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We propose an algorithm for the detection of retinal landmarks (optic nerve head or optic disc, macula, and vasculature) based on optic cup location and anatomical structural details from diabetic retinopathy (DR) images of both left and right eye. Our algorithm uses color fundus images obtained from mydriatic camera. The algorithm proceeds through four main steps 1. Color image pre-processing- to...
Glaucoma is a leading cause of blindness with permanent damage to optic nerve head. ARGALI is an automated computer-aided diagnosis system designed for glaucoma detection via optic cup-to-disc ratio assessment. It employs several methods to determine the optic cup and disc from retinal images. Optic disc detection and segmentation works have been widely reported with high success rate. However, the...
This paper presents an investigation into different approaches for segmentation-driven retinal image registration. This constitutes an intermediate step towards detecting changes occurring in the topography of blood vessels, which are caused by disease progression. A temporal dataset of retinal images was collected from small animals (i.e. mice). The perceived low quality of the dataset employed favoured...
Glaucoma is the one of the two major causes of blindness, which can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A multimodality fusion approach for neuroretinal cup detection improves the accuracy of the boundary estimation...
Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A Convex Hull based Neuro-Retinal Optic Cup Ellipse Optimization algorithm improves the accuracy of the boundary...
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