Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Ahmed A. Ewees, Laith Abualigah, Dalia Yousri, Ahmed T. Sahlol, Mohammed A. A. Al-qaness, Samah Alshathri and Mohamed Abd Elaziz
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Ahmed A. Ewees: Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
Dalia Yousri: Electrical Engineering Department, Faculty of Engineering, Fayoum University, Faiyum 63514, Egypt
Ahmed T. Sahlol: Department of Computer, Damietta University, Damietta 34511, Egypt
Mohammed A. A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Samah Alshathri: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2021, vol. 9, issue 19, 1-25

Abstract: Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.

Keywords: image segmentation; multilevel thresholding; artificial ecosystem-based optimization (AEO); differential evolution (DE); optimization algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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