Page 244 - Computational Retinal Image Analysis

References 241

using laws texture masks and support vector machines, Comput. Methods Biomech.
Biomed. Eng. Imaging Vis. 6 (4) (2018) 405–416.
[30] R. Srivastava, L. Duan, D.W.K. Wong, J. Liu, T.Y. Wong, Detecting retinal microaneurysms
and hemorrhages with robustness to the presence of blood vessels, Comput. Methods
Programs Biomed. 138 (2017) 83–91.
[31] A. Osareh, B. Shadgar, R. Markham, A computational-intelligence-based approach for
detection of exudates in diabetic retinopathy images, IEEE Trans. Inf. Technol. Biomed.
13 (4) (2009) 535–545.
[32] E. Grisan, A. Ruggeri, A hierarchical Bayesian classification for non-vascular lesions
detection in fundus images, IFMBE Proc, 11, 2005, pp. 1727–1983. vol.
[33] E.M. Massey, A. Hunter, Augmenting the classification of retinal lesions using spatial
distribution, Engineering in Medicine and Biology Society, EMBC, 2011 Annual
International Conference of the IEEE, 2011, pp. 3967–3970.
[34] T. Spencer, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester, An image-processing
strategy for the segmentation and quantification of microaneurysms in fluorescein
angiograms of the ocular fundus, Comput. Biomed. Res. 29 (4) (1996) 284–302.
[35] M.J. Cree, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester, A fully automated
comparative microaneurysm digital detection system, Eye 11 (5) (1997) 622.
[36] A.J. Frame, P.E. Undrill, M.J. Cree, J.A. Olson, K.C. McHardy, P.F. Sharp, J.V. Forrester,
A comparison of computer based classification methods applied to the detection of
microaneurysms in ophthalmic fluorescein angiograms, Comput. Biol. Med. 28 (3)
(1998) 225–238.
[37] A.D. Fleming, S. Philip, K.A. Goatman, J.A. Olson, P.F. Sharp, Automated microaneurysm
detection using local contrast normalization and local vessel detection, IEEE Trans. Med.
Imaging 25 (9) (2006) 1223–1232.
[38] H. Li, O. Chutatape, Automated feature extraction in color retinal images by a model
based approach, IEEE Trans. Biomed. Eng. 51 (2) (2004) 246–254.
[39] C. Sinthanayothin, J.F. Boyce, T.H. Williamson, H.L. Cook, E. Mensah, S. Lal, D. Usher,
Automated detection of diabetic retinopathy on digital fundus images, Diabet. Med. 19
(2) (2002) 105–112.
[40] B. Zhang, X. Wu, J. You, Q. Li, F. Karray, Detection of microaneurysms using multi-
scale correlation coefficients, Pattern Recogn. 43 (6) (2010) 2237–2248.
[41] C. Pereira, L. Gonçalves, M. Ferreira, Exudate segmentation in fundus images using an
ant colony optimization approach, Inf. Sci. 296 (2015) 14–24.
[42] M. García, C.I. Sánchez, J. Poza, M.I. López, R. Hornero, Detection of hard exudates in
retinal images using a radial basis function classifier, Ann. Biomed. Eng. 37 (7) (2009)
1448–1463.
[43] M.D. Saleh, C. Eswaran, An automated decision-support system for non-proliferative
diabetic retinopathy disease based on MAs and HAs detection, Comput. Methods
Programs Biomed. 108 (1) (2012) 186–196.
[44] R. Phillips, J. Forrester, P. Sharp, Automated detection and quantification of retinal
exudates, Graefe’s Arch. Clin. Exp. Ophthalmol. 231 (2) (1993) 90–94.
[45] C.I. Sánchez, M. García, A. Mayo, M.I. López, R. Hornero, Retinal image analysis
based on mixture models to detect hard exudates, Med. Image Anal. 13 (4) (2009)
650–658.
[46] M.J.J.P. van Grinsven, B. van Ginneken, C.B. Hoyng, T. Theelen, C.I. Sánchez, Fast
convolutional neural network training using selective data sampling: application to
hemorrhage detection in color fundus images, IEEE Trans. Med. Imaging 35 (5) (2016)