A Novel Approach of Retinal Disorder Diagnosing Using Optical Coherence Tomography Scanners

  • Maciej SzymkowskiEmail author
  • Emil Saeed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10730)


OCT is a promising technology that allows getting a lot of data in each sample. Authors hope that it is possible to create a system that would automatically diagnose various retinal diseases basing on OCT images with the accuracy of 95% which may revolutionize and shorten diagnostic pathway. At the beginning authors focus on automatic distinguishing the healthy images from pathological retinas. In this paper a novel approach has been presented. The algorithm has been described and results have been revealed and discussed. OCT is a way for detecting many various diseases. However, the amount of information to be processed is much more numerous so the task seems to be more difficult than it is in fundus imaging. In this paper some advanced diseases with the macular oedema detection algorithm basing on OCT images are presented.


Optical coherence tomography OCT Diabetes mellitus Age-related macular degeneration Exudates Retina disorders Image processing Classification Computer detection algorithm Automated diagnosis 



We would like express our sincere thanks to Medical University of Bialystok, Department of Ophthalmology. No work could be done without the generous help in sharing the data to process as well as the expertise.

This work was supported by grant S/WI/1/2013 from Bialystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.


  1. 1.
    Kelty, P.J., Payne, J.F., Trivedi, R.H., et al.: Macular thickness assessment in healthy eyes based on ethnicity using stratus OCT Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 49(6), 2668–2672 (2008)CrossRefGoogle Scholar
  2. 2.
    Shah, A.R., Williams, S., Baumal, C.R., et al.: Predictors of response to intravitreal anti-vascular endothelial growth factor treatment of age-related macular degeneration. Am. J. Ophthalmol. 163, 154–166 (2016)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Li, J., Yan, Y., et al.: Diabetic macular morphology changes may occur in the early stage of diabetes. BMC Ophthalmol. 16(12) (2016)Google Scholar
  4. 4.
    Gooding, K.M., Shore, A.C., Ling, R., et al.: Regional differences in macular thickness in the early stages of diabetic retinopathy in type 2 diabetes. Diabetologia 58(1), S526 (2015)Google Scholar
  5. 5.
    Bressler, N.M., Edwards, A.R., Antoszyk, A.N., et al.: Retinal thickness on stratus Optical Coherence Tomography in people with diabetes and minimal or no diabetic retinopathy. Am. J. Ophthalmol. 145(5), 894–901 (2008)CrossRefGoogle Scholar
  6. 6.
    Chen, X., Song, W., Cai, H., et al.: Macular edema after cataract surgery in diabetic eyes evaluated by optical coherence tomography. Int. J. Ophthalmol. 9(1), 81–85 (2016)Google Scholar
  7. 7.
    Mokwa, N.F., Ristau, T., Keane, P.A., et al.: Grading of age-related macular degeneration: comparison between color fundus photography, fluorescein angiography, and spectral domain optical coherence tomography. J. Ophthalmol. (2013). Article ID 385915Google Scholar
  8. 8.
    Swiebocka-Wiek, J.: The detection of the retina’s lesions in Optical Coherence Tomography (OCT). In: Kulczycki, P., Kóczy, László T., Mesiar, R., Kacprzyk, J. (eds.) CITCEP 2016. AISC, vol. 462, pp. 179–195. Springer, Cham (2017). CrossRefGoogle Scholar
  9. 9.
    Lee, J.Y., Stephanie, J.C., Pratul, P.S., et al.: Fully automatic software for retinal thickness in eyes with diabetic macular edema from images acquired by cirrus and spectralis systems. Invest. Ophthalmol. Vis. Sci. 54(12), 7595–7602 (2013)CrossRefGoogle Scholar
  10. 10.
    Chen, X., Niemeijer, M., Zhang, L., et al.: 3D segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut. IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012)CrossRefGoogle Scholar
  11. 11.
    Lammer, J., Bolz, M., Baumann, B., Pircher, M., Gerendas, B., Schlanitz, F., Hitzenberger, C.K., Schmidt-Erfurth, U.: Detection and analysis of hard exudates by polarization-sensitive optical coherence tomography in patients with diabetic maculopathy. Invest. Ophthalmol. Vis. Sci. 55(3), 1564–1571 (2014)CrossRefGoogle Scholar
  12. 12.
    Stankiewicz, A., Marciniak, T., Dąbrowski, A., Stopa, M., Rakowicz, P., Marciniak, E.: Denoising methods for improving automatic segmentation in OCT images of human eye. Bull. Pol. Academic. Sci. Tech. Sci. 1 (2017)Google Scholar
  13. 13.
    Esmaeili, M., Rabbani, H., Dehnavi, A.M., Dehghani, A.: Automatic detection of exudates and optic disk in retinal images using curvelet transform. IET Image Process. 6(7), 1005–1013 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Rokade, P.M., Manza, R., Jonathan, P.: Computer aided hard exudates detection on digital fundus images using morphology and multi-resolution analysis. Int. J. Adv. Comput. Technol. (IJACT) 4(5), 20–27 (2015)Google Scholar
  15. 15.
    Ganesh Babua, T.R., Shenbaga Devi, S., Venkateshc, R.: Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection. Biomed. Pap. Med. Fac. Univ. Palacky Olomouc Czech Repub. 159(4), 607–615 (2015)Google Scholar
  16. 16.
    Saeed, K., Nagashima, T.: Biometrics and Kansei Engineering. Springer, New York (2012). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland
  2. 2.Department of Ophthalmology, Faculty of MedicineMedical University of BialystokBialystokPoland

Personalised recommendations