TY - GEN
T1 - Integrating holistic and local deep features for glaucoma classification
AU - Li, Annan
AU - Cheng, Jun
AU - Wong, Damon Wing Kee
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - Automated glaucoma detection is an important application of retinal image analysis. Compared with segmentation based approaches, image classification based approaches have a potential of better performance. However, it still remains a challenging problem for two reasons. Firstly, due to insufficient sample size, learning effective features is difficult. Secondly, the shape variations of optic disc introduce misalignment. To address these problem, a new classification based approach for glaucoma detection is proposed, in which deep convolutional networks derived from large-scale generic dataset is used to representing the visual appearance and holistic and local features are combined to mitigate the influence of misalignment. The proposed method achieves an area under the receiver operating characteristic curve of 0.8384 on the Origa dataset, which clearly demonstrates its effectiveness.
AB - Automated glaucoma detection is an important application of retinal image analysis. Compared with segmentation based approaches, image classification based approaches have a potential of better performance. However, it still remains a challenging problem for two reasons. Firstly, due to insufficient sample size, learning effective features is difficult. Secondly, the shape variations of optic disc introduce misalignment. To address these problem, a new classification based approach for glaucoma detection is proposed, in which deep convolutional networks derived from large-scale generic dataset is used to representing the visual appearance and holistic and local features are combined to mitigate the influence of misalignment. The proposed method achieves an area under the receiver operating characteristic curve of 0.8384 on the Origa dataset, which clearly demonstrates its effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=85009083345&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7590952
DO - 10.1109/EMBC.2016.7590952
M3 - Conference contribution
C2 - 28268570
AN - SCOPUS:85009083345
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1328
EP - 1331
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Y2 - 16 August 2016 through 20 August 2016
ER -