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Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Author

Listed:
  • Ahmed A. Ewees

    (Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia
    Department of Computer, Damietta University, Damietta 34511, Egypt)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia)

  • 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
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    School of Computer Science and Robotics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

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.

Suggested Citation

  • Ahmed A. Ewees & Laith Abualigah & Dalia Yousri & Ahmed T. Sahlol & Mohammed A. A. Al-qaness & Samah Alshathri & Mohamed Abd Elaziz, 2021. "Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 9(19), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2363-:d:641673
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    Citations

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    Cited by:

    1. Dejan G. Ćirić & Zoran H. Perić & Nikola J. Vučić & Miljan P. Miletić, 2023. "Analysis of Industrial Product Sound by Applying Image Similarity Measures," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    2. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 10(7), pages 1-42, March.
    3. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah & Yuxiang Liu, 2022. "A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 10(9), pages 1-30, May.

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