Abstract
Glaucoma is the second leading cause of irreversible vision loss. Early diagnosis and treatment can, however, slow the progression of the disease. Specialists making this diagnosis rely on several tests and examinations such as visual field tests and examinations of retinal images and optical coherence tomography images. One of the regions examined by specialists when checking for retinal conditions is the optic nerve head region, which is the brightest region in retinal images. Within this region, the ratio between the cup and the disc can be used when diagnosing for glaucoma. Calculating the cup–disc ratio requires the segmentation of both the disc and the cup from retinal images. In a previous paper, a method for segmenting the disc was proposed. Here another deep learning model, H-OCS, is proposed for segmenting the cup from retinal images. A customized InceptionV3 model with transfer learning and image augmentation is used. Additionally, the output of H-OCS is refined and enhanced using a series of post-processing steps. H-OCS is tested on six publicly available datasets: RimOneV3, Drishti, Messidor, Refuge, Riga, and Magrebia and several ablation studies are conducted to evaluate the effectiveness of the proposed approach. Additionally, the performance of H-OCS is compare with other studies. An overall average accuracy of 97.86%, DC of 88.37%, Sensitivity of 89.09% and IoU of 79.66% was achieved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, R., et al.: Optic disc and cup segmentation through fuzzy broad learning system for glaucoma screening. IEEE Trans. Industr. Inform. 17(4), 2476–2487 (2020)
Almazroa, A., et al.: Retinal fundus images for glaucoma analysis: the RIGA dataset. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, vol. 10579, p. 105790B. International Society for Optics and Photonics (2018)
Huazhu, F., Cheng, J., Yanwu, X., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018)
Fu, H., et al.: Segmentation and quantification for angle-closure glaucoma assessment in anterior segment OCT. IEEE Trans. Med. Imaging 36(9), 1930–1938 (2017)
Halır, R., Flusser, J.: Numerically stable direct least squares fitting of ellipses. In: Proceedings of 6th International Conference in Central Europe on Computer Graphics and Visualization. WSCG, vol. 98, pp. 125–132. Citeseer (1998)
Orlando, J.I., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)
Panda, R., Puhan, N.B., Mandal, B., Panda, G.: Glauconet: patch-based residual deep learning network for optic disc and cup segmentation towards glaucoma assessment. SN Comput. Sci. 2(2), 1–17 (2021)
Pena-Betancor, C., et al.: Estimation of the relative amount of hemoglobin in the cup and neuroretinal rim using stereoscopic color fundus images. Investig. Ophthalmol. Vis. Sci. 56(3), 1562–1568 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Sarhan, A., et al.: Utilizing transfer learning and a customized loss function for optic disc segmentation from retinal images. arXiv preprint arXiv:2010.00583 (2020)
Sarhan, A., Rokne, J., Alhajj, R.: Glaucoma detection using image processing techniques: a literature review. Comput. Med. Imaging Graph. 78, 101657 (2019)
Sarhan, A., Rokne, J., Alhajj, R., Crichton, A.: Transfer learning through weighted loss function and group normalization for vessel segmentation from retinal images. arXiv preprint arXiv:2012.09250 (2020)
Sevastopolsky, A., Drapak, S., Kiselev, K., Snyder, B.M., Keenan, J.D., Georgievskaya, A.: Stack-u-net: refinement network for image segmentation on the example of optic disc and cup. arXiv preprint arXiv:1804.11294 (2018)
Shankaranarayana, S.M., Ram, K., Mitra, K., Sivaprakasam, M.: Fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation. IEEE J. Biomed. Health Inform. 23(4), 1417–1426 (2019)
Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., Jain, M., Tabish, A.U.S.: Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: International Symposium on Biomedical Imaging (ISBI), pp. 53–56. IEEE (2014)
Wang, S., Lequan, Yu., Yang, X., Chi-Wing, F., Heng, P.-A.: Patch-based output space adversarial learning for joint optic disc and cup segmentation. IEEE Trans. Med. Imaging 38(11), 2485–2495 (2019)
Shuang, Yu., Xiao, D., Frost, S., Kanagasingam, Y.: Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput. Med. Imaging Graph. 74, 61–71 (2019)
Zhao, X., Wang, S., Zhao, J., Wei, H., Xiao, M., Ta, N.: Application of an attention U-Net incorporating transfer learning for optic disc and cup segmentation. Signal Image Video Process. 15(5), 913–921 (2021)
Zilly, J., Buhmann, J.M., Mahapatra, D.: Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput. Med. Imaging Graph. 55, 28–41 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sarhan, A., Rokne, J., Alhajj, R. (2021). H-OCS: A Hybrid Optic Cup Segmentation of Retinal Images. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-89128-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89127-5
Online ISBN: 978-3-030-89128-2
eBook Packages: Computer ScienceComputer Science (R0)