Identification and Detection of Glaucoma Using Image Segmentation Techniques

Glaucoma is a retinal disease by increase in extra fluid in front of eye increasing the pressure in eye leading to total or complete blindness. The gangalion cells in retinal is affected due to increase in eye pressure leads to total or complete blindness. If not treated on time, one can go blind for lifetime. This is mostly found in population with age above 40 years. Glaucoma cannot be cured, but early detection of Glaucoma and proper medication can stop the problem. The proposed work represents different operators of segmentation of image processing method to find earlier detection of glaucoma. Preprocessing methods such as filtering, and image segmentation is used in the proposed work. The various operators of segmentation are sobel, canny, prewitt and Robert. The entropy values for set of 40 images using above operators is evaluated and the operator with highest entropy values gives optimal result. Manual determination and examination of ophthalmic images is time taking and tedious work. Automatic determination and examination of retinal or eye images gives accurate assessment. It aims at determination, diagnosis, and prevention of problems related to glaucoma. The segmentation operators used in the present paper can obtain the best operator that provides optimal result.