Novel Square Error Minimization-Based Multilevel Thresholding Method for COVID-19 X-Ray Image Analysis Using Fast Cuckoo Search

TL;DR: 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, and the SE minimization-based optimal multilevel thresholding method using the FCS, coined as SE-FCS, is proposed.

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.