Combination of Enhanced Depth Imaging Optical Coherence Tomography and Fundus Images for Glaucoma Screening

  • Zailiang Chen
  • Xianxian Zheng
  • Hailan ShenEmail author
  • Ziyang Zeng
  • Qing Liu
  • Zhuo Li
Image & Signal Processing
Part of the following topical collections:
  1. Advanced Computational Intelligence and Soft Computing in Medical Imaging


Glaucoma is an eye disease that damages the optic nerve and can lead to irreversible loss of peripheral vision gradually and even blindness without treatment. Thus, diagnosing glaucoma in the early stage is essential for treatment. In this paper, an automatic method for early glaucoma screening is proposed. The proposed method combines structural parameters and textural features extracted from enhanced depth imaging optical coherence tomography (EDI-OCT) images and fundus images. The method first segments anterior the lamina cribrosa surface (ALCS) based on region-aware strategy and residual U-Net and then extracts structural features of the lamina cribrosa, such as lamina cribrosa depth and deformation of lamina cribrosa. In fundus images, scanning lines based on disc center and brightness reduction are used for optic disc segmentation and brightness compensation is utilized for segmenting the optic cup. Afterward, the cup-to-disc ratio (CDR) and textural features are extracted from fundus images. Hybrid features are used for training and classification to screen glaucoma by gcForest in the early stage. The proposed method has given exceptional results with 96.88% accuracy and 91.67% sensitivity.


Glaucoma screening EDI-OCT Fundus images Hybrid feature extraction 



This research is supported by the National Natural Science Foundation of China under Grant No. 61672542.

