Lung tumor image fusion based on VGG19-GAN

Xuewen Zhang; Jingjing Zong; Yusen Yang; Xinmeng Sun

doi:10.1117/12.3044199

27 August 2024 Lung tumor image fusion based on VGG19-GAN

Xuewen Zhang, Jingjing Zong, Yusen Yang, Xinmeng Sun

Proceedings Volume 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024); 132520J (2024) https://doi.org/10.1117/12.3044199
Event: 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 2024, Kaifeng, China

Abstract

Positron emission tomography(PET) and computed tomography(CT) imaging can obtain the functional metabolic information and anatomical information of the human body, respectively. In response to the problem of how to merge the complementary information of PET and CT, a lung tumor image fusion method based on VGG19 and GAN model is proposed in this paper. The basic idea is to extract the content of the PET image and the style of the CT image separately, and then merge these two to generate the fusion image. The style image is represented using a series of statistical methods that capture features such as color, texture, and style. The content image is represented using a series of deep learning techniques that capture features such as shapes, structures, and semantic information. The experimental results show that the proposed method has better subjective and objective results than the compared algorithms, which can generate higher-quality PET/CT fusion images. This method provides comprehensive diagnostic information, which is helpful for the diagnosis of lung tumors in clinical treatment and reduces misdiagnosis and underdiagnoses.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuewen Zhang, Jingjing Zong, Yusen Yang, and Xinmeng Sun "Lung tumor image fusion based on VGG19-GAN", Proc. SPIE 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 132520J (27 August 2024); https://doi.org/10.1117/12.3044199

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KEYWORDS

Image fusion

Positron emission tomography

Computed tomography

Tumors

Medical imaging

Lung

Image quality

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