April 2011
Volume 52, Issue 14
ARVO Annual Meeting Abstract  |   April 2011
A Comparison between a new Automated Macular Edema Screening System and Retina Experts Diagnosis
Author Affiliations & Notes
  • Luca Giancardo
    U. of Burgundy, Le Creusot, France
    Oak Ridge National Laboratory, Oak Ridge, Tennessee
  • Fabrice Meriaudeau
    U. of Burgundy, Le Creusot, France
  • Thomas P. Karnowski
    Oak Ridge National Laboratory, Oak Ridge, Tennessee
  • Kenneth W. Tobin, Jr.
    Oak Ridge National Laboratory, Oak Ridge, Tennessee
  • Yaqin Li
    U. Tennessee, Hamilton Eye Institute, Memphis, Tennessee
  • Seema Garg
    Dept. of Ophthalmology, U. of North Carolina, Chapel Hill, North Carolina
  • Karen Fox
    Delta Health Alliance, Stoneville, Mississippi
  • Edward Chaum
    U. Tennessee, Hamilton Eye Institute, Memphis, Tennessee
  • Footnotes
    Commercial Relationships  Luca Giancardo, None; Fabrice Meriaudeau, None; Thomas P. Karnowski, None; Kenneth W. Tobin, Jr., TRIAD system (P); Yaqin Li, None; Seema Garg, None; Karen Fox, None; Edward Chaum, TRIAD system (P)
  • Footnotes
    Support  Oak Ridge National Laboratory, the NEI, (EY017065), by a UTHSC Departmental grant from RPB/Fight for Sight, New York, NY, by the Plough Foundation, Memphis, TN and by the Burgundy Council, France
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 1338. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Luca Giancardo, Fabrice Meriaudeau, Thomas P. Karnowski, Kenneth W. Tobin, Jr., Yaqin Li, Seema Garg, Karen Fox, Edward Chaum; A Comparison between a new Automated Macular Edema Screening System and Retina Experts Diagnosis. Invest. Ophthalmol. Vis. Sci. 2011;52(14):1338.

      Download citation file:

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

  • Supplements

Purpose: : Telemedicine and computer assisted analysis can aid in the early detection of diabetic retinopathy and other diseases in large patient populations. We have developed an automated system to diagnose macular edema (ME) in retinal fundus images. The algorithm was developed for a telemedicine network of primary care clinics based in Memphis, TN, North Carolina, and Mississippi. We present a comparative study between the automated diagnosis system and the diagnosis of two retina specialists.

Methods: : Two publicly available datasets are employed: DMED and Messidor. The former is a set of 169 fundus images collected from our telemedicine network which were used to train and develop the automatic diagnosis algorithms. The latter set consists of 1200 images collected and diagnosed by outside experts; these are employed to determine the automatic and the retina specialists’ diagnosis. The ME diagnosis algorithms are based on a high-speed computer-based "hard exudates" detection. The algorithms detect the field of view, normalize the image, then perform edge analysis and evaluation of the global probability of exudation. The final diagnosis is generated using a pure pattern recognition approach: a novel set of features is computed and employed to train a classifier. When a new image is presented to the system, the classifier produces a diagnosis based on the images shown during the training phase.

Results: : Two retina specialists determined the presence or absence of exudates in order to diagnose ME on a random sample of 300 images of the Messidor dataset, with 104 images exhibiting ME and 196 exhibiting no ME. The automatic system operated on the entire dataset. The reference standard is provided by the Messidor dataset, which has been employed by various research groups. We compared the performance of the automatic system by creating the ROC Curve and overlaid the Specificity/Sensitivity of the two retina specialists. Also we used Kappa value/AC1-statistics as concordance metrics, obtaining: Expert1 0.63/0.73, Expert2 0.62/0.64, Automatic Diagnosis 0.64/0.85.

Conclusions: : Our approach is a promising method to automatically screen for ME. With this study we demonstrate that the system's performances is comparable to retina specialists. Similar studies on diabetic retinopathy will be conducted in the future to further evaluate the automated system.

Keywords: image processing • edema • imaging/image analysis: clinical 

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.