Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy

Enrico Pellegrini (Lead / Corresponding author), Gavin Robertson, Emanuele Trucco, Tom J MacGillivray, Carmen Lupascu, Jano van Hemert, Michelle C. Williams, David E. Newby, Edwin J. R. van Beek, J. Graeme Houston

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected vessel contours, the vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in vessel segmentation and in width estimation, in comparison to other automatic techniques.
Original languageEnglish
Pages (from-to)4329-4337
Number of pages9
JournalBiomedical Optics Express
Issue number12
Publication statusPublished - 1 Dec 2014


  • Image analysis
  • Image recognition, algorithms and filters
  • Pattern recognition
  • Neural networks
  • Ophthalmology


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