TY - GEN
T1 - Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
AU - Jiang, Yuming
AU - Xia, Hu
AU - Xu, Yanwu
AU - Cheng, Jun
AU - Fu, Huazhu
AU - Duan, Lixin
AU - Meng, Zhigang
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Glaucoma is one of the major causes of blindness. Researchers keep looking for better ways to detect glaucoma in its early stage before it gets worse and cannot be cured. Among existing methods, the vertical cup to disc ratio (CDR) has been found to be effective for glaucoma measurement, which is calculated from the diameters of the optic cup and disc regions. Therefore, in order to achieve a more accurate CDR, a good segmentation of the optic disc and cup regions is quite important. Noting that the shape of the disc and cup regions can be assumed to be an ellipse, in this work we propose to find the minimal bounding boxes for the two regions based on the recent advances of deep learning. Also, considering blood vessels, passing through the disc area in a fundus image, can affect the detection of the bounding boxes, we further propose to remove the blood vessels beforehand in order to further boost the overall performance. Comprehensive experiments show that our proposed method achieves state-of-the-art performance on ORIGA-650 for optic disc and cup segmentation.
AB - Glaucoma is one of the major causes of blindness. Researchers keep looking for better ways to detect glaucoma in its early stage before it gets worse and cannot be cured. Among existing methods, the vertical cup to disc ratio (CDR) has been found to be effective for glaucoma measurement, which is calculated from the diameters of the optic cup and disc regions. Therefore, in order to achieve a more accurate CDR, a good segmentation of the optic disc and cup regions is quite important. Noting that the shape of the disc and cup regions can be assumed to be an ellipse, in this work we propose to find the minimal bounding boxes for the two regions based on the recent advances of deep learning. Also, considering blood vessels, passing through the disc area in a fundus image, can affect the detection of the bounding boxes, we further propose to remove the blood vessels beforehand in order to further boost the overall performance. Comprehensive experiments show that our proposed method achieves state-of-the-art performance on ORIGA-650 for optic disc and cup segmentation.
UR - http://www.scopus.com/inward/record.url?scp=85056668444&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512400
DO - 10.1109/EMBC.2018.8512400
M3 - Conference contribution
C2 - 30440527
AN - SCOPUS:85056668444
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 862
EP - 865
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -