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A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis.

Yating Wang1, Hongjun Li1

  • 1School of Information Science and Technology, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
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This study introduces a novel grey relational analysis method for accurate retinal vessel segmentation, addressing limited data challenges. The approach significantly improves diagnostic capabilities for eye diseases.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate retinal vessel segmentation is crucial for diagnosing and treating various eye diseases.
  • Limited annotated datasets pose a significant challenge for developing robust segmentation models.
  • Grey theory offers a promising approach for handling problems with scarce data and poor information.

Purpose of the Study:

  • To propose a novel grey relational-based method for accurate retinal vessel segmentation.
  • To address the limitations of small and unlabeled datasets in medical image analysis.
  • To enhance computer-aided diagnosis systems for retinal diseases.

Main Methods:

  • Developed a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) for image enhancement.
Keywords:
adaptive discriminative filteringgrey relational analysisretinal vessel segmentationunsupervised algorithm

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  • Designed a threshold segmentation model based on grey relational analysis (TS-GRA) for segmenting enhanced vessel images.
  • Implemented a post-processing stage including hole filling and isolated pixel removal for refined segmentation.

    • Main Results:

    • The proposed method demonstrated high performance on public datasets (DRIVE, STARE, HRF).
    • Achieved an average accuracy of 96.03% and specificity of 98.51% on the DRIVE dataset.
    • Showcased superior performance compared to current mainstream retinal vessel segmentation methods.

    Conclusions:

    • The novel grey relational-based method offers an effective solution for retinal vessel segmentation, especially with limited data.
    • The method provides accurate and robust segmentation results, outperforming existing techniques.
    • This approach has the potential to significantly advance computer-aided diagnosis in ophthalmology.