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Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images
Author(s) -
Eladawi Nabila,
Elmogy Mohammed,
Khalifa Fahmi,
Ghazal Mohammed,
Ghazi Nicola,
Aboelfetouh Ahmed,
Riad Alaa,
Sandhu Harpal,
Schaal Shlomit,
ElBaz Ayman
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13142
Subject(s) - artificial intelligence , diabetic retinopathy , cad , optical coherence tomography , segmentation , support vector machine , pattern recognition (psychology) , computer science , sørensen–dice coefficient , optical coherence tomography angiography , image segmentation , computer vision , medicine , radiology , engineering drawing , engineering , diabetes mellitus , endocrinology
Purpose This paper introduces a new computer‐aided diagnosis (CAD) system for detecting early‐stage diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) images. Methods The proposed DR‐CAD system is based on the analysis of new local features that describe both the appearance and retinal structure in OCTA images. It starts with a new segmentation approach that has the ability to extract the blood vessels from superficial and deep retinal OCTA maps. The high capability of our segmentation approach stems from using a joint Markov–Gibbs random field stochastic model integrating a 3D spatial statistical model with a first‐order appearance model of the blood vessels. Following the segmentation step, three new local features are estimated from the segmented vessels and the foveal avascular zone (FAZ): (a) vessels density, (b) blood vessel calibre, and (c) width of the FAZ. To distinguish mild DR patients from normal cases, the estimated three features are used to train and test a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Results On a cohort of 105 subjects, the presented DR‐CAD system demonstrated an overall accuracy (ACC) of 94.3%, a sensitivity of 97.9%, a specificity of 87.0%, the area under the curve (AUC) of 92.4%, and a Dice similarity coefficient (DSC) of 95.8%. This in turn demonstrates the promise of the proposed CAD system as a supplemental tool for early detection of DR. Conclusion We developed a new DR‐CAD system that is capable of diagnosing DR in its early stage. The proposed system is based on extracting three different features from the segmented OCTA images, which reflect the changes in the retinal vasculature network.