An efficient multi-thresholding based COVID-19 CT images segmentation approach using an improved equilibrium optimizer. (March 2022)

• Record Type: Journal Article
• Authors: Houssein, Essam H.; Helmy, Bahaa El-din; Oliva, Diego; Jangir, Pradeep; Premkumar, M.; Elngar, Ahmed A.; Shaban, Hassan
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: Metaheuristics -- Equilibrium Optimizer (EO) -- Dimension learning hunting (DLH) -- Multi-level thresholding -- Image segmentation -- COVID-19 CT images; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103401
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Adapting EO based on Dimension Learning Hunting (DLH) called I-EO is proposed. COVID-19 CT images are accurately segmented by using I-EO. I-EO is applied to solve the multi-threshold image segmentation problem. The performance of the I-EO is compared with several well-known meta-heuristics. The qualitative and quantitative results revealed the robustness of the I-EO. Abstract: Optimization is the process of searching for the optimal (best-so-far) solution among a wide range of solutions. Besides, in the last two decades, a family of algorithms known as metaheuristic algorithms (MHs) has been widely used. MHs have attracted researchers' interest due to their efficiency, easy implementation, and understanding. The equilibrium optimizer (EO) is a recent MH that has been used to tackle several real world problems. Despite the robustness of the EO algorithm, it suffers of the unbalance between the exploration and exploitation phases, this situation causes that the search process be trapped in local optimal values. In this study, an improved version of the EO that combines the standard operators with the dimension learning hunting (DLH) is introduced. The proposed method called I-EO is tested over the CEC'2020 benchmark functions. Quantitative and qualitative results confirmed the robustness and superiority of the proposed algorithm compared to a set of well-known optimization methods. Besides, I-EO is proposed to tackle a real-world application; the multi-level thresholding segmentation for a set of CT images of COVID-19 by maximizing the fuzzy entropy. The segmentation results show the excellent performance in all experiments and confirmed that the proposed I-EO could be an efficient tool for image segmentation. The different elements of the CT are properly segmented by the I-EO based approach. Moreover, the statistical analysis, quality metrics, comparisons and non-parametric tests validates the performance of the I-EO to segment CT images of COVID-19.