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Updated: May 14, 2026

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
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Detecting flash artifacts in fundus imagery.

Vincent C Paquit1, Thomas P Karnowski, Deniz Aykac

  • 1Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. paquitvc@ornl.gov

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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A new method detects flash artifacts in retina images for telemedicine screening. This quality check improves automated diagnosis by identifying images needing special handling or artifact removal.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Telemedicine for retinopathy screening requires high-quality fundus images for accurate diagnosis.
  • Existing quality checks may miss flash artifacts, impacting automated anomaly detection.
  • Flash artifacts can compromise the reliability of both human and automated diagnostic systems.

Purpose of the Study:

  • To develop and evaluate a method for detecting flash artifacts in retina fundus images.
  • To enhance the quality control process in telemedicine-based eye screening.
  • To improve the performance of automated systems for detecting retinal anomalies.

Main Methods:

  • A multi-step algorithm combining colorimetric information and morphological pattern matching was developed.

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  • The method specifically targets flash artifacts in the central retinal area.
  • The algorithm was tested on a dataset of 5218 retina fundus images.

    • Main Results:

    • The flash artifact detection system achieved a sensitivity of 96.54%.
    • The system demonstrated a specificity of 70.16% in identifying flash artifacts.
    • The method proved effective on a large, representative image dataset.

    Conclusions:

    • The developed flash artifact detection method is a valuable tool for quality screening in telemedicine networks.
    • Implementing this detection can improve automated analysis by flagging images for special processing.
    • This technique supports more reliable automated detection of retinal anomalies.