Lung tumor image fusion based on VGG19-GAN

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.