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EATHOA: Elite-evolved hiking algorithm for global optimization and precise multi-thresholding image segmentation in

Mahmoud Abdel-Salam1, Essam H Houssein2, Marwa M Emam3

  • 1Faculty of Computer and Information Science, Mansoura University, Mansoura, 35516, Egypt.

Computers in Biology and Medicine
|August 7, 2025
PubMed
Summary

A new algorithm, Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), effectively segments brain hemorrhages in medical images. This method improves accuracy and efficiency for intracerebral hemorrhage diagnosis.

Keywords:
Elite algorithmGlobal optimizationHiking algorithmIntracerebral hemorrhage segmentationMulti-threshold image segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Optimization

Background:

  • Intracerebral hemorrhage (ICH) is a critical condition requiring accurate and timely diagnosis.
  • Medical image segmentation is vital for ICH diagnosis, but current methods struggle with complex images and multi-threshold segmentation (MTIS).
  • Existing techniques face computational challenges and performance degradation with increasing thresholds in ICH image segmentation.

Purpose of the Study:

  • To develop an advanced optimization algorithm for high-dimensional and multimodal problems, specifically for ICH image segmentation.
  • To enhance the accuracy and computational efficiency of multi-threshold image segmentation (MTIS) for intracerebral hemorrhage (ICH).
  • To introduce the Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA) for improved medical image analysis.

Main Methods:

  • Proposed the Elite-Adaptive-Turbulent Hiking Optimization Algorithm (EATHOA), an enhancement of the Hiking Optimization Algorithm (HOA).
  • Integrated Elite Opposition-Based Learning (EOBL), Adaptive k-Average-Best Mutation (AKAB), and a Turbulent Operator (TO) into EATHOA.
  • Evaluated EATHOA on CEC2017 and CEC2022 benchmark functions and applied it to multi-threshold image segmentation (MTIS) of intracerebral hemorrhage (ICH) images.

Main Results:

  • EATHOA demonstrated superior global optimization performance compared to state-of-the-art algorithms on benchmark functions.
  • Applied to ICH image segmentation, EATHOA achieved high PSNR (34.4671), FSIM (0.9710), and SSIM (0.8816) at six threshold levels.
  • The algorithm outperformed recent methods in segmentation accuracy and computational efficiency for ICH images.

Conclusions:

  • EATHOA offers a computationally efficient and effective solution for the complex challenges of intracerebral hemorrhage (ICH) image segmentation.
  • The proposed algorithm shows significant potential as a powerful tool for advanced medical image analysis.
  • EATHOA provides superior performance in accuracy and efficiency for multi-threshold image segmentation (MTIS) tasks in medical imaging.