@inproceedings{c1ca7de2c34a457ea5ea07745966d65e,
title = "Graph based optic nerve head segmentation",
abstract = "In this paper, we propose a fast and accurate method to locate and segment ONH boundary. This method integrates both the gradient and the local intensity cues to locate the ONH. It formulates the ONH segmentation problem into tracing an optimal path under graph theory framework. The optimal graph search technic considers both local and global information and makes our algorithm work well under poor ONH border contrast. Experiments over two public datasets show that the proposed method can accurate segment ONH boundary under blurred border or different pathological lesions. The average automatic ONH segmentation accuracies are 88.7% for MESSIDOR dataset and 88.6% for ARIA dataset. It can reach 90.2% for MESSIDOR dataset and 91.0% for ARIA dataset if use manual determined center.",
author = "Qing Nie and Leyuan Fang and Huiqi Li and Fei Gao",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014 ; Conference date: 09-06-2014 Through 11-06-2014",
year = "2014",
month = oct,
day = "20",
doi = "10.1109/ICIEA.2014.6931311",
language = "English",
series = "Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1010--1015",
booktitle = "Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014",
address = "United States",
}