DIAGNOSIS OF GLAUCOMA USING SUPERPIXEL CLASSIFICATION METHOD

Abstract

Glaucoma is a global health problem expected to affect millions of people in world wide. Glaucoma is a chronic eye disease of the optic nerve and a leading cause of blindness and vision loss in worldwide. If glaucoma is not diagnosed and indulgence in time, it can steps forward to loss of vision and even blindness. Now a days several methods is used to detect and assessment of glaucoma such as intraocular pressure (IOP), abnormal visual field and assessment of damaged optic nerve head. The intraocular pressure measurement is performed using non-contact tonometry, but it is not sensitive for population based glaucoma screening. The assessment of abnormal visual field is performed by functional test through special equipment, but it is only present in territory hospitals and therefore unsuitable for screening. The Optic nerve head assessment can be done by a trained professional.   So to avoid these problems a new method is proposed for screening glaucoma using super pixel classification. The proposed system performs optic disc and optic cup segmentation. It uses the 2D fundus images. In optic disc segmentation, clustering algorithms are used to sort each superpixel as disc or non-disc, where as in optic cup segmentation the apart the clustering algorithms, gabor filter is also incorporated into the feature space to enhance the performance. The proposed method have been assessed based on the area of the optic disc and optic cup. 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. A larger CDR indicates a higher risk of glaucoma. The proposed work is to be carried out using Matlab technical computing languag