Frangi-Net

  • Weilin Fu
  • Katharina Breininger
  • Roman Schaffert
  • Nishant Ravikumar
  • Tobias Würfl
  • Jim Fujimoto
  • Eric Moult
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network (“Frangi-Net”), and illustrate that the Frangi-Net is equivalent to the original Frangi filter. Furthermore, we show that, as a neural network, Frangi-Net is trainable. We evaluate the proposed method on a set of 45 high resolution fundus images. After fine-tuning, we observe both qualitative and quantitative improvements in the segmentation quality compared to the original Frangi measure, with an increase up to 17% in F1 score.

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Literatur

  1. 1.
    Maji D, Santara A, Mitra P, et al. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images. arXiv. 2016.Google Scholar
  2. 2.
    Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Comput Surv. 2004;36(2):81–121.Google Scholar
  3. 3.
    Frangi AF, Niessen WJ, Vincken KL, et al.; Springer. Multiscale vessel enhancement filtering. Proc MICCAI. 1998; p. 130–137.Google Scholar
  4. 4.
    Budai A, Bock R, Maier A, et al. Robust vessel segmentation in fundus images. Int J Biomed Imaging. 2013.Google Scholar
  5. 5.
    Maji D, Santara A, Ghosh S, et al.; IEEE. Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. Proc EMBC. 2015; p. 3029–3032.Google Scholar
  6. 6.
    Tetteh G, Rempfler M, Zimmer C, et al.; Springer. Deep-FExt: deep feature extraction for vessel segmentation and centerline prediction. Int Worksh Mach Learn Med Imaging. 2017; p. 344–352.Google Scholar
  7. 7.
    Würfl T, Ghesu FC, Christlein V, et al.; Springer. Deep learning computed tomography. Proc MICCAI. 2016; p. 432–440.Google Scholar
  8. 8.
    Budai A, Odstrcilik J. High resolution fundus image database; 2013.Google Scholar
  9. 9.
    Yin B, Li H, Sheng B, et al. Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Med Image Anal. 2015;26(1):232–242.Google Scholar
  10. 10.
    Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15(1):29.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Weilin Fu
    • 1
  • Katharina Breininger
    • 1
  • Roman Schaffert
    • 1
  • Nishant Ravikumar
    • 1
  • Tobias Würfl
    • 1
  • Jim Fujimoto
    • 3
  • Eric Moult
    • 3
  • Andreas Maier
    • 1
    • 2
  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander-University Erlangen-NurembergErlangenDeutschland
  2. 2.Erlangen Graduate School in Advanced Optical Technologies (SAOT)ErlangenDeutschland
  3. 3.Department Electrical Engineering and Computer Science and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeUSA

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