April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automated Assessment of Retinal Vascular Abnormalities for Computer-assisted Screening of Retinopathies
Author Affiliations & Notes
  • Vinayak S Joshi
    Medical Image analysis, VisionQuest Biomedical LLC, Albuquerque, NM
  • E Simon Barriga
    Medical Image analysis, VisionQuest Biomedical LLC, Albuquerque, NM
    Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM
  • Sheila C Nemeth
    Medical Image analysis, VisionQuest Biomedical LLC, Albuquerque, NM
  • Wendall Bauman
    Retina Institute of South Texas, San Antonio, TX
  • Peter Soliz
    Medical Image analysis, VisionQuest Biomedical LLC, Albuquerque, NM
  • Footnotes
    Commercial Relationships Vinayak Joshi, VisionQuest Biomedical LLC (E); E Simon Barriga, VisionQuest Biomedical LLC (E); Sheila Nemeth, VisionQuest Biomedical LLC (E); Wendall Bauman, Retina Institute of South Texas (I); Peter Soliz, VisionQuest Biomedical LLC (I)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 233. doi:https://doi.org/
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Vinayak S Joshi, E Simon Barriga, Sheila C Nemeth, Wendall Bauman, Peter Soliz; Automated Assessment of Retinal Vascular Abnormalities for Computer-assisted Screening of Retinopathies. Invest. Ophthalmol. Vis. Sci. 2014;55(13):233. doi: https://doi.org/.

      Download citation file:


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

      ×
  • Supplements
Abstract
 
Purpose
 

To present a fully automated system that uses digital fundus images to detect retinal vascular abnormalities indicative of sight- or life-threatening conditions.

 
Methods
 

The automated retinal vascular abnormality detection system is developed using innovative algorithms for identification of abnormal vessel branching angles, abnormal branching coefficient, artery-venous (AV) nicking, copper/silver wiring, and retinal emboli. These algorithms are based on the vascular features describing color, intensity variation, and morphology; as well as the information obtained from vessel analysis techniques developed for vessel segmentation, segmentation correction, vessel separation, and artery-venous classification. After evaluating the images for quality, the system implements the automatic vessel analysis techniques, and detects the vascular abnormalities. The abnormality information can then be integrated with quantitative vessel morphology properties such as AV ratio and tortuosity, into a graphical user interface to aid the retinal grader in computer-assisted screening of retinopathies. A block diagram in Figure 1 shows the innovative methods in yellow and the previously developed methods in blue.

 
Results
 

We tested each vessel abnormality detection algorithm with a dataset of 25 fundus images, resulting in an average accuracy of 77% against the retinal specialist’s ground truth. This is a marked improvement over previously reported accuracy of retinal vascular abnormalities detection. This software can enhance retinal graders performance by: 1) Improving grader agreement in detection of retinal vascular abnormalities (currently reported Kappa=0.4); 2) Reducing the required grading time by obviating the need for manual analysis (current grading time/patient=15 min).

 
Conclusions
 

This work presents the development of an automated system for detecting retinal vascular abnormalities, which enhances grader’s screening ability in terms of accuracy, grading time, and consistency. This system will aid in improving the screening of retinal vascular abnormalities that are indicative of sight- or life-threatening conditions such as stroke or cardiovascular infarction.

 
 
Figure 1. Block diagram of the retinal vascular assessment system
 
Figure 1. Block diagram of the retinal vascular assessment system
 
Keywords: 551 imaging/image analysis: non-clinical • 549 image processing • 688 retina  
×
×

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.

×