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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Multi-Mechanism Artificial Lemming Algorithm for Global Optimization and Color Multi-Threshold Image Segmentation.

Liang Tao1,2, Lingzhi Li1,2, Fan Lu1

  • 1School of Future Science and Engineering, Soochow University, Suzhou 215222, China.

Biomimetics (Basel, Switzerland)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

A new Multi-Mechanism Artificial Lemming Algorithm (MALA) improves color image segmentation. MALA enhances the exploration-exploitation balance, leading to more accurate threshold selection and better segmentation results compared to the original algorithm.

Keywords:
CEC2017artificial lemming algorithmglobal optimizationmulti-threshold image segmentationswarm intelligence

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Color multi-threshold image segmentation is a complex, non-convex optimization problem.
  • Increasing thresholds expands the search space, raising computational demands.
  • Existing algorithms like ALA face challenges with exploration-exploitation balance and boundary handling.

Purpose of the Study:

  • To introduce a novel Multi-Mechanism Artificial Lemming Algorithm (MALA).
  • To enhance the stability of exploration-exploitation and boundary handling in optimization.
  • To improve the performance of color multi-threshold image segmentation.

Main Methods:

  • Developed MALA by integrating three enhancement mechanisms into the Artificial Lemming Algorithm (ALA).
  • Evaluated MALA's general optimization capability on the CEC2017 benchmark suite.
  • Applied MALA to color multi-threshold image segmentation using Otsu's criterion.

Main Results:

  • MALA demonstrated competitive convergence and improved objective values against ALA and other algorithms on the CEC2017 benchmark.
  • Segmentation experiments showed MALA achieved competitive fitness values.
  • MALA yielded higher Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) metrics.

Conclusions:

  • MALA offers a stable and effective approach to color multi-threshold image segmentation.
  • The integrated mechanisms enhance population-level guidance and boundary handling.
  • MALA shows potential as a general optimization method applicable to image segmentation tasks.