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Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images

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Abstract

Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the Arithmetic Optimization Algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.

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Data is available from the authors upon reasonable request.

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Authors and Affiliations

  1. Khawarizmi University Technical College, Amman, 11953, Jordan

    Mohammad Otair & Saif Tawfiq

  2. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

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

    Laith Abualigah

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

    Laith Abualigah

  5. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

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

    Laith Abualigah

  7. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah & Putra Sumari

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

    Laith Abualigah

  9. Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan

    Mohammad Alshinwan

  10. Unit for Data Science and Computing, North-West University, 11 Hoffman Street, Potchefstroom, 2520, South Africa

    Absalom E. Ezugwu

  11. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, 38044, Abu Dhabi, United Arab Emirates

    Raed Abu Zitar

Authors
  1. Mohammad Otair
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  2. Laith Abualigah
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  4. Mohammad Alshinwan
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  5. Absalom E. Ezugwu
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  6. Raed Abu Zitar
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  7. Putra Sumari
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Corresponding author

Correspondence to Laith Abualigah.

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Otair, M., Abualigah, L., Tawfiq, S. et al. Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images. Multimed Tools Appl 83, 41051–41081 (2024). https://doi.org/10.1007/s11042-023-17221-9

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  • DOI: https://doi.org/10.1007/s11042-023-17221-9

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