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An improved watershed-based algorithm for ore image segmentation.

Weiwei Li1, Wenwen Liu2, Yuelong Li1

  • 1School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China.

Scientific Reports
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an improved watershed algorithm for precise ore particle segmentation. The novel method enhances accuracy and efficiency in automated mineral processing, outperforming existing techniques.

Area of Science:

  • Mineral Processing
  • Image Analysis
  • Computational Geology

Background:

  • Accurate ore particle size analysis is crucial for automated mineral processing.
  • Traditional segmentation methods struggle with accuracy and adaptability in complex mining environments.
  • Existing approaches often fail to balance segmentation precision and computational efficiency.

Purpose of the Study:

  • To develop a novel ore image segmentation method that overcomes the limitations of traditional approaches.
  • To improve the accuracy and efficiency of ore particle size analysis in automated mineral processing.
  • To provide a robust solution for complex mining scenarios.

Main Methods:

  • A novel segmentation method combining an improved distance transform with the watershed algorithm.
Keywords:
Distance transformationImage processingMorphological gradientOre adhesionStructuring element

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  • Image preprocessing followed by optimization of seed regions using a local-maximum strategy.
  • Refinement of watershed segmentation using morphological operations.

    • Main Results:

    • Achieved a segmentation accuracy of 0.932 on seven test images.
    • Demonstrated a low over-segmentation rate of 0.109 and under-segmentation rate of 0.091.
    • Significantly outperformed traditional watershed and UNet methods in experimental tests.

    Conclusions:

    • The proposed method offers superior performance and stability for practical ore particle analysis.
    • This approach effectively addresses the challenges of accuracy and efficiency in ore segmentation.
    • The technique provides a valuable advancement for automated mineral processing systems.