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References 271

[74] J. Kirkpatrick, T. Spencer, A. Manivannan, P. Sharp, J. Forrester, Quantitative image
analysis of macular drusen from fundus photographs and scanning laser ophthalmo-
scope images, Eye 9 (1995) 48.
[75] J.H. Acton, R.P. Cubbidge, H. King, P. Galsworthy, J.M. Gibson, Drusen detection in
retro-mode imaging by a scanning laser ophthalmoscope, Acta Ophthalmol. 89 (2011)
e404–e411.
[76] B. Diniz, R.M. Ribeiro, D.C. Rodger, M. Maia, S. Sadda, Drusen detection by confo-
cal aperture-modulated infrared scanning laser ophthalmoscopy, Br. J. Ophthalmol. 97
(2013) 285–290.
[77] F.G. Schlanitz, C. Ahlers, S. Sacu, C. Schütze, M. Rodriguez, S. Schriefl, I. Golbaz,
T. Spalek, G. Stock, U. Schmidt-Erfurth, Performance of drusen detection by
spectral-domain optical coherence tomography, Invest. Ophthalmol. Vis. Sci. 51 (2010)
6715–6721.
[78] F.G. Schlanitz, B. Baumann, T. Spalek, C. Schütze, C. Ahlers, M. Pircher, E. Götzinger,
C.K. Hitzenberger, U. Schmidt-Erfurth, Performance of automated drusen detection by
polarization-sensitive optical coherence tomography, Invest. Ophthalmol. Vis. Sci. 52
(2011) 4571–4579.
[79] A. Deckert, S. Schmitz-Valckenberg, J. Jorzik, A. Bindewald, F. Holz, U. Mansmann,
Automated analysis of digital fundus autofluorescence images of geographic atrophy in
advanced age-related macular degeneration using confocal scanning laser ophthalmos-
copy (cSLO), BMC Ophthalmol. 5 (2005) 8.
[80] Q. Chen, L. de Sisternes, T. Leng, L.L. Zheng, L. Kutzscher, D.L. Rubin, Semi-automatic
geographic atrophy segmentation for SD-OCT images, Biomed. Opt. Express 4 (2013)
2729–2750.
[81] C. Schütze, M. Bolz, R. Sayegh, B. Baumann, M. Pircher, E. Götzinger,
C.K. Hitzenberger, U. Schmidt-Erfurth, Lesion size detection in geographic atrophy
by polarization-sensitive optical coherence tomography and correlation to conventional
imaging techniques, Invest. Ophthalmol. Vis. Sci. 54 (2013) 739–745.
[82] S.J. Chiu, J.A. Izatt, R.V. O'Connell, K.P. Winter, C.A. Toth, S. Farsiu, Validated au-
tomatic segmentation of AMD pathology including drusen and geographic atrophy in
SD-OCT images, Invest. Ophthalmol. Vis. Sci. 53 (2012) 53–61.
[83] Q. Zhang, C.-L. Chen, Z. Chu, F. Zheng, A. Miller, L. Roisman, J. Rafael de Oliveira
Dias, Z. Yehoshua, K.B. Schaal, W. Feuer, G. Gregori, S. Kubach, L. An, P.F. Stetson,
M.K. Durbin, P.J. Rosenfeld, R.K. Wang, Automated quantitation of choroidal neo-
vascularization: a comparison study between spectral-domain and swept-source OCT
angiograms, Invest. Ophthalmol. Vis. Sci. 58 (2017) 1506–1513.
[84] M.W.M. Wintergerst, T. Schultz, J. Birtel, A.K. Schuster, N. Pfeiffer, S. Schmitz-
Valckenberg, F.G. Holz, R.P. Finger, Algorithms for the automated analysis of age-
related macular degeneration biomarkers on optical coherence tomography: a systematic
review, Transl. Vis. Sci. Technol. 6 (2017) 10.
[85] Y. Kanagasingam, A. Bhuiyan, M.D. Abràmoff, R.T. Smith, L. Goldschmidt, T.Y. Wong,
Progress on retinal image analysis for age related macular degeneration, Prog. Retin.
Eye Res. 38 (2014) 20–42.
[86] Y. Zheng, M.H.A. Hijazi, F. Coenen, Automated “disease/no disease” grading of age-
related macular degeneration by an image mining approach, Invest. Ophthalmol. Vis.
Sci. 53 (2012) 8310–8318.
[87] M.H.A. Hijazi, F. Coenen, Y. Zheng, Data mining techniques for the screening of age-
related macular degeneration, Knowl.-Based Syst. 29 (2012) 83–92.