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Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

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A Correction to this article was published on 02 March 2024

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Abstract

The traditional threshold methods used for image segmentation are effective for bi-level thresholds. In the case of complex images that contain many objects or color images, the computational complexity is significantly elevated. Multi-level threshold methods for the segmentation of color images can be seen as a complicated optimization problem. In this paper, an improved version of the Arithmetic Optimization Algorithm, called AOAa, is proposed based on the efficient search operators of Aquila Optimizer to obtain optimal threshold values in various levels of color and gray images. Otsu and Kapur’s entropy methods are used in this study as objective functions. Experiments were conducted on 16 benchmark images; COVID-19, color, and gray. The results are analyzed regarding the fitness function, peak signal-to-noise ratio (PSNR), and structural index similarity (SSIM). The obtained results showed that the proposed method got better results than several other well-established methods.

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Acknowledgements

The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Program number (RSPD2024R704), King Saud University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  2. Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq

    Nada Khalil Al-Okbi

  3. Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800, 11421, Riyadh, Saudi Arabia

    Emad Mahrous Awwad

  4. Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, 11421, Riyadh, Saudi Arabia

    Mohamed Sharaf

  5. College of Engineering, Al Ain University, 112612, Abu Dhabi, United Arab Emirates

    Mohammad Sh. Daoud

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  7. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  8. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  9. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

  10. Applied science research center, Applied science private university, Amman, 11931, Jordan

    Laith Abualigah

  11. Faculty of Information Technology, Isra University, Amman, 11622, Jordan

    Laith Abualigah

  12. College of Engineering, Yuan Ze University, Taoyuan, Taiwan

    Laith Abualigah

  13. New Engineering Industry College, Putian University, Putian, 351100, China

    Nada Khalil Al-Okbi

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  1. Laith Abualigah
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Correspondence to Laith Abualigah.

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Abualigah, L., Al-Okbi, N.K., Awwad, E.M. et al. Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation. Evolving Systems (2024). https://doi.org/10.1007/s12530-023-09566-1

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