Analysis of Retinal Vascular Biomarkers for Early Detection of Diabetes

Jiong Zhang, Behdad Dashtbozorg, Fan Huang, Tos T. J. M. Berendschot, Bart M. ter Haar Romeny

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

This paper presents an automated retinal vessel analysis system for the measurement and statistical analysis of vascular biomarkers. The proposed retinal vessel enhancement, segmentation, optic disc and fovea detection algorithms provide fundamental tools for extracting the vascular network within the predefined region of interest (ROI). Based on that, the artery/vein classification, vessel caliber, curvature and fractal dimension measurement tools are used to assess the quantitative vascular biomarkers: width, tortuosity, and fractal dimension. A statistical analysis on the extracted geometric biomarkers is set up using a dataset provided by the Maastricht study with the aim of exploring the associations between different vessel biomarkers and type 2 diabetes mellitus. A linear regression analysis is used to model the relationships between different factors. The results indicate that the vascular biomarker variables have associations with diabetes. These findings demonstrate the possibility of applying the proposed pipeline tools on further analysis of vessel biomarkers for the computer-aided diagnosis.
Original languageEnglish
Title of host publicationVipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017
EditorsJoão Manuel R.S. Tavares, R.M. Natal Jorge
Place of PublicationCham
PublisherSpringer
Pages811-817
Number of pages7
ISBN (Print)978-3-319-68195-5
DOIs
Publication statusPublished - 2018

Cite this

Zhang, J., Dashtbozorg, B., Huang, F., Berendschot, T. T. J. M., & ter Haar Romeny, B. M. (2018). Analysis of Retinal Vascular Biomarkers for Early Detection of Diabetes. In J. M. R. S. Tavares, & R. M. Natal Jorge (Eds.), VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017 (pp. 811-817). Springer. https://doi.org/10.1007/978-3-319-68195-5_88