During the automatic reconstruction of panoramic images, the effect of dental arch curve fitting will affect the integrity of the content of the panoramic image. Metal implants in the patient’s mouth usually lead to a decrease in the contrast of the panoramic image, which affects the doctor’s diagnosis. In this paper, an automatic oral panoramic image reconstruction method was proposed. By calculating key image areas and image extraction fusion algorithms, the dental arch curve could be automatically detected and adjusted on a small number of images, and the intensity distribution of teeth, bone tissue and metal implants on the image could be adjusted to reduce the impact of metal on other tissues, to generate high-quality panoramic images. The method was tested on 50 cases of cone beam computed tomography (CBCT) data with good results, which can effectively improve the quality of panoramic images.
Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, QAB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.