May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
An Image Processing Method for Quantitative Thickness Measurement of Retinal Layers Imaged by Optical Coherence Tomography
Author Affiliations & Notes
  • A. M. Bagci
    University of Illinois at Chicago, Chicago, Illinois
    Electrical and Computer Engineering,
  • M. Shahidi
    University of Illinois at Chicago, Chicago, Illinois
    Ophthalmology and Visual Sciences,
  • R. Ansari
    University of Illinois at Chicago, Chicago, Illinois
    Electrical and Computer Engineering,
  • N. Blair
    University of Illinois at Chicago, Chicago, Illinois
    Ophthalmology and Visual Sciences,
  • M. Blair
    University of Illinois at Chicago, Chicago, Illinois
    Ophthalmology and Visual Sciences,
  • R. Zelkha
    University of Illinois at Chicago, Chicago, Illinois
    Ophthalmology and Visual Sciences,
  • Footnotes
    Commercial Relationships  A.M. Bagci, None; M. Shahidi, None; R. Ansari, None; N. Blair, None; M. Blair, None; R. Zelkha, None.
  • Footnotes
    Support  Department of VA, NEI
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 1889. doi:https://doi.org/
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    • Get Citation

      A. M. Bagci, M. Shahidi, R. Ansari, N. Blair, M. Blair, R. Zelkha; An Image Processing Method for Quantitative Thickness Measurement of Retinal Layers Imaged by Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1889. doi: https://doi.org/.

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

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Abstract

Purpose: : The ability to measure thickness of retinal layers has potential value for early detection of pathologies and disease monitoring. A new image processing segmentation algorithm was developed for automated detection of retinal layer boundaries and measurement of thickness of 6 retinal layers in optical coherence tomography (OCT) images.

Methods: : OCT images were acquired with time and spectral domain OCT instruments in 15 visually normal healthy subjects. A dedicated software program was developed in Matlab for image processing and analysis. The image processing algorithm segmented the OCT image by the following steps: 1) alignment of A-scans; 2) gray-level mapping; 3) directional filtering; 4) edge detection; and 5) model-based decision making. Thickness profiles for 6 retinal layers were generated in normal subjects. Automated boundary detection and quantitative thickness measurements estimated using the algorithm were compared with measurements obtained from boundaries manually marked by 3 observers.

Results: : On OCT images, 7 retinal layer boundaries were automatically identified by the algorithm. The root mean squared error (RMSE) between the manual and automatic boundary detection ranged between 3 and 10 microns. Thickness profiles were generated for 6 retinal layers: nerve fiber layer (NFL), inner plexiform layer and ganglion cell layer (IPL+GCL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and photoreceptor inner segments (ONL+PIS), and photoreceptor outer segments (POS). The mean absolute values of differences between automated and manual thickness measurements were comparable to inter-observer differences, ranging between 3 and 4 microns. Thickness profiles of retinal layers corresponded with normal anatomy. Inner retinal thickness profiles demonstrated minimum thickness at the fovea. The OPL and ONL+PIS thickness profiles displayed a minimum and maximum thickness at the fovea, respectively. The POS thickness profile was relatively constant along the OCT scans through the fovea.

Conclusions: : The application of this image processing technique is promising for investigating thickness changes of retinal layers due to disease progression and therapeutic intervention.

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