May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Development of a Graphic User Interface as an Additional Tool of Diagnostic Differentiation of Retinal Tissue Using Optical Coherence Tomography
Author Affiliations & Notes
  • W. Gao
    Bascom Palmer Eye Institute, University of Miami, Miami, Florida
  • S. Ranganathan
    Bascom Palmer Eye Institute, University of Miami, Miami, Florida
  • E. Tátrai
    Department of Ophthalmology, Semmelweis University, Budapest, Hungary
  • G. M. Somfai
    Department of Ophthalmology, Semmelweis University, Budapest, Hungary
  • D. C. Fernández
    Bascom Palmer Eye Institute, University of Miami, Miami, Florida
  • Footnotes
    Commercial Relationships  W. Gao, None; S. Ranganathan, None; E. Tátrai, None; G.M. Somfai, None; D.C. Fernández, None.
  • Footnotes
    Support  NEI P30 EY014801 and Research to Prevent Blindness
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 1891. doi:https://doi.org/
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      W. Gao, S. Ranganathan, E. Tátrai, G. M. Somfai, D. C. Fernández; Development of a Graphic User Interface as an Additional Tool of Diagnostic Differentiation of Retinal Tissue Using Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1891. doi: https://doi.org/.

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

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Abstract
 
Purpose:
 

To introduce and describe a quantitative tool for the local analysis of structural and optical properties of the intraretinal layers on Stratus OCT images.

 
Methods:
 

The OCT raw data was exported to a compatible PC and pre-processed using OCT Retinal Image Analysis software (OCTRIMA). As a result, the various cellular layers of the retina (RNFL, GCL+IPL, INL, OPL, ONL, IS/OS and RPE) were extracted and the segmentation results along with the local reflectivity profiles were used as the main input data in the GUI developed. All functionality was programmed using the GUI capabilities of Matlab. The GUI contains one graphic window for the visualization of the raw data and segmentation results, and two control panels that allow the user to pre-process the image and also analyze the output results. The output control panel includes the scattering coefficients and co-ocurrence based classifiers results obtained for the extracted intraretinal layers. The co-ocurrence based classifiers implemented were energy, entropy, correlation, local homogeneity and contrast. One subroutine was designed to let the user to select the target area or the specific intraretinal layer, and a second subroutine facilitated the calculation of the quantitative measures.

 
Results:
 

The GUI delivers additional quantitative measures for each OCT scan and offers new insights into the structural and optical properties of the retina. Figure below shows the main program window of the GUI in its first stage of development.

 
Conclusions:
 

It was possible to develop a GUI that includes specific subroutines, which increase the amount of information that can be gathered from OCT examination, and might have the potential to provide an additional tool of diagnostic differentiation of retinal tissue.  

 
Keywords: imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • imaging/image analysis: non-clinical • optical properties 
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