ABSTRACT
The lesions of COVID-19 CT image show various kinds of ground-glass opacity and consolidation, which are distributed in left lung, right lung or both lungs. The lung lobes are uneven and it have similar gray value to the surrounding arteries, veins, and bronchi. The lesions of COVID-19 have different sizes and shapes in different periods. Accurate segmentation of lung parenchyma in CT image is a key step in COVID-19 detection and diagnosis. Aiming at the unideal effect of traditional image segmentation methods on lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of the lung, so that the segmentation threshold can change adaptively according to the detailed information of lung lobes, trachea, bronchi and ground-glass opacity. Then the lung parenchyma is further processed to obtain the lung parenchyma template, and then the defective template is repaired combined with the improved Freeman chain code and Bezier curve. Finally, the lung parenchyma is extracted by multiplying the template with the lung CT image. The accuracy of lung parenchyma segmentation has been improved in the contrast clarity of CT image and the consistency of lung parenchyma regional features, with an average segmentation accuracy rate of 97.4%. The experimental results show that for COVID-19 and suspected cases, the method has an ideal segmentation effect, and it has good accuracy and robustness.
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.
ABSTRACT
AIMS: Flavonoids and related compounds, such as quercetin-based antiviral drug Gene-Eden-VIR/Novirin, inhibit the protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The alkylated chalcones isolated from Angelica keiskei inhibit SARS-CoV proteases. In this study, we aimed to compare the anti-SARS CoV-2 activities of both newly synthesized chalcone derivatives and these two drugs. METHODS: Determination of the potent antiviral activity of newly synthesized chalcone derivatives against SARS-CoV-2 by calculating the RT-PCR cycling threshold (Ct ) values. RESULTS: Antiviral activities of the compounds varied because of being dose dependent. Compound 6, 7, 9, and 16 were highly effective against SARS-CoV-2 at the concentration of 1.60 µg/mL. Structure-based virtual screening was carried out against the most important druggable SARS-CoV-2 targets, viral RNA-dependent RNA polymerase, to identify putative inhibitors that could facilitate the development of potential anti-coronavirus disease-2019 drug candidates. CONCLUSIONS: Computational analyses identified eight compounds inhibiting each target, with binding affinity scores ranging from -4.370 to -2.748 kcal/mol along with their toxicological, ADME, and drug-like properties.