Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Shuhui Hao; Changcheng Huang; Ali Asghar Heidari; Zhangze Xu; Huiling Chen; Eatedal Alabdulkreem; Hela Elmannai; Xianchuan Wang

doi:10.1016/j.bspc.2023.105147

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Biomed Signal Process Control. 2023 Sep:86:105147. doi: 10.1016/j.bspc.2023.105147. Epub 2023 Jun 14.

Authors

Shuhui Hao¹, Changcheng Huang¹, Ali Asghar Heidari¹, Zhangze Xu¹, Huiling Chen¹, Eatedal Alabdulkreem², Hela Elmannai³, Xianchuan Wang⁴

Affiliations

¹ Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China.
² Department of Computer Science, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
³ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁴ Information Technology Center, Wenzhou Medical University, Wenzhou 325035, China.

Abstract

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Rényi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.

Keywords: COVID-19 X-ray images; Fruit fly optimization algorithm; Meta-heuristic algorithm; Multi-threshold image segmentation.