CASE PRESENTATION: A 33-year-old Malay man with underlying acquired immunodeficiency syndrome receiving highly active antiretroviral therapy was referred to the Opthalmology Department with complaints of blurry vision for the past 2 months. He had tested positive for and been diagnosed with COVID-19 1 month previously. Clinical examination of the fundus revealed extensive perivascular sheathing of both the artery and vein suggestive of FBA in the right eye. Laboratory testing of nasal swabs for COVID-19 polymerase chain reaction (PCR) and serum CMV antibody were positive. The patient was then admitted to the COVID-19 ward and treated with intravenous ganciclovir.

METHODS: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class.

RESULTS: Our experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation.