Automatic Detection of Folds and Wrinkles Due to Swelling of the Optic Disc

  • Jason AgneEmail author
  • Jui-Kai Wang
  • Randy H. Kardon
  • Mona K. GarvinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)


We propose a method for detecting mechanically induced wrinkles that present themselves around the optic disc as a result of swelling. Folds and wrinkles have recently been found to be useful features for diagnosing optic disc swelling and for differentiating papilledema (optic nerve swelling due to raised intracranial pressure) from pseudopapilledema. A total of 22 patients were diagnosed with varying degrees and causes of optic disc swelling, with 3D spectral domain optical coherence tomography (SD-OCT) images obtained. The images were used to create fold-enhanced 2D images. Features were extracted pertaining to the orientation, Gabor responses, Fourier responses, and coherence to train a pixel-level classifier to distinguish between folds, vessels, image artifacts, and background. An area under the curve of 0.804 was achieved for the classification.



This study was supported, in part, by the National Institutes of Health R01 EY023279 and the Department of Veterans Affairs Merit Award I01 RX001786.


  1. 1.
    Hayreh, S.S.: Ischemic optic neuropathy. Int. Ophthalmol. 1(1), 9–18 (1978)CrossRefGoogle Scholar
  2. 2.
    Hayreh, S.S.: Optic disc edema in raised intracranial pressure, V. Pathogenesis. Arch. Ophthalmol. 97(9), 1553–1565 (1977)CrossRefGoogle Scholar
  3. 3.
    Sibony, P.A., Kupersmith, M.J., Feldon, S.E., Wang, J.K., Garvin, M., The OCT Substudy Group for the NORDIC IIHTT: Retinal and choroidal folds in papilledema. Invest. Ophthalmol. Vis. Sci. 56, 5670–5680 (2015)Google Scholar
  4. 4.
    Wang, J.K., Kardon, R.H., Kupersmith, M.J., Garvin, M.K.: Automated quantification of volumetric optic disc swelling in papilledema using spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53(7), 4069–4075 (2012)CrossRefGoogle Scholar
  5. 5.
    Batool, N., Chellappa, R.: Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints. Pattern Recogn. 48(3), 642–658 (2015)CrossRefGoogle Scholar
  6. 6.
    Püspöki, Z., Storath, M., Sage, D., Unser, M.: Transforms and operators for directional bioimage analysis: a survey. In: De Vos, W.H., Munck, S., Timmermans, J.-P. (eds.) Focus on Bio-Image Informatics. AAECB, vol. 219, pp. 69–93. Springer, Cham (2016). doi: 10.1007/978-3-319-28549-8_3 CrossRefGoogle Scholar
  7. 7.
    Rezakhaniha, R., Agianniotis, A., Schrauwen, J., Griffa, A., Sage, D., Bouten, C., van de Vosse, F., Unser, M., Stergiopulos, N.: Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomech. Model. Mechanobiol. 11(3–4), 461–473 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringThe University of IowaIowa CityUSA
  2. 2.Iowa City VA Health Care SystemIowa CityUSA
  3. 3.Department of Ophthalmology and Visual SciencesThe University of IowaIowa CityUSA

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