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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 134543, 10 pages
http://dx.doi.org/10.1155/2013/134543
Research Article

Analysis of Visual Appearance of Retinal Nerve Fibers in High Resolution Fundus Images: A Study on Normal Subjects

1Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, University of Technology, Technicka 12, 61600 Brno, Czech Republic
2International Clinical Research Center, Center of Biomedical Engineering, St. Anne’s University Hospital Brno, Pekarska 53, 65691 Brno, Czech Republic
3Department of Ophthalmology, University of Erlangen-Nuremberg, Schwabachanlage 6, 91054 Erlangen, Germany
4Pattern Recognition Lab and Erlangen Graduate School of Advanced Optical Technologies, University of Erlangen-Nuremberg, Martensstraße 3, 91058 Erlangen, Germany
5Ophthalmology Clinic of Dr. Tomas Kubena, U Zimniho Stadionu 1759, 760 00 Zlin, Czech Republic

Received 31 May 2013; Accepted 3 October 2013

Academic Editor: Kazuhisa Nishizawa

Copyright © 2013 Radim Kolar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Jan Odstrcilik, Jiri Jan, Radim Kolar, Bernhard Hoeher, and Bernhard Schmauss, “Registration of image sequences from experimental low-cost fundus camera,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8545, pp. 174–183, 2014. View at Publisher · View at Google Scholar
  • Medha V. Wyawahare, and Pradeep M. Patil, “Feature selection and classification for automatic etection of retinal nerve fibre layer thinning in retinal fundus images,” International Journal of Biomedical Engineering and Technology, vol. 19, no. 3, pp. 205–219, 2015. View at Publisher · View at Google Scholar
  • Sang Beom Han, Hee Kyung Yang, Ji Eun Oh, Kwang Gi Kim, and Jeong-Min Hwang, “Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening,” British Journal of Ophthalmology, pp. bjophthalmol-2015-307527, 2016. View at Publisher · View at Google Scholar
  • Dharmanna Lamani, T. C. Manjunath, M. Mahesh, and Y. S. Nijagunarya, “Retinal Nerve Fiber Layer Analysis in Digital Fundus Images: Application to Early Glaucoma Diagnosis,” Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, pp. 69–79, 2016. View at Publisher · View at Google Scholar
  • Jose Ignacio Orlando, Elena Prokofyeva, Mariana Del Fresno, and Matthew B. Blaschko, “Convolutional neural network transfer for automated glaucoma identification,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10160, 2017. View at Publisher · View at Google Scholar
  • Rashmi Panda, N.B. Puhan, Aparna Rao, Debananda Padhy, and Ganapati Panda, “Automated retinal nerve fiber layer defect detection using fundus imaging in glaucoma,” Computerized Medical Imaging and Graphics, vol. 66, pp. 56–65, 2018. View at Publisher · View at Google Scholar
  • Rashmi Panda, Niladri B. Puhan, Aparna Rao, Bappaditya Mandal, Debananda Padhy, and Ganapati Panda, “Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma,” Journal of Medical Imaging, vol. 5, no. 04, pp. 1, 2018. View at Publisher · View at Google Scholar
  • Neha Gour, and Pritee Khanna, “Automated Glaucoma Detection using GIST and Pyramid Histogram of Oriented Gradients (PHOG) descriptors,” Pattern Recognition Letters, 2019. View at Publisher · View at Google Scholar