September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
New Public Retinal Image Database for Tortuosity Evaluation
Author Affiliations & Notes
  • Jeffrey C Wigdahl
    Information Engineering, University of Padova, Padova, Italy
  • Roberto Annunziata
    Vampire Project, School of Science and Engineering (Computing), University of Dundee, Dundee, United Kingdom
  • Laura Hughes
    College of Medicine and Veterinary Medicine, University of Edinburgh, Edinbrugh, United Kingdom
  • Shyamanga Borooah
    Center for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
  • Alfredo Ruggeri
    Information Engineering, University of Padova, Padova, Italy
  • Emanuele Trucco
    Vampire Project, School of Science and Engineering (Computing), University of Dundee, Dundee, United Kingdom
  • Footnotes
    Commercial Relationships   Jeffrey Wigdahl, None; Roberto Annunziata, None; Laura Hughes, None; Shyamanga Borooah, None; Alfredo Ruggeri, None; Emanuele Trucco, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3399. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Jeffrey C Wigdahl, Roberto Annunziata, Laura Hughes, Shyamanga Borooah, Alfredo Ruggeri, Emanuele Trucco; New Public Retinal Image Database for Tortuosity Evaluation. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3399.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : Tortuosity in retinal images can be used as a biomarker in the detection of several systemic diseases, including diabetes and hypertension. This work provides a new retinal image database to test and compare tortuosity metrics at both the vessel and image level, as well as a comparison of several of the popular methods for tortuosity estimation on the dataset.

Methods : One macula-centered image was acquired for each eye in 37 patients (74 images) at the University of Edinburgh using a Canon non-mydriatic camera at 45° field of view. A total of 100 arteries and 100 veins were chosen and graded from the images. The tortuosity of these vessel segments was graded as either absent, low or high by two clinical specialists. Image-level tortuosity was also graded on the same scale by a total of 5 specialists. The database consists of the retinal images, vessel centerline points from chosen vessels (used to reproduce the vessel path), and the ground truth. Six previously developed tortuosity metrics were tested against a representative subset of the database (25 arteries and 25 veins). These metrics are the arch /chord ratio (DM), tortuosity density (TD), slope chain coding (SCC), and two integral curvature measures (Tau3, 5). Descriptions of algorithms have been reported previously (Lisowska et al., EMBC 2014). Full dataset and results will be made available at http://bioimlab.dei.unipd.it/Data%20Sets.htm

Results : Intergrader variability was calculated for the subset of vessels. Cohen’s kappa for agreement was .73 for veins and .61 for arteries. Tortuosity metrics were calculated and agreement between metrics and the two graders can be seen in Table 1. Results show that no one metric had the highest agreement simultaneously across veins, arteries, and graders. The tortuosity density metric had the highest average agreement across all categories.

Conclusions : This work provides a new public database for tortuosity estimation including images, vessel segments, and ground truth. Results on a subset of vessels suggests that a single tortuosity metric has difficulty capturing the qualitative grading of clinicians.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Table 1. Agreement between metrics and Graders. Values represent Cohen’s kappa and linear weighted kappa statistic.

Table 1. Agreement between metrics and Graders. Values represent Cohen’s kappa and linear weighted kappa statistic.

 

Fig. 1. Examples of different tortuosity levels (0-2) by row from the database. Red and Blue dots mark the start and end points of the vessels. For the images above, both graders were in agreement on the tortuosity grade.

Fig. 1. Examples of different tortuosity levels (0-2) by row from the database. Red and Blue dots mark the start and end points of the vessels. For the images above, both graders were in agreement on the tortuosity grade.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×