Page 120 - Computational Retinal Image Analysis
P. 120
References 113
[15] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, S. Barman,
Blood vessel segmentation methodologies in retinal images—a survey, Comput.
Methods Programs Biomed. 108 (1) (2012) 407–433.
[16] J. Almotiri, K. Elleithy, A. Elleithy, Retinal vessels segmentation techniques and
algorithms: a survey, Appl. Sci. 8 (2) (2018) 1–31.
[17] M. Miri, Z. Amini, H. Rabbani, R. Kafieh, A comprehensive study of retinal vessel
classification methods in fundus images, J. Med. Signals Sens. 7 (2) (2017) 59–70.
[18] O. Faust, U. Acharya, E. Ng, K. Ng, J. Suri, Algorithms for the automated detection of
diabetic retinopathy using digital fundus images: a review, J. Med. Syst. 36 (1) (2012)
145–157.
[19] M. Mookiah, U. Acharya, C. Chua, C. Lim, E. Ng, A. Laude, Computer-aided diagnosis
of diabetic retinopathy: a review, Comput. Biol. Med. 43 (12) (2013) 2136–2155.
[20] Y. Douven, Retina Tracking for Robot-Assisted Vitreoretinal Surgery (Master’s thesis),
Eindhoven University of Technology, 2015.
[21] D. Braun, S. Yang, J. Martel, C. Riviere, B. Becker, EyeSLAM: real-time simultaneous
localization and mapping of retinal vessels during intraocular microsurgery, Int. J. Med.
Robot. 14 (1) (2018) 1–10.
[22] S. Lajevardi, A. Arakala, S. Davis, K. Horadam, Retina verification system based on
biometric graph matching, IEEE Trans. Image Process. 22 (9) (2013) 3625–3635.
[23] Z. Waheed, U. Akram, A. Waheed, M. Khan, A. Shaukat, Person identification using
vascular and non-vascular retinal features, Comput. Electr. Eng. 53 (2016) 359–371.
[24] J. Staal, M. Abramoff, M. Niemeijer, M. Viergever, B. van Ginneken, Ridge-based
vessel segmentation in color images of the retina, IEEE Trans. Med. Imaging 23 (4)
(2004) 501–509.
[25] A. Hoover, V. Kouznetsova, M. Goldbaum, Locating blood vessels in retinal images by
piecewise threshold probing of a matched filter response, IEEE Trans. Med. Imaging 19
(3) (2000) 203–210.
[26] Q. Hu, M. Abramoff, M. Garvin, Automated separation of binary overlapping trees in
low-contrast color retinal images, in: MICCAI, 2013.
[27] B. Dashtbozorg, A. Mendonca, A. Campilho, An automatic graph-based approach for
artery/vein classification in retinal images, IEEE Trans. Image Process. 23 (3) (2014)
1073–1083.
[28] G. Azzopardi, N. Petkov, Automatic detection of vascular bifurcations in segmented
retinal images using trainable COSFIRE filters, Pattern Recogn. Lett. 34 (8) (2013)
922–933.
[29] T. Kohler, A. Budai, M. Kraus, J. Odstrcilik, G. Michelson, J. Hornegger, Automatic
no-reference quality assessment for retinal fundus images using vessel segmentation,
in: IEEE Int. Symp. on Computer-Based Medical Systems, 2013, pp. 95–100.
[30] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, S. Barman,
An ensemble classification-based approach applied to retinal blood vessel segmentation,
IEEE Trans. Biomed. Eng. 59 (9) (2012) 2538–2548.
[31] D. Farnell, F. Hatfield, P. Knox, M. Reakes, S. Spencer, D. Parry, S. Harding,
Enhancement of blood vessels in digital fundus photographs via the application of
multiscale line operators, J. Frankl. Inst. 345 (7) (2008) 748–765.
[32] S. Holm, G. Russell, V. Nourrit, N. McLoughlin, DR HAGIS—a novel fundus image
database for the automatic extraction of retinal surface vessels, SPIE J. Med. Imaging 4
(1) (2017) 1–11.