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Related Experiment Video

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation.

Laith Abualigah1,2,3,4,5, Mahmoud Habash6, Essam Said Hanandeh7

  • 1Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113 Jordan.

Journal of Bionic Engineering
|February 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Reptile Search Algorithm-Salp Swarm Algorithm (RSA-SSA) for efficient gray-scale image segmentation. RSA-SSA improves multi-level thresholding by enhancing search space and avoiding local optima for superior results.

Keywords:
BioinspiredImage segmentationMeta-heuristic algorithmMulti-level thresholdingReptile Search AlgorithmSalp Swarm Algorithm

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-level thresholding is crucial for image segmentation.
  • Existing meta-heuristic algorithms face challenges in search space exploration and avoiding local optima.
  • Nature-inspired algorithms offer potential for improving optimization in image processing tasks.

Purpose of the Study:

  • To propose a novel hybrid meta-heuristic optimization algorithm, RSA-SSA, for gray-scale image segmentation.
  • To enhance the search space exploration and convergence properties for optimal multi-level thresholding.
  • To validate the efficacy of RSA-SSA on benchmark images, including COVID-19 datasets.

Main Methods:

  • Developed a hybrid Reptile Search Algorithm (RSA) and Salp Swarm Algorithm (SSA) named RSA-SSA.
  • Implemented RSA-SSA for gray-scale multi-level thresholding using Otsu's variance method.
  • Evaluated performance using fitness function, Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Friedman ranking test.

Main Results:

  • RSA-SSA demonstrated superior performance in finding optimal multi-level thresholds compared to existing methods.
  • The algorithm effectively avoided the problem of searching in the same area, leading to better solutions.
  • Validation on COVID-19 images confirmed the robustness and effectiveness of RSA-SSA.

Conclusions:

  • RSA-SSA is a highly effective nature-inspired optimization algorithm for image segmentation.
  • The proposed method offers significant improvements over other meta-heuristic algorithms in literature.
  • RSA-SSA provides a robust solution for multi-level thresholding in image analysis.