ABSTRACT
Recently, image thresholding methods based on various entropy functions have been found popularity. Nonetheless, entropic-based methods depend on the spatial distribution of the grey level values in an image. Hence, the accuracy of these methods is limited due to the non-uniform distribution of the grey values. Further, the analysis of the COVID-19 X-ray images is evolved as an important area of research. Therefore, it is needed to develop an efficient method for the segmentation of the COVID-19 X-ray images. To address these issues, an efficient non-entropy-based thresholding method is suggested. A novel fitness function in terms of the segmentation score (SS) is introduced, which is used to reduce the segmentation error. A soft computing approach is suggested. An efficient optimizer using the chance-based birds' intelligence is introduced to maximize the fitness values. The new optimizer is validated utilizing the benchmark test functions. The statistical parameters reveal that the suggested optimizer is efficient. It shows a quite significant improvement over its counterparts-optimization based on seagull/cuckoo birds. Precisely, the paper includes three novel contributions-(i) fitness function, (ii) chance-based birds' intelligence for optimization, (iii) multiclass segmentation. The COVID-19 X-ray images are taken from the Kaggle Radiography database, to the experiment. Its results are compared with three different state-of-the-art entropy-based techniques-Tsallis, Kapur's, and Masi. For providing a statistical analysis, Friedman's mean rank test is conducted and our method Ranked one. Its superiority is claimed in terms of Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM) and Structure Similarity Index (SSIM). On the whole, an improvement of about 11% in PSNR values is achieved using the proposed method. This method would be helpful for medical image analysis.
ABSTRACT
Background: Antivirals and immunosuppressive agents are used with variable success in the treatment of COVID-19. Mycophenolate, an inhibitor of enzyme inosine monophosphate dehydrogenase, is an immunosuppressant used to prevent allograft rejection and other autoimmune diseases. Few laboratory studies have also reported antiviral properties of mycophenolate. The current study tried to assess the safety and efficacy of mycophenolate in patients hospitalised with COVID-19. Methods: This was a prospective non-randomised open label study with the objective to assess the effect of addition of mycophenolate to the standard of care on mortality due to COVID-19 and duration of hospital stay. The target study population was comprised of patients requiring inpatient treatment for COVID-19 during the period from Jan 15-April 15, 2021. The study was registered with Clinical Trial Registry of India (CTRI/2021/01/030477, registered on date-14/01/2021). Adult patients (n = 106) requiring hospitalisation for COVID-19 received mycophenolate, 360 mg, one tablet daily for one month. Mycophenolate was initiated within 48 h of the diagnosis of SARS-CoV-2 infection by RTâPCR. While patients who did not consent for mycophenolate (n = 106), received only standard of care, and were considered as control group. The relevant clinical data including NEWS2 scores and high-resolution computed tomography of the thorax were collected and analysed. Findings: The mortality and hospital stay were significantly lower in the study group compared to the control group. Mycophenolate significantly reduced mortality after adjustment for other predictors (adjusted odds ratio: 0.082 with 95% CI: 0.012-0.567). Mycophenolate was an independent predictor of survival in patients hospitalised due to COVID-19. There was also no evidence of secondary bacterial infections and post-COVID complications. Interpretation: Mycophenolate administration is safe in COVID-19. Mycophenolate reduces mortality and duration of hospital stay in patients with COVID-19. Funding: Shri Janai Research Foundation, India.
ABSTRACT
Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ray images. Most of the threshold selection techniques are entropy-based. Nevertheless, these techniques suffer from their dependencies on the spatial distribution of grey values. To tackle these issues, a novel non-entropic threshold selection method is proposed, which is the primary key contribution having found a new source of information to the biomedical image processing field. The firsthand Square Error (SE)-based objective function is suggested. The second key contribution is the new optimizer called Fast Cuckoo Search (FCS), which is useful and brings novel ideas into the subject, used to optimize the suggested objective functions for computing the optimal thresholds. To ensure a faster convergence with a quality optimal solution, we include extra exploitation together with a chance factor. The FCS is validated using the well-known classical and CEC 2014 benchmark test functions, which shows a significant improvement over its predecessors—Adaptive Cuckoo Search (ACS) and other state-of-the-art optimizers. Further, the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed. To experiment, images are considered from the Kaggle Radiography database. We have compared its performances with Tsallis, Kapur’s, and Masi entropy-based techniques using well-known segmentation metrics and achieved a performance increase of 2.95%, 5.51% and 10.50%, respectively. The proposed method shows superiority using Friedman’s mean rank statistical test and ranked first. [ FROM AUTHOR] Copyright of International Journal of Image & Graphics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)