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Application of an improved watershed algorithm based on distance map reconstruction in bean image segmentation.

Hongquan Liu1, Weijin Zhang2, Fushun Wang2,3

  • 1College of Urban and Rural Construction, Hebei Agricultural University, Baoding, 071000, China.

Heliyon
|May 2, 2023
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Summary

A new DMR-watershed algorithm significantly improves seed image segmentation accuracy. This method enhances object counting and phenotype analysis in seed testing, especially for challenging adherent seeds.

Keywords:
Bean imageImage segmentationSeed phenotypeWatershed algorithm

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Image Processing

Background:

  • Image segmentation is crucial for seed testing, providing data on object counts, area, and contours.
  • Traditional watershed algorithms struggle with uneven grey levels, leading to oversegmentation and undersegmentation, impacting seed phenotype accuracy.

Purpose of the Study:

  • To propose an improved watershed algorithm (DMR-watershed) for accurate seed image segmentation.
  • To address the limitations of traditional algorithms in handling adherent seeds and uneven image quality.

Main Methods:

  • Developed the DMR-watershed algorithm using distance map reconstruction and grey distribution analysis.
  • Selected grey reduction amplitude (h) to create a mask image mirroring the original's grey trend.
  • Reconstructed greyscale maps with adaptive thresholds to eliminate false minima and generate accurate distance maps.

Main Results:

  • The DMR-watershed algorithm achieved a 0% residual rate and 100% counting accuracy for two-particle and multiparticle adhesion cases.
  • Outperformed traditional watershed, edge detection, and concave point analysis algorithms in segmenting adherent adzuki bean seeds.
  • Concave point analysis was unsuitable for multiparticle adhesion, destroying seed images.

Conclusions:

  • The DMR-watershed algorithm significantly enhances the accuracy of image segmentation for adherent seeds.
  • This improved method offers a valuable new reference for image processing in seed testing research.
  • The algorithm effectively overcomes oversegmentation and undersegmentation issues common in seed analysis.