Skip to main content
SpringerLink
Log in

Vessel Preserving CNN-Based Image Resampling of Retinal Images

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

Included in the following conference series:

Abstract

High quality resolution enhancement of eye fundus images is an important problem in medical image processing. Retinal images are usually noisy and contain low-contrast details that have to be preserved during upscaling. This makes the development of retinal image resampling algorithm a challenging problem.

The most promising results are achieved with the use of convolutional neural networks (CNN). We choose the popular algorithm SRCNN for general image resampling and investigate the possibility of using this algorithm for retinal image upscaling.

In this paper, we propose a new training scenario for SRCNN with specific preparation of training data and a transfer learning. We demonstrate an improvement of image quality in terms of general purpose image metrics (PSNR, SSIM) and basic edges metrics—the metrics that represent the image quality for strong isolated edges.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kim, W.H., Lee, J.S.: Blind single image super resolution with low computational complexity. Multimed. Tools Appl. 76(5), 7235–7249 (2017)

    Article  Google Scholar 

  2. Li, Z., Huang, F., Zhang, J., Dashtbozorg, B., Abbasi-Sureshjani, S., Sun, Y., Long, X., Yu, Q., ter Haar Romeny, B., Tan, T.: Multi-modal and multi-vendor retina image registration. Biomed. Opt. Express 9(2), 410–422 (2018)

    Article  Google Scholar 

  3. Deng, K., Tian, J., Zheng, J., Zhang, X., Dai, X., Xu, M.: Retinal fundus image registration via vascular structure graph matching. J. Biomed. Imaging 2010, 14 (2010)

    Google Scholar 

  4. Abdel-Hamid, L., El-Rafei, A., El-Ramly, S., Michelson, G.: Performance dependency of retinal image quality assessment algorithms on image resolution: analyses and solutions. Signal Image Video Process. 12(1), 9–16 (2018)

    Article  Google Scholar 

  5. Xu, J., Chutatape, O.: Auto-adjusted 3-d optic disk viewing from low-resolution stereo fundus image. Comput. Biol. Med. 36(9), 921–940 (2006)

    Article  Google Scholar 

  6. Nakagawa, T., Suzuki, T., Hayashi, Y., Mizukusa, Y., Hatanaka, Y., Ishida, K., Hara, T., Fujita, H., Yamamoto, T.: Quantitative depth analysis of optic nerve head using stereo retinal fundus image pair. J. Biomed. Opt. 13(6), 064026 (2008)

    Article  Google Scholar 

  7. Jebadurai, J., Peter, J.D.: Super-resolution of retinal images using multi-kernel SVR for IoT healthcare applications. Future Gener. Comput. Syst. 83, 338–346 (2018)

    Article  Google Scholar 

  8. Xu, X., Ding, W., Wang, X., Cao, R., Zhang, M., Lv, P., Xu, F.: Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics. Sci. Rep. 6, 34603 (2016)

    Article  Google Scholar 

  9. Zhang, L., Wu, X.: An edge-guide image interpolation via directional filtering and data fusion. IEEE Trans. Image Process. 15, 2226–2235 (2006)

    Article  Google Scholar 

  10. Giachetti, A., Asuni, N.: Real time artifact-free image interpolation. IEEE Trans. Image Process. 20(10), 2760–2768 (2011)

    Article  MathSciNet  Google Scholar 

  11. Zhou, D., Shen, X., Dong, W.: Image zooming using directional cubic convolution interpolation. IET Image Process. 6(6), 627–634 (2012)

    Article  MathSciNet  Google Scholar 

  12. Li, X., Orchard, M.: New edge-directed interpolation. IEEE Trans. Image Process. 10, 1521–1527 (2001)

    Article  Google Scholar 

  13. Choi, J.S., Kim, M.: Super-interpolation with edge-orientation based mapping kernels for low complex 2x upscaling. IEEE Trans. Image Process. 25(1), 469–483 (2015)

    Article  Google Scholar 

  14. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  15. Dong, W., Zhang, L., Lukac, R., Shi, G.: Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Trans. Image Process. 22(4), 1382–1394 (2013)

    Article  MathSciNet  Google Scholar 

  16. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13

    Chapter  Google Scholar 

  17. Nasonov, A., Chesnakov, K., Krylov, A.: CNN Based Retinal Image Upscaling Using Zero Component Analysis, pp. 27–31. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2017)

    Article  Google Scholar 

  18. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint: arXiv:1408.5093

  19. Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)

    Article  Google Scholar 

  20. Krizhevsky, A.: Masters Thesis “Learning multiple layers of features from tiny images” (2009). www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf

  21. Nasonov, A., Chesnakov, K., Krylov, A.: Convolutional neural networks based image resampling with noisy training set. In: International Conference on Signal Processing (ICSP2016), Chengdu, China, pp. 62–66 (2016)

    Google Scholar 

  22. Kälviäinen, R., Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis (2007)

    Google Scholar 

  23. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  24. Nasonov, A., Nasonova, A., Krylov, A.: Edge width estimation for defocus map from a single image. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 15–22. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25903-1_2

    Chapter  Google Scholar 

  25. Nasonov, A.V., Krylov, A.S.: Edge quality metrics for image enhancement. Pattern Recogn. Image Anal. 22(1), 346–353 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Mathematical Methods of Image Processing, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia

    Andrey Krylov, Andrey Nasonov, Konstantin Chesnakov & Alexandra Nasonova

  2. Korea Electrotechnology Research Institute, Busan, Korea

    Seung Oh Jin

  3. Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea

    Uk Kang

  4. Department of Biomedical Science and Family Medicine, Seoul National University College of Medicine, Seoul, Korea

    Sang Min Park

Authors
  1. Andrey Krylov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrey Nasonov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Konstantin Chesnakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexandra Nasonova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seung Oh Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Uk Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sang Min Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrey Krylov .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Krylov, A. et al. (2018). Vessel Preserving CNN-Based Image Resampling of Retinal Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_67

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93000-8_67

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation