@inproceedings{bca3494f0f0c4c129f50d0855aa22754,
title = "Automatic notch detection in retinal images",
abstract = "This paper presents a new method to detect notching in the optic cup using retinal images. Optic cup notching is an important feature in differentiating normal from glaucomatous eyes. The proposed notching detection method comprises four steps: disc and vessel segmentation, vessel bend detection at key regions, feature points selection and automatic classification. The key step of vessel bend detection involves computing the local curvature of the vessels, then ranking them based on the angle of vessel bend and the local gradient in the neighborhood region. The algorithm was tested on a set of color fundus images and achieved a notching detection rate of 88.9%, a false alarm rate of 4.0%, and an overall accuracy of 95.4%.",
keywords = "glaucoma, notch detection, optic cup, retina, vessel curvature",
author = "Tan, {Mei Hui} and Ying Sun and Ong, {Sim Heng} and Jiang Liu and Mani Baskaran and Tin Aung and Wong, {Tien Yin}",
note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 ; Conference date: 07-04-2013 Through 11-04-2013",
year = "2013",
doi = "10.1109/ISBI.2013.6556805",
language = "English",
isbn = "9781467364546",
series = "Proceedings - International Symposium on Biomedical Imaging",
pages = "1440--1443",
booktitle = "ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging",
}