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A Detailed Systematic Review on Retinal Image Segmentation Methods.

Nihar Ranjan Panda1, Ajit Kumar Sahoo2

  • 1Department of Electronics and Communication Engineering, Silicon Institute of Technology, Bhubaneswar, Orissa, 751024, India. niharranjanpandarp@gmail.com.

Journal of Digital Imaging
|May 4, 2022
PubMed
Summary
This summary is machine-generated.

This review compares deep learning and machine learning for retinal blood vessel segmentation. Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM) achieved the highest accuracy (98%) for early disease detection.

Keywords:
Fundus imageMatched filteringMulti-scale approachNeural network methodsRetinal segmentationSupervised segmentationVessel tracing

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Retinal blood vessel segmentation is crucial for detecting diseases like diabetes and hypertension.
  • Existing reviews often focus on single frameworks, limiting comprehensive analysis.

Purpose of the Study:

  • To review and compare diverse methodologies for retinal blood vessel segmentation.
  • To identify the most effective neural network model for this task.

Main Methods:

  • Comparison of machine learning (ML) and deep learning (DL) approaches.
  • Evaluation of segmentation performance using metrics like sensitivity, specificity, and accuracy.
  • Utilized public datasets including STARE, DRIVE, ROSE, REFUGE, and CHASE.

Main Results:

  • Deep learning models, particularly Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM), demonstrated superior performance.
  • CNN-rSVM achieved an accuracy of 98% and sensitivity of 96% on public datasets.
  • The study validated the implementation capacity of various segmentation techniques.

Conclusions:

  • CNN-rSVM is a highly effective method for retinal blood vessel segmentation.
  • Accurate segmentation facilitates earlier diagnosis and treatment of serious eye conditions.
  • This review provides a benchmark for selecting optimal models in retinal image analysis.