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.