TY - GEN
T1 - Automatic localization of optic disc using modified U-Net
AU - Gu, Zaiwang
AU - Jiang, Shanshan
AU - Lee, Jimmy
AU - Xie, Jianyang
AU - Cheng, Jun
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2018 Copyright is held by the owner/author(s).
PY - 2018/5/15
Y1 - 2018/5/15
N2 - The optic disc (OD) localization plays an important role in the automatic retinal image analysis for many applications such as glaucoma detection, macular localization, and retinal vessel analysis. In this paper, we propose a method based on U-net and Depth-First-Select Graph to accurately and efficiently locate the optic disc. The adopted U-net architecture is based on ResNet-50, and it predicts the center of OD and produces a probability map. Then based on the probability map, we use the Depth-First-Select algorithm to select the brightest and largest region, which is most likely to be the OD. The proposed method is evaluated on the ORIGA and Messidor dataset. Our experiment shows that the proposed method achieves 100% accuracy in ORIGA and 99.83% accuracy in Messidor. It outperforms other optic disc localization algorithms.
AB - The optic disc (OD) localization plays an important role in the automatic retinal image analysis for many applications such as glaucoma detection, macular localization, and retinal vessel analysis. In this paper, we propose a method based on U-net and Depth-First-Select Graph to accurately and efficiently locate the optic disc. The adopted U-net architecture is based on ResNet-50, and it predicts the center of OD and produces a probability map. Then based on the probability map, we use the Depth-First-Select algorithm to select the brightest and largest region, which is most likely to be the OD. The proposed method is evaluated on the ORIGA and Messidor dataset. Our experiment shows that the proposed method achieves 100% accuracy in ORIGA and 99.83% accuracy in Messidor. It outperforms other optic disc localization algorithms.
KW - Deep learning
KW - Deep-first-select
KW - Retinal image analysis
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85055351067&partnerID=8YFLogxK
U2 - 10.1145/3232651.3232671
DO - 10.1145/3232651.3232671
M3 - Conference contribution
AN - SCOPUS:85055351067
T3 - ACM International Conference Proceeding Series
SP - 79
EP - 83
BT - Proceedings of 2018 International Conference on Control and Computer Vision, ICCCV 2018
PB - Association for Computing Machinery
T2 - 2018 International Conference on Control and Computer Vision, ICCCV 2018
Y2 - 15 June 2018 through 18 June 2018
ER -