CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images

• Published in: Computers in biology and medicine Vol. 169; p. 107838
• Main Authors: Li, Yupeng, Zhao, Dong, Ma, Chao, Escorcia-Gutierrez, José, Aljehane, Nojood O., Ye, Xia
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2024; Elsevier Limited
• Subjects: Adaptability; Algorithms; Basic converters; Benchmark; Convergence; Coronaviruses; COVID-19; COVID-19 Testing; Dispersion; Efficiency; Entropy; Experiments; Foraging behavior; Global optimization; Humans; Hunting; Image enhancement; Image processing; Image segmentation; Intelligence; Internal Medicine; Medical diagnosis; Methods; Optimization algorithms; Other; Population-based method; RIME; Searching; Signal to noise ratio; Swarm intelligence; X-Rays; COVID-19; Benchmark; Image segmentation; RIME; Medical diagnosis; Population-based method
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107838

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis. •An improved Rime optimization algorithm is proposed, called CDRIME.•A CDRIME-driven multi-threshold segmentation method is proposed, called CDRIME-MTIS.•CDRIME has excellent exploration, exploitation, and convergence capabilities on IEEE CEC2017.•CDRIME-MTIS is proven to have great potential for improving the diagnosis of COVID-19.•CDRIME successfully optimizes the search defects and segmentation capabilities of RIME.