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Abstract
Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. At present to detect Glaucoma intraocular pressure (IOP) method is used. It is a fluid pressure inside the eye. The intraocular pressure (IOP) measurement uses tonometry, which sometimes may increases the pressure due to which optic nerve is damaged. Optic nerve head assessment in retinal fundus images is both more promising and superior. This method uses 3D fundus images. 3D images are not easily available and of high cost. So to avoid these problems glaucoma screening using superpixel classification is used. This project proposes glaucoma screening using superpixel classification. It uses the 2D fundus images. In optic disc segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic cup segmentation, in addition to the histograms and centre surround statistics and the location information is also included in the cup segmentation. The proposed segmentation methods have been evaluated with optic disc and optic cup boundaries manually marked by trained professionals. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma for a given patient.
Keywords: Optic disc segmentation, K-Means clustering, Gabor filter, Optic cup segmentation, Glaucoma screening.Â##plugins.themes.academic_pro.article.details##
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