A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images

Ahmed Eltanboly, Marwa Ismail, Ahmed Shalaby, Andy Switala, Ayman El-Baz, Shlomit Schaal, Georgy Gimel’farb, Magdi El-Azab

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Purpose: Detection (diagnosis) of diabetic retinopathy (DR) in optical coherence tomography (OCT) images for patients with type 2 diabetes, but almost clinically normal retina appearances. Methods: The proposed computer-aided diagnostic (CAD) system detects the DR in three steps: (a) localizing and segmenting 12 distinct retinal layers on the OCT image; (b) deriving features of the segmented layers, and (c) learning most discriminative features and classifying each subject as normal or diabetic. To localise and segment the retinal layers, signals (intensities) of the OCT image are described with a joint Markov-Gibbs random field (MGRF) model of intensities and shape descriptors. Each segmented layer is characterized with cumulative probability distribution functions (CDF) of its locally extracted features, such as reflectivity, curvature, and thickness. A multistage deep fusion classification network (DFCN) with a stack of non-negativity-constrained autoencoders (NCAE) is trained to select the most discriminative retinal layers’ features and use their CDFs for detecting the DR. A training atlas was built using the OCT scans for 12 normal subjects and their maps of layers hand-drawn by retina experts. Results: Preliminary experiments on 52 clinical OCT scans (26 normal and 26 with early-stage DR, balanced between 40–79 yr old males and females; 40 training and 12 test subjects) gave the DR detection accuracy, sensitivity, and specificity of 92%; 83%, and 100%, respectively. The 100% accuracy, sensitivity, and specificity have been obtained in the leave-one-out cross-validation test for all the 52 subjects. Conclusion: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer-assisted diagnostic system for early DR detection using the OCT retinal images.

Original languageEnglish (US)
Pages (from-to)914-923
Number of pages10
JournalMedical Physics
Issue number3
StatePublished - Mar 2017


  • Markov–Gibbs random field (MGRF)
  • diabetic retinopathy (DR)
  • joint image-region-map model
  • non-negativity-constrained autoencoder (NCAE)
  • optical coherence tomography (OCT)

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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