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Hybrid Adaptive Crayfish Optimization with Differential Evolution for Color Multi-Threshold Image Segmentation.

Honghua Rao1,2, Heming Jia2, Xinyao Zhang3

  • 1School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China.

Biomimetics (Basel, Switzerland)
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved adaptive crayfish optimization algorithm with differential evolution (ACOADE) for color image segmentation. ACOADE enhances convergence and robustness, outperforming other methods in objective function value and image quality metrics.

Keywords:
Kapur entropy methodOtsu methodadaptive foraging quantity adjustment strategycolor multi-threshold image segmentationcrayfish optimization algorithm

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Multi-threshold image segmentation is crucial for image analysis but faces challenges with algorithm convergence.
  • Existing optimization algorithms, like the basic crayfish optimization algorithm, struggle to find optimal solutions in later stages.

Purpose of the Study:

  • To propose a novel hybrid algorithm, adaptive crayfish optimization with differential evolution (ACOADE), for color multi-threshold image segmentation.
  • To enhance the convergence and exploration capabilities of the crayfish optimization algorithm for improved segmentation results.

Main Methods:

  • ACOADE optimizes the foraging parameter 'p' and incorporates an adaptive strategy to boost randomness.
  • The differential evolution (DE) algorithm's core formula is integrated to balance exploration and exploitation.
  • Performance validated using IEEE CEC2020 test functions and compared against eight other algorithms.
  • Effectiveness for image segmentation assessed using Kapur entropy and Otsu methods, with comparisons to eight other algorithms.

Main Results:

  • ACOADE demonstrated superior performance in objective function values compared to benchmark algorithms.
  • Image segmentation quality metrics, including Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index Measure (FSIM), and Structural Similarity Index Measure (SSIM), showed significant improvements.
  • The Wilcoxon signed-rank test confirmed ACOADE's statistical superiority.

Conclusions:

  • ACOADE offers significant advantages in objective function value, image quality, convergence speed, and robustness for multi-threshold image segmentation.
  • The hybrid approach effectively addresses the limitations of traditional crayfish optimization algorithms.
  • ACOADE represents a promising advancement for color image segmentation tasks.