TY - GEN
T1 - Optic disc detection via deep learning in fundus images
AU - Xu, Peiyuan
AU - Wan, Cheng
AU - Cheng, Jun
AU - Niu, Di
AU - Liu, Jiang
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In order to realize the localization of optic disc (OD) effectively, a new end-to-end approach based on CNN was proposed in this paper. CNN is a revolutionary network structure which has shown its power in fields of computer vision like classification, object detection and segmentation. We intend to make use of CNN in the study of fundus images. Firstly, we use a basic CNN on which specialized layers are trained to find the pixels probably in OD region. Then we sort out candidate pixels furtherly via threshold. By calculating the center of gravity of these pixels, the location of OD is finally determined. The method has been tested on three databases including ORIGA, MESSIDOR and STARE. In totally 1240 images to be tested, the OD of 1193 are successfully located with the rate of 96.2%. Besides the accuracy, the time cost is another advantage. It takes only 0.93 s to test one image on average in STARE and 0.51 s in MESSIDOR.
AB - In order to realize the localization of optic disc (OD) effectively, a new end-to-end approach based on CNN was proposed in this paper. CNN is a revolutionary network structure which has shown its power in fields of computer vision like classification, object detection and segmentation. We intend to make use of CNN in the study of fundus images. Firstly, we use a basic CNN on which specialized layers are trained to find the pixels probably in OD region. Then we sort out candidate pixels furtherly via threshold. By calculating the center of gravity of these pixels, the location of OD is finally determined. The method has been tested on three databases including ORIGA, MESSIDOR and STARE. In totally 1240 images to be tested, the OD of 1193 are successfully located with the rate of 96.2%. Besides the accuracy, the time cost is another advantage. It takes only 0.93 s to test one image on average in STARE and 0.51 s in MESSIDOR.
KW - Convolution neural networks
KW - Optic disc localization
KW - Retinal fundus image
UR - http://www.scopus.com/inward/record.url?scp=85029818624&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67561-9_15
DO - 10.1007/978-3-319-67561-9_15
M3 - Conference contribution
AN - SCOPUS:85029818624
SN - 9783319675602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 141
BT - Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Cardoso, M. Jorge
A2 - Arbel, Tal
PB - Springer Verlag
T2 - International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -