April 2009
Volume 50, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2009
Automated Glaucoma Classification Using Nerve Fiber Layer Segmentations on Circular Spectral Domain OCT B-Scans
Author Affiliations & Notes
  • M. A. Mayer
    Chair of Pattern Recognition, Department of Computer Science,
    Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  • J. Hornegger
    Chair of Pattern Recognition, Department of Computer Science,
    Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  • C. Y. Mardin
    Department of Ophthalmology,
    Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  • F. E. Kruse
    Department of Ophthalmology,
    Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  • R. P. Tornow
    Department of Ophthalmology,
    Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  • Footnotes
    Commercial Relationships  M.A. Mayer, None; J. Hornegger, Siemens AG, F; C.Y. Mardin, None; F.E. Kruse, None; R.P. Tornow, None.
  • Footnotes
    Support  Erlangen Graduate School in Advanced Optical Technologies (SAOT), German Research Foundation (DFG SFB 539)
Investigative Ophthalmology & Visual Science April 2009, Vol.50, 1101. doi:
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      M. A. Mayer, J. Hornegger, C. Y. Mardin, F. E. Kruse, R. P. Tornow; Automated Glaucoma Classification Using Nerve Fiber Layer Segmentations on Circular Spectral Domain OCT B-Scans. Invest. Ophthalmol. Vis. Sci. 2009;50(13):1101.

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

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Abstract

Purpose: : To inspect the possibility for a glaucoma classification using an automated nerve fiber layer segmentation on circular OCT scans.

Methods: : Circular B-scans (diameter 3.4mm, 512 or 768 A-scans) around the optic disk were acquired from 204 subjects using a spectral domain OCT system (Spectralis HRA+OCT, Heidelberg Engineering). The patients were diagnosed by experts and separated into a Normal (N, 132 subjects) and Glaucoma (G, 72 subjects) group. This leads to a two-class classification problem. The method for an automated glaucoma classification was as follows: An automated nerve fiber layer (NFL) segmentation algorithm developed at our department was used to obtain NFL thickness profiles (example see figure). Out of these profiles the following two feature types were generated resulting in 14 features:- The minimum, maximum and mean was calculated for: All profile values, the one-third biggest and the one-third smalles ones.- The thickness profiles (768 and 512 A-Scans) were reduced to 128 values by averaging neighbours. This vector was further compressed to five values using principal component analysis.To eliminate the possibility that the age related degradation of the NFL affects the results, for the training and testing of the classifier subjects were excluded randomly in such a way that the age-distribution was equal in all age decades. This reduction left 61 N and 61 G patients. A Support Vector Machine classifier was used for classification. A 10 fold crossvalidation determined the result. The experiments were carried out 10 times to capture the variation of the different random exclusions.

Results: : The classification accuracy was on average 88,5% and the ROC area 88,5%, too. For glaucoma detection a sensitivity of 91,1% and a specificity of 87,0% was achieved.

Conclusions: : The results show that the NFL thickness from a single circular B-Scan is a useful glaucoma indicator that allows automated classification with a high accuracy. A possible application of such a classification is to use it in cheap OCT devices for a mass glaucoma screening.

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