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Automatic Algorithm-Aided Segmentation of Retinal Nerve Fibers Using Fundus Photographs.

Diego Luján Villarreal1

  • 1Departamento de Mecatrónica y Biomédica, Escuela de Ingeniería y Ciencias, Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey 64700, Mexico.

Journal of Imaging
|September 26, 2025
PubMed
Summary

This study introduces an image processing algorithm to map human retinal nerve fiber layer (RNFL) bundle trajectories. The algorithm accurately traces these fibers, aiding in understanding retinal anatomy and potential disease markers.

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Area of Science:

  • Ophthalmology and Medical Imaging
  • Computational Biology and Image Analysis

Background:

  • Accurate mapping of retinal nerve fiber layer (RNFL) bundle trajectories is crucial for diagnosing and monitoring various ocular conditions.
  • Existing methods may lack precision or struggle with individual anatomical variations.

Purpose of the Study:

  • To develop and validate an image processing algorithm for personalized segmentation and mapping of retinal nerve fiber layer (RNFL) bundle trajectories.
  • To assess the algorithm's accuracy and efficiency in tracing RNFL fibers across diverse fundus images.

Main Methods:

  • Preprocessing for noise reduction and vessel removal.
  • Application of a maximum-minimum modulation algorithm to isolate retinal nerve fiber (RNF) segments.
  • Utilizing a modified Garway-Heath map for orientation categorization and Bezier curves for trajectory fitting.
Keywords:
course of axonsimage processing segmentationoptic pathwaysretinal nerve fiber layerretinal nerve fiber trajectoryvisual pathways

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  • Testing on 300 fundus images from five databases, including healthy and diabetic subjects.

    • Main Results:

    • The algorithm achieved an average efficiency of 97.44% in tracing RNFL trajectories compared to the Jansonius map.
    • Demonstrated good performance on low-resolution images and provided accurate orientation angle measurements (mean difference 11.01 ± 1.25°).
    • Established correlations between RNFL trajectories and visual field test points, with significant influence of optic parameters on specific regions.

    Conclusions:

    • The developed algorithm provides accurate, personalized mapping of RNFL bundle trajectories using accessible biometric data.
    • This tool has potential for enhanced diagnostic capabilities in ophthalmology by distinguishing fibers based on unique anatomical features.
    • The findings highlight the algorithm's utility in research and clinical settings for detailed retinal analysis.