FUZZY LOGIC BASED OPTICAL DISC LOCALIZATION AND DETECTION OF STEREOSCOPIC RETINAL IMAGES IN NROI

Glaucoma, diabetic retinopathy, and macular degeneration can be identified by segmenting retinal blood vessels. Glaucoma is most frequent and has serious ocular consequences that can even lead to blindness within these diseases. Intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defects are the diagnostic criteria for glaucoma include This form of blood vessel segmentation helps in early detection for ophthalmic diseases, and potentially reduces the risk of blindness. The low-contrast and streoscopic retinal images cannot be used for extraction of blood vessels owing to narrow blood vessels. This present work proposes an algorithm for segmentation of blood vessels from low-contrast; streoscopic retinal images, and compares the results between expert ophthalmologists' hand-drawn groundtruths and segmented image (i.e. the output of the present work). Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that this work segments blood vessels successfully with sensitivity, specificity, PPV, PLR and accuracy of 99.62%, 54.66%, 95.08%, 219.72 and 95.03%, respectively and for the de-noised filtered image the values are 99.56%,50.97%, 94.70%,203.08%,94.60% respectively