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Four Models for Automatic Recognition of Left and Right Eye in Fundus Images

  • Xin Lai
  • Xirong Li
  • Rui Qian
  • Dayong Ding
  • Jun Wu
  • Jieping XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

Fundus image analysis is crucial for eye condition screening and diagnosis and consequently personalized health management in a long term. This paper targets at left and right eye recognition, a basic module for fundus image analysis. We study how to automatically assign left-eye/right-eye labels to fundus images of posterior pole. For this under-explored task, four models are developed. Two of them are based on optic disc localization, using extremely simple max intensity and more advanced Faster R-CNN, respectively. The other two models require no localization, but perform holistic image classification using classical Local Binary Patterns (LBP) features and fine-tuned ResNet-18, respectively. The four models are tested on a real-world set of 1,633 fundus images from 834 subjects. Fine-tuned ResNet-18 has the highest accuracy of 0.9847. Interestingly, the LBP based model, with the trick of left-right contrastive classification, performs closely to the deep model, with an accuracy of 0.9718.

Keywords

Medical image analysis Fundus images Left and right eye recognition Optic disc localization Left-right contrastive classification Deep learning 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61672523), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG19).

References

  1. 1.
    Cassin, B., Solomon, S.: Dictionary of Eye Terminology. Triad Publishing Company, Gainesville (1990)Google Scholar
  2. 2.
    Gamm, D.M., Albert, D.M.: Blind spot (2011). https://www.britannica.com/science/blind-spot. Accessed 30 July 2018
  3. 3.
    Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRefGoogle Scholar
  4. 4.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the CVPR (2016)Google Scholar
  5. 5.
    Kaggle: Diabetic retinopathy detection (2015). https://www.kaggle.com/c/diabetic-retinopathy-detection
  6. 6.
    Oinonen, H., Forsvik, H., Ruusuvuori, P., Yli-Harja, O., Voipio, V., Huttunen, H.: Identity verification based on vessel matching from fundus images. In: Proceedings of the ICIP (2010)Google Scholar
  7. 7.
    Ojala, T., PietikaÈinen, M., MaÈenpaÈaÈ, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. T-PAMI 24(7), 971–987 (2002)CrossRefGoogle Scholar
  8. 8.
    Orlando, J., Prokofyeva, E., del Fresno, M., Blaschko, M.: Convolutional neural network transfer for automated glaucoma identification. In: Proceedings of the ISMIPA (2017)Google Scholar
  9. 9.
    Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)Google Scholar
  10. 10.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Pittaras, N., Markatopoulou, F., Mezaris, V., Patras, I.: Comparison of fine-tuning and extension strategies for deep convolutional neural networks. In: Proceedings of the MMM (2017)Google Scholar
  12. 12.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. T-PAMI 39, 1137–1149 (2017)CrossRefGoogle Scholar
  13. 13.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Tan, N.M., et al.: Automatic detection of left and right eye in retinal fundus images. In: Lim, C.T., Goh, J.C.H. (eds.) ICBME 2009, vol. 23, pp. 610–614. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-92841-6_150CrossRefGoogle Scholar
  15. 15.
    Tan, N.M., et al.: Classification of left and right eye retinal images. In: Proceedings of the SPIE (2010)Google Scholar
  16. 16.
    van der Walt, S., et al.: the scikit-image contributors: scikit-Image: image processing in Python. PeerJ 2, e453 (2014)CrossRefGoogle Scholar
  17. 17.
    Wei, Q., Li, X., Wang, H., Ding, D., Yu, W., Chen, Y.: Laser scar detection in fundus images using convolutional neural networks. In: Proceedings of the ACCV (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xin Lai
    • 1
    • 2
  • Xirong Li
    • 1
  • Rui Qian
    • 1
  • Dayong Ding
    • 2
  • Jun Wu
    • 3
  • Jieping Xu
    • 1
    Email author
  1. 1.Key Lab of DEKERemin University of ChinaBeijingChina
  2. 2.Vistel AI LabBeijingChina
  3. 3.Northwestern Polytechnical UniversityXi’anChina

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