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Bourne, R. R., Stevens, G. A., White, R. A. et al., Causes of vision loss worldwide, 1990-2010: A systematic analysis. Lancet Global Health. 1(6):e339–e349, 2013. Scholar
  2. 2.
    Stevens, G. A., White, R. A., Flaxman, S. R. et al., Global prevalence of vision impairment and blindness: Magnitude and temporal trends, 1990-2010. Ophthalmology. 120(12):2377–2384, 2013. Scholar
  3. 3.
    Tham, Y. C., Li, X., Wong, T. Y. et al., Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology. 121(11):2081, 2014. Scholar
  4. 4.
    Quigley, H. A., and Broman, A. T., The number of people with glaucoma worldwide in 2010 and 2020. British journal of ophthalmology. 90(3):262–267, 2006. Scholar
  5. 5.
    Chen, Z. L., Mo, Y. F., Ouyang, P. B. et al., Retinal vessel optical coherence tomography images for Anemia screening. Medical & Biological Engineering & Computing., 2018. Scholar
  6. 6.
    Chen, Z. L., Li, D. B., Shen, H. L. et al., Automated retinal layer segmentation in OCT images of age-related macular degeneration. IET Image Processing., 2019.
  7. 7.
    Tian, H., Li, L., and Song, F., Study on the deformations of the lamina cribrosa during glaucoma. Acta Biomaterialia. 55:340–348, 2017. Scholar
  8. 8.
    Chien, J. L., Ghassibi, M. P., Mahadeshwar, P. et al., A novel method for assessing Lamina Cribrosa structure ex vivo using anterior segment enhanced depth imaging optical coherence tomography. Journal of Glaucoma. 26(7):626–632, 2017. Scholar
  9. 9.
    Kiumehr, S., Park, S. C., Dorairaj, S. et al., In vivo evaluation of focal lamina cribrosa defects in glaucoma. Archives of ophthalmology. 130(5):552–559, 2012. Scholar
  10. 10.
    Park, H. Y., and Park, C. K., Diagnostic capability of lamina cribrosa thickness by enhanced depth imaging and factors affecting thickness in patients with glaucoma. Ophthalmology. 120(4):745–752, 2013. Scholar
  11. 11.
    Kim, Y. W., Kim, D. W., Jeoung, J. W. et al., Peripheral lamina cribrosa depth in primary open-angle glaucoma: A swept-source optical coherence tomography study of lamina cribrosa. Eye. 29(10):1368–1374, 2015. Scholar
  12. 12.
    Sawada, Y., Hangai, M., Murata, K. et al., Lamina Cribrosa depth variation measured by spectral-domain optical coherence tomography within and between four glaucomatous optic disc phenotypes. Investigative Ophthalmology & Visual Science. 56(10):5777–5784, 2015. Scholar
  13. 13.
    Chen, Z. L., Peng, P., Zou, B. J. et al., Automatic anterior Lamina Cribrosa surface depth measurement based on active contour and energy constraint. Journal of Computer Science and Technology. 32(6):1214–1221, 2017. Scholar
  14. 14.
    Belghith, A., Bowd, C., Medeiros, F. A., et al., Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes? 2015 12th IEEE International Symposium on Biomedical Imaging (ISBI), 2015.
  15. 15.
    Haleem, M. S., Han, L., Hemert, J. et al., A novel adaptive deformable model for automated optic disc and cup segmentation to aid Glaucoma diagnosis. Journal of Medical Systems. 42(20), 2018.
  16. 16.
    Noronha, K. P., Acharya, U., Nayak, K. P. et al., Automated classification of glaucoma stages using higher order cumulant features. Biomedical Signal Processing and Control. 10:174–183, 2014. Scholar
  17. 17.
    Khalil, T., Akram, M. U., Khalid, S. et al., Improved automated detection of glaucoma from fundus image using hybrid structural and textural features. IET Image Processing. 11(9):693–700, 2017. Scholar
  18. 18.
    Issac, A., Sarathi, M. P., and Dutta, M. K., An adaptive threshold based image processing technique for improved glaucoma detection and classification. Computer Methods and Programs in Biomedicine. 122(2):229–244, 2015. Scholar
  19. 19.
    Maheshwari, S., Pachori, R. B., and Acharya, U. R., Automated screening of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE Journal of Biomedical and Health Informatics. 21(3):803–813, 2017. Scholar
  20. 20.
    Soltani, A., Battikh, T., Jabri, I. et al., A new expert system based on fuzzy logic and image processing algorithms for early glaucoma screening. Biomedical Signal Processing & Control. 40:366–377, 2018. Scholar
  21. 21.
    Soorya, M., Issac, A., and Dutta, M. K., An automated and robust image processing algorithm for glaucoma screening from fundus images using novel blood vessel tracking and bend point detection. International Journal of Medical Informatics. 110:52–70, 2018. Scholar
  22. 22.
    Raghavendra, U., Fujita, H., Bhandary, S. V. et al., Deep convolution neural network for accurate diagnosis of Glaucoma using digital fundus images. Information Sciences. 441:41–49, 2018. Scholar
  23. 23.
    Niwas, S. I., Lin, W., Bai, X. et al., Automated anterior segment OCT image analysis for angle closure Glaucoma mechanisms classification. Computer Methods & Programs in Biomedicine. 130:65–75, 2016. Scholar
  24. 24.
    Gopinath, K., Sivaswamy, J., Mansoori, T., Automatic glaucoma assessment from angio-OCT images. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016.
  25. 25.
    Zhou, Z.H., Feng, J., Deep Forest: Towards An Alternative to Deep Neural Networks. 2017 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
  26. 26.
    Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation. 2015 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015. Scholar
  27. 27.
    Drozdzal, M., Vorontsov, E., Chartrand, G., et al., The importance of skip connections in biomedical image segmentation. 2016 2nd Workshop on Deep Learning and Data Labeling for Medical Applications (DLMIA), 2016. Scholar
  28. 28.
    He, K., Zhang, X., Ren, S., et al., Deep residual learning for image recognition. 2016 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  29. 29.
    Mari, J. M., Strouthidis, N. G., Park, S. C. et al., Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation. Investigative Ophthalmology and Visual Science. 54(3):2238–2247, 2013. Scholar
  30. 30.
    Girard, M. J., Tun, T. A., Husain, R. et al., Lamina cribrosa visibility using optical coherence tomography: Comparison of devices and effects of image enhancement techniques. Investigative Ophthalmology and Visual Science. 56(2):865–874, 2015. Scholar
  31. 31.
    Krizhevsky, A., Sutskever, I., Hinton, G., ImageNet Classification with Deep Convolutional Neural Networks. 2012 25th International Conference on Neural Information Processing Systems (NIPS), 2012. Scholar
  32. 32.
    Ojala, T., Pietikainen, M., and Harwood, D., A comparative study of texture measures with classification based on featured distributions. Pattern Recognition. 29(1):51–59, 1996. Scholar
  33. 33.
    Rhodes, L. A., Huisingh, C. E., Quinn, A. E. et al., Comparison of Bruch’s membrane opening-minimum rim width among those with Normal ocular health by race. American Journal of Ophthalmology. 174:113–118, 2017. Scholar
  34. 34.
    Nannini, D. R., Kim, H., Fan, F. et al., Genetic risk score is associated with vertical cup-to-disc ratio and improves prediction of primary open-angle Glaucoma in Latinos. Ophthalmology. 125(6):815–821, 2018. Scholar
  35. 35.
    Eleyan, A., Demirel, H., Co-occurrence based statistical approach for face recognition. 2009 24th IEEE International Symposium on Computer and Information Sciences (ISCIS), 2009.
  36. 36.
    Xian, G. M., An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Systems with Applications. 37(10):6737–6741, 2010. Scholar
  37. 37.
    Gunduz, A., and Principe, J. C., Correntropy as a novel measure for nonlinearity tests. Signal Processing. 89(1):14–23, 2009. Scholar
  38. 38.
    Gilles, J., Empirical wavelet transform. IEEE Transactions on Signal Processing. 61(16):3999–4010, 2013. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringCentral South UniversityChangshaChina
  2. 2.The Second Xiangya Hospital of Central South UniversityChangshaChina

Personalised recommendations