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

Computer vision and image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by...

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Bibliographic Details
Published in:arXiv.org 2020-04
Main Authors: Tavakoli, Meysam, Mahdieh Nazar, Golestaneh, Alireza, Kalantari, Faraz
Format: Article
Language:English
Subjects:
Algorithms
Automation
Blood vessels
Color imagery
Computer vision
Diabetic retinopathy
Diameters
Ground truth
Image analysis
Image detection
Image processing
Image segmentation
Iterative methods
Lesions
Matched filters
Mathematical morphology
Physicians
Retina
Retinal images
Sensors
Workload
Online Access:Get full text
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Summary:Computer vision and image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by using computer vision and image processing approaches in a successful manner. Optic Nerve Head (ONH) detection is a crucial step in retinal image analysis algorithms. The goal of ONH detection is to find and detect other retinal landmarks and lesions and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector, Canny edge detector, and Matched filter edge detector for detection of the ONH either in the normal fundus images or in the presence of retinal lesions (e.g. diabetic retinopathy). To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth data collected by ophthalmologists on retinal images belonging to a test set of 120 images. As shown in the results section, by using the Laplacian-of-Gaussian vessel segmentation, our automated algorithm finds 18 ONHs in true location for 20 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 in true location for 20 images in the CHASE-DB database and 32 out of 40 images in the DRIVE database. And lastly, using the matched filter in the vessel segmentation, our algorithm finds 19 ONHs in true location for 20 images in the CHASE-DB database and all images in the DRIVE.
ISSN:2331-8422