Analysis of Human Retinal Images for Automated Glaucoma Screening
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 12
Abstract
Automatic retinal image analysis is emerging and important screening tool for early detection of eye diseases. Glaucoma is chronic & progressive eye disease that damages the optic nerve due to increase in intraocular pressure of eye. It is the second leading cause of blindness globally. Current tests which are used to detect Glaucoma using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. The assessment of Optic nerve head damage in retinal fundus images is more promising and superior. The manual examination of optic disc (OD) is a standard procedure used for detecting glaucoma. In this paper we proposes a system of automatic optic cup and optic disk segmentation using super pixel classification for glaucoma screening. The SLIC (Simple Linear Iterative Clustering) algorithm is incorporated to segment the fundus retinal image into compact and nearly uniform super pixels. It divides an image into a grid of regular pixels, as super pixels have the important property of preserving local boundaries. For optic disk & optic cup segmentation K-means clustering pixel technique, Gabor wavelet transform & thresholding is used. Then segmented optic disc and optic cup are 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.
Authors and Affiliations
Prakash. H. Patil, Seema. V. Kamkhedkar
Keywords
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