Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO. (July 2022) - Wang, Guowei; Guo, Shuli; Han, Lina; Cekderi, Anil Baris; Song, Xiaowei; Zhao, Zhilei
Highlights: In section 2, an improved wavelet threshold based on the shrinkage factor is proposed. In this method, the threshold decreases with the increase of decomposition scale. It improves the accuracy of noise detection to a greater extent for asymptomatic COVID-19. In section 2, we develop the wavelet threshold function based on the adjustment factor integrated with the arc tangent, which overcomes the discontinuity and the constant deviation of the traditional threshold function. It is suitable for noisy signals with different variance. In section 3, a wavelet transform based on the optimization of parameters combined with improved PSO is proposed, so that the wavelet parameters can change adaptively according to the details of lung lobes and ground-glass shadow with relatively few iterations. In section 4, aiming at the different kinds of asymptomatic COVID-19 CT images, the simulation experiments prove that the paper method has strong robustness to Gaussian noise, which enhances the ability of image denoising while better protecting the details of the lesion. It reduces the rate of missed and mistake diagnosis for asymptomatic COVID-1. Abstract: The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best.