Diagnosis of Bronchial and Pulmonary Fungal Infection Using Gradient Weighted Denoising Algorithm-Based CT Images

Abstract

Based on computed tomography (CT) with a gradient weighted denoising algorithm, the image denoising technique was applied to diagnose bronchial and pulmonary fungal infection to discuss the features of CT images and the efficiency of the denoising algorithm. Therefore, it could assist clinicians in disease treatment. The clinical data and imaging data of 100 patients with invasive pulmonary fungal infection were collected in the hospital. All of them were rolled into a natural denoising CT group (routine group) and gradient weighted denoising algorithm-based image denoising group (algorithm group). The images from the routine group were processed by the routine natural denoising method, and the images from the algorithm group were denoised with the gradient weighted denoising algorithm. The results showed that the algorithm group had greater denoising efficiency and less denoising time compared with the routine group ( $P<0.05$ ). The diagnostic sensitivity, specificity, and accuracy of the denoised images from the algorithm group were higher markedly than the above three indicators of the routine group ( $P<0.05$ ). For bronchopulmonary infections, the sensitivity, specificity, and accuracy of the PDE model for CT denoised images were 99.00%, 96.87%, and 98.33%, the positive rate of chest CT examination was 86.2%, which was higher markedly than the rate of ordinary CT examination (70.5%), and the difference was statistically substantial ( $P<0.05$ ). Besides, the mean absolute error (MAE), peak signal to noise ratio (PSNR), and mean structural similarity index measure (MSSIM) of the algorithm group were better than those of the unprocessed images and the routine group ( $P<0.05$ ). Moreover, the algorithm group had a good visual effect. In conclusion, the gradient weighted denoising algorithm could effectively remove the noise and bar artifacts in CT images and well retain the edge details of CT images, thereby improving the quality of CT images. Therefore, it was suitable for clinical diagnosis and had practical application value.