Glaucoma Detection by Segmenting the Super Pixels from Fundus Colour Retinal Images

Glaucoma is an eye disorder which is caused due to irreversible, progressive damage of optic nerve that leads to loss of vision. Often in early stage, people are not able to realize that they are affected by glaucoma because there will no symptom like pain or sudden loss of vision. Glaucoma is a non-curable disease and hence early detection of glaucoma is very essential. This paper proposes an automated image processing approach for detection of glaucoma which may be a diagnostic tool to help ophthalmologist in mass screening of glaucoma suspects. The proposed approach is based on the segmentation of optic disk and the optic cup and computing the cup-to-disc ratio. For segmentation of optic cup and optic disk, a double threshold method is used, one for removing blood vessels and background and second threshold for segmenting the super intensity pixels contained by the optic disk and optic cup. Further, Hough Transform is used to calculate the radius of optic disk and optic cup. The vertical cup to disk ratio is used as a parameter for identification of glaucoma symptoms in the fundus image. The results of the proposed method indicate that the approach is effective in glaucoma detection with better accuracy over existing methods.