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Optical Coherence Tomography – Automatic Retina Classification Through Support Vector Machines

Published Online: August 5th 2012 European Ophthalmic Review, 2012;6(4):200-3 DOI: http://doi.org/10.17925/EOR.2012.06.04.200
Authors: Rui Bernardes, Pedro Serranho, Torcato Santos, Valter Gonçalves, José Cunha-Vaz
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Abstract:
Overview

Optical coherence tomography (OCT) is becoming one of the most important imaging modalities in ophthalmology due to its non-invasiveness and by allowing the visualisation the human retina structure in detail. It was recently proposed that OCT data embeds functional information from the human retina. Specifically, it was proposed that blood–retinal barrier status information is present within OCT data from the human retina. Besides this ability, the authors present data supporting the idea of having the OCT data encoding the ageing of the retina in addition to the disease (diabetes) condition from the healthy status. The methodology followed makes use of a supervised classification procedure, the support vector machine (SVM) classifier – based solely on the statistics of the distribution of OCT data from the human retina (i.e. OCT data between the inner limiting membrane and the retinal pigment epithelium). Results achieved suggest that information on both the healthy status of the blood–retinal barrier and on the ageing process co-exist encoded within the optical properties of the human retina.

Keywords

Optical coherence tomography, support vector machines, supervised classification, retina, diabetic retinopathy, ageing

Article:

Both the ageing process and diabetes promote changes on the human retina, although not always visible through the regular eye fundus examination. The authors’ research group has been focused on imaging changes within the human retina of diabetic patients aiming for better characterisation and on detection at the very early stages even when these cannot be detected in the eye fundus.

Current trends in medical imaging point to the increasing use of non-invasive techniques. In this sense, the authors started focusing our efforts in assessing the possibility of gathering information from the eye fundus through non-invasive techniques. Nevertheless, these are required to provide the same or even higher levels of information than the one currently provided. A particularly interesting non-invasive technique in use in the field of ophthalmology is the optical coherence tomography (OCT). This imaging technique is spreading quickly and, in consequence, is becoming available in multiple eye care facilities.

The authors research group has been interested in diabetic retinopathy with a special focus on the breakdown of the blood–retinal barrier (BRB) in consequence of diabetes.1–3 The number of diabetic patients is increasing worldwide4 and this multifactorial disease has a large social and economic impact in the active working population.5

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Disclosure

The publication of this article was supported in part by the Fundação para a Ciência e a Tecnologia (FCT) under the research project PTDC/SAU-BEB/103151/2008 and program COMPETE (FCOMP-01-0124-FEDER-010930).

Correspondence

Rui Bernardes, Association for Innovation and Biomedical Research on Light and Image (AIBILI), Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal. E: rcb@aibili.pt

Support

The authors would like to thank Dr Melissa Horne and Carl Zeiss Meditec (Dublin, CA, US) for their support on getting access to OCT data and AIBILI Clinical Trial Centre technicians for their support in managing data, working with patients and performing scans.

Received

2011-10-12T00:00:00

References

  1. Cunha-Vaz J, Faria de Abreu JR, Campos AJ, Early breakdown of the blood-retinal barrier in diabetes, Br J Ophthalmol, 1975;59:649–56.
  2. Cunha-Vaz JG, Pathophysiology of diabetic retinopathy, Br J Ophthalmol, 1978;62(6):351–5.
  3. Cunha-Vaz J, Bernardes R, Lobo C, Blood-retinal barrier, Eur J Ophthalmol, 2010;21(S6):3–9.
  4. IDF Diabetes Atlas fifth edition, Diabetes and Impaired Glucose Tolerance, 2009. Available at: www.idf.org/ diabetesatlas/diabetes-and-impaired-glucosetolerance (accessed 27 September 2011).
  5. IDF Diabetes Atlas fifth edition, The Economic Impacts of Diabetes, 2009. Available at: www.idf.org/diabetesatlas/ economic-impacts-diabetes (accessed 27 September 2011).
  6. Lobo C, Bernardes RC, Santos FJ, Cunha-Vaz JG, Mapping retinal fluorescein leakage with confocal scanning laser fluorometry of the human vitreous, Arch Ophthalmol, 1999;117:631–7.
  7. Bernardes R, Dias J, Cunha-Vaz J, Mapping the human blood-retinal barrier function, IEEE Trans Biomed Eng, 2005;52:106–16.
  8. Lobo CL, Bernardes RC, de Abreu JR, Cunha-Vaz JG, One-year follow-up of blood-retinal barrier and retinal thickness alterations in patients with type 2 diabetes mellitus and mild nonproliferative retinopathy, Arch Ophthalmol, 2001;119:1469–74.
  9. Lobo CL, Bernardes RC, Figueira JP, et al., Three-year follow-up study of blood-retinal barrier and retinal thickness alterations in patients with type 2 diabetes mellitus and mild nonproliferative diabetic retinopathy, Arch Ophthalmol, 2004;122:211–7.
  10. Alfaro V, Gómez-Ulla F, Quiroz-Mercado H, et al., Retinopatía diabética – Tratado médico quirúrgico, 1st edition, Madrid: MAC LINE, SL, 2006.
  11. Bernardes R, Santos T, Serranho P, et al., Noninvasive evaluation of retinal leakage using optical coherence tomograph, Ophthalmologica, 2011;226:29–36.
  12. Bouma B, Tearney G, Handbook of Optical Coherence Tomography, New York, US: Marcel Dekker, Inc, 2002.
  13. Kiernan DF, Hariprasad SM, Chin EK, et al., Prospective comparison of cirrus and stratus optical coherence tomography for quantifying retinal thickness, Am J Ophthalmol, 2009;147:267–75.
  14. Bernardes R, Santos T, Cunha-Vaz J, Evaluation of Blood-Retinal Barrier Function from Fourier Domain High-Definition Optical Coherence Tomography. Proceedings of IFMBE 25/XI, WC, 2009;316–9.
  15. Bernardes R, Optical Coherence Tomography: health information embedded on OCT signal statistics, Proceedings of the 33rd Annual International Conference of the IEEE EMBS, Boston, US, 30 August–3 September 2011.
  16. Duda R, Hart P, Stork D, Pattern Classification, Chichester, UK: Wiley-Interscience, 2000.
  17. Chang C, Lin C, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011. Software available at: www.csie.ntu. edu.tw/~cjlin/libsvm (accessed 7 October 2012).

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