April 2011
Volume 52, Issue 14
ARVO Annual Meeting Abstract  |   April 2011
Automated Computerized Analysis of Optic Nerve Images for Detection and Staging of Papilledema
Author Affiliations & Notes
  • Sebastian Echegaray
    VisionQuest Biomedical LLC, Albuquerque, New Mexico
  • Gilberto Zamora
    VisionQuest Biomedical LLC, Albuquerque, New Mexico
  • Honggang Yu
    VisionQuest Biomedical LLC, Albuquerque, New Mexico
  • Wenbin Luo
    Engineering Department, St. Mary's University, San Antonio, Texas
  • Peter Soliz
    VisionQuest Biomedical LLC, Albuquerque, New Mexico
  • Randy H. Kardon
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa
  • Footnotes
    Commercial Relationships  Sebastian Echegaray, VisionQuest Biomedical LLC (E); Gilberto Zamora, VisionQuest Biomedical LLC (E); Honggang Yu, VisionQuest Biomedical LLC (E); Wenbin Luo, St. Mary's University (F); Peter Soliz, VisionQuest Biomedical LLC (E); Randy H. Kardon, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 2985. doi:
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      Sebastian Echegaray, Gilberto Zamora, Honggang Yu, Wenbin Luo, Peter Soliz, Randy H. Kardon; Automated Computerized Analysis of Optic Nerve Images for Detection and Staging of Papilledema. Invest. Ophthalmol. Vis. Sci. 2011;52(14):2985.

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

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Purpose: : To develop and validate an automated system to analyze digital fundus images for staging and monitoring swelling of the optic disc due to raised intracranial pressure.

Methods: : A retrospective set of 294 longitudinal, digital fundus photographs of the optic disc from 39 subjects with papilledema was used. Ground truth was determined from independent analysis by three neuro-ophthalmologists using the modified Frisen Scale. A suite of image analysis algorithms was developed to quantify the features used by human expert analysis: 1) blurriness of the optic disc border was assessed by analyzing the location along radial lines from the center of the disc with the largest change in pixel intensity, 2) texture features of the RNFL were quantified within a peripapillary retinal annulus, 3) vessel obscuration was defined by a new metric, the vessel continuity index (VCI), that quantifies the continuity along each vessel’s radial length. We used a decision tree forest algorithm for classification and leave-one-out cross correlation to test the performance of our model.

Results: : The algorithm showed substantial agreement (Κ = .71, p < 0.001) with ground truth when grading papilledema per patient, and substantial agreement (Κ = .61, p < 0.001) when evaluating per image. Each of the image features that were quantified changed with grade of papilledema.

Conclusions: : These results show that it is feasible to automatically detect and stage papilledema. The algorithm provides objective, quantitative, accurate, and repeatable assessment of the stage of papilledema at levels of accuracy comparable to those of expertly trained neuro-ophthalmologists. This algorithm could be used for rapid analysis of digital images acquired in clinical, intensive care, and emergency response settings by non-ophthalmologists to diagnose papilledema and its severity.

Keywords: optic disc • optic nerve • image processing 

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