Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (3): 1198-1262.doi: 10.1007/s42235-022-00295-w
Multi‑verse Optimizer with Rosenbrock and Difusion Mechanisms for Multilevel Threshold Image Segmentation from COVID‑19 Chest X‑Ray Images
Yan Han1; Weibin Chen1; Ali Asghar Heidari2; Huiling Chen1
- 1 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China 2 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Multi‑verse Optimizer with Rosenbrock and Difusion Mechanisms for Multilevel Threshold Image Segmentation from COVID‑19 Chest X‑Ray Images
Yan Han1; Weibin Chen1; Ali Asghar Heidari2; Huiling Chen1
- 1 Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China 2 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
摘要： Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great signifcance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and difusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.