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Image Segmentation Based on the Optimized K-Means Algorithm with the Improved Hybrid Grey Wolf Optimization:

Xinyi Chai1, Zijun Wu2, Wei Li2

  • 1School of Automation, Jiangsu University of Science and Technology, No. 666 Changxiang Road, Zhenjiang 212100, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary

This study introduces an improved K-means algorithm (IGK-means) for faster and more accurate ore particle image segmentation. The novel method enhances detection accuracy, crucial for mineral processing and crushing efficiency.

Keywords:
K-meansimage segmentationimproved GWOore particle size detection

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Area of Science:

  • Mineral Processing
  • Image Analysis
  • Computational Intelligence

Background:

  • Accurate ore particle size detection is vital for mineral processing efficiency.
  • Current image segmentation methods for ore particles suffer from low quality and efficiency.
  • Effective segmentation impacts ore density calculation, beneficiation, and crushing evaluation.

Purpose of the Study:

  • To develop a novel, fast, and accurate image segmentation algorithm for ore particles.
  • To address the limitations of traditional K-means in ore particle image analysis.
  • To improve the reliability of particle size detection in mineral processing.

Main Methods:

  • A hybridized K-means algorithm (IGK-means) is proposed.
  • The Improved Grey Wolf Optimizer-Slime Mould Algorithm (IGWO_SOA) optimizes initial cluster centers.
  • IGWO_SOA incorporates nonlinear convergence factors and migration/spiral search mechanisms.

Main Results:

  • The IGK-means algorithm demonstrated superior image segmentation quality compared to traditional methods.
  • The proposed method is insensitive to variations in illumination.
  • Achieved Peak Signal-to-Noise Ratio (PSNR) up to 24.24 dB and Feature Similarity Index Measure (FSIM) up to 0.2733.

Conclusions:

  • The novel IGK-means algorithm offers a practical and effective solution for ore particle image segmentation.
  • This advancement can significantly improve the accuracy of ore particle size detection.
  • The algorithm's robustness to illumination enhances its applicability in industrial settings.