Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Z., Lee, B.H., Liu, J., Wong, D.W.K., et al.: Optic disc region of interest localization in fundus image for Glaucoma detection in ARGALI. In: Industrial Electronics & Applications, pp. 1686–1689 (2010)
Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.W.K., Tan, N.M., Tao, D., Cheng, C.Y., Aung, T., Wong, T.Y.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. on Med. Imaging 32(6), 1019–1032 (2013)
Boyce, J.F., Cook, H.L., et al.: Automated location of the optic disk, fovea, and retinal blood vessels from digital color fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999)
Barrett, S.F., Naess, E., Molvik, T.: Employing the hough transform to locate the optic disk. Biomed. Sci. Instrum. 37(1), 81–86 (2001)
Haar, F.T.: Automatic localization of the optic disc in digital color images of the human retina. Utrecht University, Utrecht (2005)
Osareh, A.: Automated identification of diabetic retinal exudates and the optic disc. University of Bristol, Bristol (2004)
Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disk detection using pyramidal decomposition and Hausdorff based template matching. IEEE Trans. Med. Imaging 20(11), 1193–1200 (2001)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Science (2014)
Li, H., Chutatape, O.: Automatic location of optic disk in retinal images. In: International Conference on Image Processing, vol. 2, pp. 837–840 (2001)
Klein, R., Klein, B., Moss, S., Davis, M., et al.: The Wisconsin epidemiologic study of diabetic retinopathy II. Arch. Ophthalmol. 102(4), 520–526 (1984)
Zhang, Z., Yin, F., Liu, J., Wong, D.W.K., Tan, N.M., Lee, B.H., Cheng, J., Wong, T.Y.: Origa-light: an online retinal fundus image database for glaucoma analysis and research. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3065–3068 (2010)
Decencière, E., et al.: Feedback on a publicly distributed database: the Messidor database. Image Anal. Stereology 33(3), 231–234 (2014)
Hoover, A.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Yu, H., Barriga, E.S., Agurto, C., et al.: Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans. Inf. Technol. Biomed. 16(4), 644–657 (2012)
Alghamdi, H., Tang, H., Waheeb, S., Peto, T.: Automatic optic disc abnormality detection in fundus images: a deep learning approach. In: OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, Iowa Research Online, pp. 17–24 (2016)
Zubair, M., Yamin, A., Khan, S.A.: Automated detection of optic disc for the analysis of retina using color fundus image. In: IEEE International Conference on Imaging Systems and Techniques, Beijing (2013). doi:10.1109/IST.2013.6729698
Foracchia, M., Grisan, E., Ruggeri, A.: Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans. Med. Imaging 23(10), 1189–1195 (2004)
Zhang, D.B., Yi, Y., Zhao, Y.Y.: Projection based optic disc detection method for retinal fundus images. Chin. J. Biomed. Eng. 32(4), 477–483 (2013)
Anastasi, M., Lodato, G., Cillino, S.: VECPs and optic disc damage in diabetes. Doc. Ophthalmol. Adv. Ophthalmol. 66(4), 331–336 (1987)
Artes, P.H., Chauhan, B.C.: Longitudinal changes in the visual field and optic disc in glaucoma. Prog. Retinal Eye Re. 24(3), 333 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xu, P., Wan, C., Cheng, J., Niu, D., Liu, J. (2017). Optic Disc Detection via Deep Learning in Fundus Images. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-67561-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67560-2
Online ISBN: 978-3-319-67561-9
eBook Packages: Computer ScienceComputer Science (R0)