A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases

Jiong Zhang, Behdad Dashtbozorg, Fan Huang, Tao Tan (Corresponding author), B.M. ter Haar Romeny

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This paper presents an automated system for extracting retinal vascular biomarkers for early detection of diabetes. The proposed retinal vessel enhancement, segmentation, optic disc (OD) and fovea detection algorithms provide fundamental tools for extracting the vascular network within the predefined region of interest. Based on that, the artery/vein classification, vessel width, tortuosity and fractal dimension measurement tools are used to assess a large number of quantitative vascular biomarkers. We evaluate our pipeline module by module against human annotations. The results indicate that our automated system is robust to the localisation of OD and fovea, segmentation of vessels and classification of arteries/veins. The proposed pipeline helps to increase the effectiveness of the biomarkers extraction and analysis for the early diabetes, and therefore, has the large potential of being further incorporated into a computer-aided diagnosis system.

Original languageEnglish
Pages (from-to)616-631
Number of pages16
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Issue number5-6
Publication statusPublished - 2 Nov 2019



  • computer-aided diagnosis
  • diabetes
  • Retinal image analysis
  • vessel biomarkers

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