  The pneumonia caused by Coronavirus disease (COVID-19) is one of major global
threat and a number of detection and treatment procedures are suggested by the
researchers for COVID-19. The proposed work aims to suggest an automated image
processing scheme to extract the COVID-19 lesion from the lung CT scan images
(CTI) recorded from the patients. This scheme implements the following
procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii)
Image post-processing to extract the lesions, and (iii) Execution of a relative
analysis between the extracted lesion segment and the Ground-Truth-Image (GTI).
This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based
multi-threshold to enhance the pneumonia lesion and implements
Markov-Random-Field (MRF) segmentation to extract the lesions with better
accuracy. The proposed scheme is tested and validated using a class of COVID-19
CTI obtained from the existing image datasets and the experimental outcome is
appraised to authenticate the clinical significance of the proposed scheme. The
proposed work helped to attain a mean accuracy of >92% during COVID-19 lesion
segmentation and in future, it can be used to examine the real clinical lung
CTI of COVID-19 patients.
