An improved Symmetric Chaotic War strategy optimization algorithm for efficient Scanning electron microscopy image segmentation: Calcium oxide catalyst case

Abstract

The Scanning Electron Microscopy (SEM) device is a useful tool that enables scientists to obtain microscopic images that aid in noticing the changes happening on the surface of the catalyst. Manual inspection of these images by eye. However, might not be adequate to obtain a better understanding. The implementation of the segmentation method is a crucial step in streamlining image representation and resolving the issues associated with image analysis. A new Symmetric Chaotic War Strategy Optimization Algorithm (SCWSO) has been developed to accomplish accurate calcium oxide catalyst SEM image segmentation. It combines symmetric chaotic numbers of Hanon map in exploration and exploitation instead of randomness in order to achieve the best solutions quickly.The developed method has been put to the test using a variety of tests, where different variances were applied to WSO and evaluated, in addition SCWSO was applied to Images affected with noise degradation techniques (Salt and pepper effect) and was evaluated, Next, performance metrics, including fitness, peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error were applied and used, and tested with various CaO catalyst SEM images in order to analyze its efficacy in CaO SEM image segmentation. The tests outcomes showed that the recommended method outperformed competing algorithms and successfully segmented CaO SEM images, allowing for easier identification of the catalyst images.