REVIEW ON IMAGE PROCESSING TECHNIQUES FOR OPTIC DISC SEGMENTATION

The review paper describes various image processing techniques for optic cup and disc segmentation for automatic detection of glaucoma. Glaucoma is one among major causes of blindness in working population. Early detection of Glaucoma through automated retinal image analysis helps in preventing vision loss. The end-to-end processing pipeline for Glaucoma detection from retinal images includes the detection of optic disc (OD), neuroretinal rim (NRR), and optic cup (OC) segmentation, feature computation from the segmented OD and OC, and estimation of Glaucoma from these features. Optic disc (OD) segmentation from retinal images is the preliminary step in developing the diagnostic tool for early Glaucoma detection. The segmented OD is preprocessed to highlight the NRR and OC area. A multi-layer perceptron with 12-D feature vector is used for pixel classification based OC segmentation. Cup-to-disc ratio and other contextual features are extracted from the segmented OD and OC. Experimental evaluation shows that the proposed methodology can be reliably utilized in screening programs for early glaucoma detection.