April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
Healthy Retinal Layers Thickness Detection by Optical Coherence Tomography Spectral Domain Images
Author Affiliations & Notes
  • Cristina M. Savastano
    Department of Ophthalmology, Catholic University, Rome, Italy
  • Gaspare Giovinco
    Department of Di.M.S.A.T.,
    University of Cassino, (FR), Italy
  • Salvatore Ventre
    Department of D.A.E.I.M.I,
    University of Cassino, (FR), Italy
  • Emilio Balestrazzi
    Department of Ophthalmology, Catholic University, Rome, Italy
  • Antonello Tamburrino
    Department of D.A.E.I.M.I,
    University of Cassino, (FR), Italy
  • Footnotes
    Commercial Relationships  Cristina M. Savastano, None; Gaspare Giovinco, None; Salvatore Ventre, None; Emilio Balestrazzi, None; Antonello Tamburrino, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 3673. doi:
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      Cristina M. Savastano, Gaspare Giovinco, Salvatore Ventre, Emilio Balestrazzi, Antonello Tamburrino; Healthy Retinal Layers Thickness Detection by Optical Coherence Tomography Spectral Domain Images. Invest. Ophthalmol. Vis. Sci. 2011;52(14):3673.

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

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Abstract
 
Purpose:
 

Accurate diagnosis and management of retinal diseases strictly depend on non-invasive imaging techniques such as Optical Coherence Tomography (OCT) in the frequency domain, which allows the collection of in-vivo retinal structural information without any invasive intervention.Therefore, OCT images segmentation is extremely useful for the detection of each retinal layer thickness. For this aim, the authors developed a fully automatic image segmentation algorithm that provides quantitative thickness measurement of retinal layers.

 
Methods:
 

In order to demonstrate the ability of the software, the authors used 4 healthy eyes images acquired from 4 of the latest commercially available OCT systems: Zeiss Cirrus, RTvue-100 Optovue, Heidelberg Spectralis, Nidek RS-3000.The software is very fast (about 45 s/image on a 2.33 GHz CPU) and the peak memory usage is about 1.3 GB. Therefore, it can be easily embedded in the OCT software provided by the manufacturers, to obtain quasi real time analyses.

 
Results:
 

The algorithm identifies 6 boundaries and measures thickness of 5 retinal layers: 1) nerve fiber layer, 2) inner plexiform layer and ganglion cell layer, 3) inner nuclear layer and outer plexiform layer, 4) outer nuclear layer and photoreceptor inner segments and 5) photoreceptor outer segments.

 
Conclusions:
 

The application of this image segmentation technique is very effective to investigate thickness of retinal layers and will be used by the authors to obtain retinal layer thickness normative data for the different OCT systems.  

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