Clustering Based Optic Disc and Optic Cup Segmentation for Glaucoma Detection
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Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using Intra Ocular Pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. The method to segment optic disc and optic cup using Simple Linear Iterative Clustering (SLIC) algorithm, K-Means clustering for glaucoma detection are proposed in this work. This method of segmentation is to obtain accurate boundary delineation. In optic disc and cup segmentation, histograms and centre surround statistics are used to classify each super pixel as disc or non-disc, the location information is also included into the feature space to boost the performance. The segmented optic disc and optic cup are then used to compute the Cup to Disc Ratio (CDR) for glaucoma screening. The CDR of the color retinal fundus camera image is the primary identifier to confirm glaucoma for a patient.