TY - GEN
T1 - Early Diagnosis of Diabetic Retinopathy in OCTA Images Based on Local Analysis of Retinal Blood Vessels and Foveal Avascular Zone
AU - Eladawi, Nabila
AU - Elmogy, Mohammed
AU - Fraiwan, Luay
AU - Pichi, Francesco
AU - Ghazal, Mohammed
AU - Aboelfetouh, Ahmed
AU - Riad, Alaa
AU - Keynton, Robert
AU - Schaal, Shlomit
AU - El-Baz, Ayman
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/26
Y1 - 2018/11/26
N2 - This paper introduces a diagnosis system for detecting early signs of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. We developed a segmentation technique that was able to extract blood vessels from both retinal superficial and deep maps. It is based on a higher order joint Markov-Gibbs random field (MGRF) model, which combines both current and spatial appearance information of retinal blood vessels. To be able to train/test a support vector machine (SVM) classifier, three local features were extracted from the segmented images. These extracted features are the density and appearance of the retinal blood vessels in addition to the distance map of the foveal avascular zone (FAZ). Then, we used SVM with linear kernel to distinguish sub-clinical DR patients from normal cases. By using 105 subjects, the presented computer-aided diagnosis (CAD) system demonstrated an overall accuracy (ACC) of 97.3 % and a Dice similarity coefficient (DSC) of 97.9%.
AB - This paper introduces a diagnosis system for detecting early signs of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. We developed a segmentation technique that was able to extract blood vessels from both retinal superficial and deep maps. It is based on a higher order joint Markov-Gibbs random field (MGRF) model, which combines both current and spatial appearance information of retinal blood vessels. To be able to train/test a support vector machine (SVM) classifier, three local features were extracted from the segmented images. These extracted features are the density and appearance of the retinal blood vessels in addition to the distance map of the foveal avascular zone (FAZ). Then, we used SVM with linear kernel to distinguish sub-clinical DR patients from normal cases. By using 105 subjects, the presented computer-aided diagnosis (CAD) system demonstrated an overall accuracy (ACC) of 97.3 % and a Dice similarity coefficient (DSC) of 97.9%.
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U2 - 10.1109/ICPR.2018.8546250
DO - 10.1109/ICPR.2018.8546250
M3 - Conference contribution
AN - SCOPUS:85059736877
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3886
EP - 3891
BT - 2018 24th International Conference on Pattern Recognition, ICPR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Conference on Pattern Recognition, ICPR 2018
Y2 - 20 August 2018 through 24 August 2018
ER -