June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Spectral-Domain Optical Coherence Tomography Optic-Nerve-Head and Macular En-Face Image Registration in Cases of Papilledema
Author Affiliations & Notes
  • Qingyang Su
    Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, United States
  • Jui-Kai Wang
    Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, United States
  • Mohammad Saleh Miri
    Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, United States
  • Victor A. Robles
    Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, United States
  • Mona K Garvin
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health System, Iowa City, Iowa, United States
    Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Qingyang Su, None; Jui-Kai Wang, None; Mohammad Saleh Miri, None; Victor Robles, None; Mona Garvin, The University of Iowa (P)
  • Footnotes
    Support  U10 EY017281­01A1, U10 EY017387­01A1, 3U10EY01728101A1S1, R01 EY023279, VA-ORD, Iowa City VA Center for The Prevention and Treatment of Visual Loss
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 646. doi:
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    • Get Citation

      Qingyang Su, Jui-Kai Wang, Mohammad Saleh Miri, Victor A. Robles, Mona K Garvin; Spectral-Domain Optical Coherence Tomography Optic-Nerve-Head and Macular En-Face Image Registration in Cases of Papilledema. Invest. Ophthalmol. Vis. Sci. 2017;58(8):646.

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

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Abstract

Purpose : Previously, we proposed an automated method to register fundus images with its corresponding spectral-domain optical coherence tomography (SD-OCT) retinal pigment epithelium (RPE) en-face images in glaucoma (Miri et al. BOEx 2016). In this work, we extended the method to register two SD-OCT RPE en-face images from the optic-nerve-head (ONH) and macular scans in cases of papilledema. This work overcame two current challenges: 1) very limited overlap region between the two en-face images, and 2) frequent appearance of the massive image shadow around the ONH due to papilledema.

Methods : The proposed algorithm, first, searched for corners using the features from the accelerated segment test (FAST) approach in both ONH and macular en-face images. Next, the histograms of oriented gradient (HOG) for each selected corner were computed, and the proposed algorithm decided potential mapping landmarks by identifying the best matches of the feature descriptors (Fig. 1). Then, the proposed algorithm removed the incorrect landmark pairs based on the geometrical distribution of all candidates. Finally, the proposed algorithm utilized random sample consensus (RANSAC) method to estimate a similarity transformation matrix and generated the registered panorama image (Fig. 2).

Results : Fifty subjects were randomly selected from the Idiopathic Intracranial Hypertension Treatment Trial baseline dataset - where 30/20 subjects with 57/39 available OCT ONH and macular image pairs were in the training/testing set, respectively. The parameters in the proposed algorithm were empirically determined using the training set. In the testing set, two manual landmark pairs for each OCT image pair were first selected between the fixed and moving images. To evaluate the registration results, the mean distance of these landmarks shifted by the proposed algorithm in the moving image was calculated. Overall, the mean unsigned difference in the testing set was 1.97±1.00 pixels (59.1±30 µm).

Conclusions : With accurate image registration, quantitative measurements at the region between the ONH and fovea (such as retinal layer thicknesses in grids, wide-field-retina-shape measures, retinal fold quantification) are potentially clinically meaningful in papilledema.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Fig. 1. Examples of HOG Descriptor Computation

Fig. 1. Examples of HOG Descriptor Computation

 

Fig. 2. Example of Landmark Selections and Registration Results

Fig. 2. Example of Landmark Selections and Registration Results

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