Retinal artery/vein classification using genetic-search feature selection

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Background and objectives: The automatic classification of retinal blood vessels into artery and vein (A/V) is still a challenging task in retinal image analysis. Recent works on A/V classification mainly focus on the graph analysis of the retinal vasculature, which exploits the connectivity of vessels to improve the classification performance. While they have overlooked the importance of pixel-wise classification to the final classification results. This paper shows that a complicated feature set is efficient for vessel centerline pixels classification. Methods: We extract enormous amount of features for vessel centerline pixels, and apply a genetic-search based feature selection technique to obtain the optimal feature subset for A/V classification. Results: The proposed method achieves an accuracy of 90.2%, the sensitivity of 89.6%, the specificity of 91.3% on the INSPIRE dataset. It shows that our method, using only the information of centerline pixels, gives a comparable performance as the techniques which use complicated graph analysis. In addition, the results on the images acquired by different fundus cameras show that our framework is capable for discriminating vessels independent of the imaging device characteristics, image resolution and image quality. Conclusion: The complicated feature set is essential for A/V classification, especially on the individual vessels where graph-based methods receive limitations. And it could provide a higher entry to the graph-analysis to achieve a better A/V labeling.

Original languageEnglish
Pages (from-to)197-207
Number of pages11
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - 1 Jul 2018


  • Artery/vein classification
  • Fundus image
  • Genetic search feature selection
  • Reproducibility of Results
  • Humans
  • Retinal Vein/diagnostic imaging
  • Probability
  • Electronic Data Processing
  • Retinal Artery/diagnostic imaging
  • Models, Statistical
  • False Positive Reactions
  • Machine Learning
  • Artifacts
  • Algorithms
  • Sensitivity and Specificity
  • Image Processing, Computer-Assisted/methods
  • Programming Languages
  • Pattern Recognition, Automated


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