ISSN: 2277-9655 [Pal* et al., 6(12): December, 2017] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com© International Journal of Engineering Sciences & Research Technology [84] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY FUZZY LOGIC BASED OPTICAL DISC LOCALIZATION AND DETECTION OF STEREOSCOPIC RETINAL IMAGES IN NROI Moumita Pal *1 , Ranjana Ray 2 ,Neha Choudhury 3* 1 Assistant. Professor, 2 Assistant.Professor, 3 Student Electronics and Communication Engineering, JIS College Of Engineering,India DOI: 10.5281/zenodo.1086613 ABSTRACT 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 ground- truths 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. KEYWORDS Fuzzy C-Means (FCM), PPV, PLR, sensitivity, specificity, accuracy, Bayes Shrink.) I. INTRODUCTION Current methods of detection and assessment of diabetic retinopathy [4] are manual, expensive and require trained ophthalmologists. Retinal blood vessel [7] morphology can be an important indicator for many diseases such as diabetes, hypertension and arteriosclerosis. The measurement of geometrical changes in veins and arteries can be applied to a variety of clinical studies. Two major problems in the segmentation of retinal blood vessels are the presence of a wide variety of vessel widths and the heterogeneous background of the retina. Retinal images provide considerable information on pathological changes caused by local ocular diseases revealing diabetes, hypertension, arteriosclerosis, cardiovascular disease and stroke. Computer-aided analysis of retinal image plays a central role in diagnostic procedures. However, automatic retinal segmentation is complicated by the fact that retinal images are often streoscopic, poorly contrasted, and the vessel widths can vary from very large to very small value. For this specific reason, in this work the preprocessing step includes adaptive fundus detection, contrast enhancement. Segmentation of blood vessels is a research area, for years. This present work proposes algorithms, which usually use some kind of de-noising and vessel enhancement of before fundus detection or vessel tracking based on adaptive bartlett fundus detection. The methods with high accuracy also have high computational needs, if thick vessels are present. The use of the proposed resolution hierarchy makes it possible to detect these vessels faster, while preserving a high accuracy. There are three basic approaches for automated segmentation of blood vessels [8]: fundus detection method, tracking method and machine trained classifiers. In the first method, many different operators are used to enhance the contrast between vessel and background, such as Sobel operators, Laplacian operators, Gaussian filters modeling the gray cross-section of blood vessel. Then the gray threshold is selected to determine the vessel. This gray threshold is crucial, because small threshold induces more noise and gray threshold causes loss of some fine vessels, adaptive or local threshold is used. Vessel tracking is another technique for vessel