Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

• Published in: Computers in biology and medicine Vol. 136; p. 104609
• Main Authors: Liu, Lei, Zhao, Dong, Yu, Fanhua, Heidari, Ali Asghar, Li, Chengye, Ouyang, Jinsheng, Chen, Huiling, Mafarja, Majdi, Turabieh, Hamza, Pan, Jingye
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2021; Elsevier Limited
• Subjects: Adaptability; Algorithms; Ant colony optimization; Convergence; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Diagnosis; Diagnostic systems; Disease transmission; Entropy; Epidemics; Fault diagnosis; Histograms; Humans; Image filters; Image processing; Image Processing, Computer-Assisted; Image segmentation; Intelligence; Internal Medicine; Medical diagnosis; Medical research; Meta-heuristic; Multilevel; Mutation; Optimization algorithms; Other; SARS-CoV-2; Swarm intelligence; X-Rays; COVID-19; Swarm-intelligence; Ant colony optimization; Diagnosis; Meta-heuristic; Image
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104609

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com. •A novel ant colony optimizer (CLACO) is proposed for COVID-19 image segmentation.•The performance of CLACO is verified by comparison with some excellent peers.•An effective COVID-19 image segmentation method based on CLACO is developed.•The segmentation method is evaluated under some representative threshold levels.•CLACO extremely improves convergence rate and image segmentation quality.