nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Vessel Preserving CNN-Based Image Resampling of Retinal Images

verfasst von : Andrey Krylov, Andrey Nasonov, Konstantin Chesnakov, Alexandra Nasonova, Seung Oh Jin, Uk Kang, Sang Min Park

Erschienen in: Image Analysis and Recognition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Assessment of Anti-tumor Immune Response in Colorectal Carcinomas from Whole Slide Images

Nächstes Kapitel Diagnosis of Non-Small Cell Lung Cancer Using Phylogenetic Diversity in Radiomics Context

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

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)CrossRef

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)

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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

10.

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

11.

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

12.

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

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)CrossRef

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)MathSciNetCrossRef

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)MathSciNetCrossRef

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_13CrossRef

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)CrossRef

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)CrossRef

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)

22.

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

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)CrossRef

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_2CrossRef

25.

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

Titel: Vessel Preserving CNN-Based Image Resampling of Retinal Images
verfasst von: Andrey Krylov
Andrey Nasonov
Konstantin Chesnakov
Alexandra Nasonova
Seung Oh Jin
Uk Kang
Sang Min Park
Verlag: Springer International Publishing
Buch: Image Analysis and Recognition
Print ISBN: 978-3-319-92999-6

Electronic ISBN: 978-3-319-93000-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93000-8_67

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner