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Computer vision and image processing techniques provide important assistanceto physicians and relieve their workload in different tasks. In particular,identifying objects of interest such as lesions and anatomical structures fromthe image is a challenging and iterative process that can be done by usingcomputer vision and image processing approaches in a successful manner. OpticNerve Head (ONH) detection is a crucial step in retinal image analysisalgorithms. The goal of ONH detection is to find and detect other retinallandmarks and lesions and their corresponding diameters, to use as a lengthreference to measure objects in the retina. The objective of this study is toapply three retinal vessel segmentation methods, Laplacian-of-Gaussian edgedetector, Canny edge detector, and Matched filter edge detector for detectionof the ONH either in the normal fundus images or in the presence of retinallesions (e.g. diabetic retinopathy). To evaluate the accuracy of our proposedmethod, we compare the output of our proposed method with the ground truth datacollected by ophthalmologists on retinal images belonging to a test set of 120images. As shown in the results section, by using the Laplacian-of-Gaussianvessel segmentation, our automated algorithm finds 18 ONHs in true location for20 color images in the CHASE-DB database and all images in the DRIVE database.For the Canny vessel segmentation, our automated algorithm finds 16 ONHs intrue location for 20 images in the CHASE-DB database and 32 out of 40 images inthe DRIVE database. And lastly, using the matched filter in the vesselsegmentation, our algorithm finds 19 ONHs in true location for 20 images in theCHASE-DB database and all images in the DRIVE.

Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology