Automatic Glaucoma Detection from Stereo Fundus Images.

This paper proposes a new method for automatic detection of glaucoma from stereo pair of fundus images. The basis for detecting glaucoma is using the optic cup-to-disc area ratio, where the surface area of the optic cup is segmented from the disparity map estimated from the stereo fundus image pair. More specifically, we first estimate the disparity map from the stereo image pair. Then, the optic disc is segmented from one of the stereo image. Based upon the location of the optic disc, we perform an active contour segmentation on the disparity map to segment the optic cup. Thereafter, we can compute the optic cup-to-disc area ratio by dividing the area (i.e. the total number of pixels) of the segmented optic cup region to that of the segmented optic disc region. Our experimental results using the available test dataset shows the efficacy of our proposed approach.

Optical Coherence Tomography Angiography of Optic Disc and Macula Vessel Density in Glaucoma and Healthy Eyes.

AIM: To study the microvascular density of the macular and optic nerve head in healthy and glaucoma subjects using optical coherence tomography angiography. METHODOLOGY: We performed a cross-sectional cohort study on healthy subjects and patients with glaucoma. The AngioVue Enhanced Microvascular Imaging System was used to capture the optic nerve head and macula images during one visit. En face segment images of the macular and optic disc were studied in layers. Microvascular density of the optic nerve head and macula were quantified by the number of pixels measured by a novel in-house developed software. Areas under the receiver operating characteristic curves (AUROC) were used to determine the accuracy of differentiating between glaucoma and healthy subjects. RESULTS: A total of 24 (32 eyes) glaucoma subjects (57.5±9.5-y old) and 29 (58 eyes) age-matched controls (51.17±13.5-y old) were recruited. Optic disc and macula scans were performed showing a greater mean vessel density (VD) in healthy compared with glaucoma subjects. The control group had higher VD than the glaucoma group at the en face segmented layers of the optic disc (optic nerve head: 0.209±0.05 vs. 0.110±0.048, P<0.001; vitreoretinal interface: 0.086±0.045 vs. 0.052±0.034, P=0.001; radial peripapillary capillary: 0.146±0.040 vs. 0.053±0.036, P<0.001; and choroid: 0.228±0.074 vs. 0.165±0.062, P<0.001). Similarly, the VD at the macula was also greater in controls than glaucoma patients (superficial retina capillary plexus: 0.115±0.016 vs. 0.088±0.027, P<0.001; deep retina capillary plexus: 0.233±0.027 vs. 0.136±0.073, P<0.001; outer retinal capillary plexus: 0.190±0.057 vs. 0.136±0.105, P=0.036; and choriocapillaris: 0.225±0.053 vs. 0.153±0.068, P<0.001. The AUROC was highest for optic disc radial peripapillary capillary (0.96), followed by nerve head (0.92) and optic disc choroid (0.76). At the macula, the AUROC was highest for deep retina (0.86), followed by choroid (0.84), superficial retina (0.81), and outer retina (0.72). CONCLUSIONS: Microvascular density of the optic disc and macula in glaucoma patients was reduced compared with healthy controls. VD of both optic disc and macula had a high diagnostic ability in differentiating healthy and glaucoma eyes.

Glaucoma classification from retina optical coherence tomography angiogram.

This paper presents a new method for classification of retina into glaucoma and non-glaucoma cases based on optical coherence tomography angiogram (OCTA). The key idea here is to analyze the retinal microvasculature in the optic disc area of an enface OCTA for glaucoma classification. To facilitate this analysis, we propose a way to extract a so-called "optic disc microvasculature" region and then propose several features that will be extracted from this microvasculature region. A machine classifier is then trained using the designated features and subsequently used to classify the OCTA data. We show that our proposed approach works well on the tested dataset.