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