of LatLRR, source images were decomposed by LatLRR. Secondly, the ED-D<sup<2</sup<GAN model was put forward as the low-rank region fusion method, which can fully extract the information contained by the low-rank region images. Among them, encoder and decoder networks were used in the generator; convolutional neural networks were also used in dual discriminators. Thirdly, a threshold adaptive weighting algorithm based on the region energy ratio is proposed as the salient region fusion rule, which can improve the overall sharpness of the fused image. The experimental results show that compared with the best methods of the other six methods, this paper is effective in multiple objective evaluation metrics, including the average gradient, edge intensity, information entropy, spatial frequency and standard deviation. 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Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D<sup<2</sup<GAN

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