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Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector.

Alexander Ze Hwan Ooi1, Zunaina Embong2, Aini Ismafairus Abd Hamid3,4

  • 1School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia.

Sensors (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study introduces an interactive method for segmenting blood vessels in retinal fundus images using Canny edge detection. This technique aids medical professionals in analyzing eye conditions by highlighting specific retinal vessels.

Keywords:
blood vesselsedge segmentationfundus imagesretinal

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Fundus photography is crucial for monitoring eye diseases.
  • Retinal vessel segmentation is essential for accurate diagnosis.
  • Existing methods may lack interactivity and precision.

Purpose of the Study:

  • To propose an interactive blood vessel segmentation method for retinal fundus images.
  • To enhance the analysis of specific retinal vessels for disease detection.
  • To provide a user-friendly tool for medical professionals.

Main Methods:

  • Utilized Canny edge detection for vessel segmentation.
  • Implemented a semi-automated approach via a graphical user interface (GUI).
  • Pre-processing involved green channel extraction, CLAHE, and retinal outline removal.

Main Results:

  • Successfully segmented specific blood vessels interactively.
  • Enabled highlighting of particular vessels through adjustable edge detection parameters.
  • Demonstrated potential for detailed analysis and detection of abnormal vessels.

Conclusions:

  • The proposed interactive Canny edge detection method offers an effective tool for retinal vessel analysis.
  • This approach facilitates focused examination of retinal vasculature for diagnosing eye conditions.
  • The GUI-based system improves usability for optometrists and ophthalmologists.