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Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level

Laith Abualigah1, Nada Khalil Al-Okbi2, Saleh Ali Alomari3

  • 1Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan. aligah.2020@gmail.com.

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|April 13, 2025
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Summary
This summary is machine-generated.

This study introduces an enhanced Reptile Search Algorithm (RSA) for multi-level image thresholding. The improved algorithm effectively optimizes threshold values for complex grayscale and color images, outperforming existing methods.

Keywords:
Image segmentationMedical imagesMulti-level thresholdOtsu method, Kapur methodReptile search algorithm

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Area of Science:

  • Computer Vision
  • Image Processing
  • Optimization Algorithms

Background:

  • Bi-level thresholding struggles with complex images containing multiple objects or colors.
  • Multi-level thresholding is essential for complex image segmentation but poses optimization challenges.

Purpose of the Study:

  • To present an improved Reptile Search Algorithm (RSA) with a Gbest operator for enhanced multi-level image thresholding performance.
  • To determine optimal threshold values for both grayscale and color images using entropy-based objective functions.

Main Methods:

  • The proposed method utilizes an enhanced Reptile Search Algorithm (RSA) incorporating a Gbest operator.
  • Entropy-based objective functions derived from Otsu and Kapur techniques are employed for optimization.
  • Experiments were conducted on 16 benchmark images, including medical (COVID-19) and standard color/grayscale datasets.

Main Results:

  • The enhanced RSA demonstrated superior performance in multi-level thresholding tasks.
  • Quantitative evaluation using fitness function, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Friedman ranking test confirmed effectiveness.
  • The algorithm proved robust across diverse image types.

Conclusions:

  • The proposed enhanced Reptile Search Algorithm offers a robust and effective solution for multi-level image thresholding.
  • This method significantly improves segmentation accuracy for complex images compared to existing state-of-the-art techniques.