13 March 2017 An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images
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Abstract
The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.
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Maziyar M. Khansari, Maziyar M. Khansari, William O’Neill, William O’Neill, Richard Penn, Richard Penn, Norman P. Blair, Norman P. Blair, Felix Chau, Felix Chau, Mahnaz Shahidi, Mahnaz Shahidi, } "An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101372C (13 March 2017); doi: 10.1117/12.2254674; https://doi.org/10.1117/12.2254674
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