Page 245 - Computational Retinal Image Analysis

242 CHAPTER 12 Diabetic retinopathy and maculopathy lesions

1273–1284.
[47] J.I. Orlando, E. Prokofyeva, M. del Fresno, M.B. Blaschko, An ensemble deep learning
based approach for red lesion detection in fundus images, Comput. Methods Programs
Biomed. 153 (2018) 115–127.
[48] P. Chudzik, S. Majumdar, F. Caliva, B. Al-Diri, A. Hunter, Exudate segmentation using
fully convolutional neural networks and inception modules, Medical Imaging 2018:
Image Processing, vol. 10574, 2018, p. 1057430.
[49] P. Chudzik, S. Majumdar, F. Caliva, B. Al-Diri, A. Hunter, Microaneurysm detection
using deep learning and interleaved freezing, Medical Imaging 2018: Image Processing,
vol. 10574, 2018, p. 105741I.
[50] P. Chudzik, B. Al-Diri, F. Calivá, G. Ometto, A. Hunter, Exudates segmentation using
fully convolutional neural network and auxiliary codebook, 40th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE,
2018, pp. 770–773.
[51] L. Dai, R. Fang, H. Li, X. Hou, B. Sheng, Q. Wu, W. Jia, Clinical report guided retinal
microaneurysm detection with multi-sieving deep learning, IEEE Trans. Med. Imaging
37 (5) (2018) 1149–1161.
[52] C. Agurto, V. Murray, E. Barriga, S. Murillo, M. Pattichis, H. Davis, S. Russell,
M. Abràmoff, P. Soliz, Multiscale AM-FM methods for diabetic retinopathy lesion
detection, IEEE Trans. Med. Imaging 29 (2) (2010) 502–512.
[53] M. Javidi, H.-R. Pourreza, A. Harati, Vessel segmentation and microaneurysm detection
using discriminative dictionary learning and sparse representation, Comput. Methods
Programs Biomed. 139 (2017) 93–108.
[54] G. Quellec, S.R. Russell, M.D. Abràmoff, Optimal filter framework for automated,
instantaneous detection of lesions in retinal images, IEEE Trans. Med. Imaging 30 (2)
(2011) 523–533.
[55] C. Köse, U. Şevik, C. İkibaş, H. Erdöl, Simple methods for segmentation and
measurement of diabetic retinopathy lesions in retinal fundus images, Comput. Methods
Prog. Biomed. 107 (2) (2012) 274–293.
[56] I.N. Figueiredo, S. Kumar, C.M. Oliveira, J.D. Ramos, B. Engquist, Automated lesion
detectors in retinal fundus images, Comput. Biol. Med. 66 (2015) 47–65.
[57] E. Decencière, G. Cazuguel, X. Zhang, G. Thibault, J.-C. Klein, F. Meyer, B. Marcotegui,
G. Quellec, M. Lamard, R. Danno, D. Elie, P. Massin, Z. Viktor, A. Erginay, B. Laÿ,
A. Chabouis, TeleOphta: machine learning and image processing methods for
teleophthalmology, IRBM 34 (2) (2013) 196–203.
[58] E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, P. Gain,
R. Ordonez, P. Massin, A. Erginay, B. Charton, J.-C. Klein, Feedback on a publicly
distributed image database: the Messidor database, Image Anal. Stereol. 33 (3) (2014)
231–234.
[59] T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen,
R. Voutilainen, H. Uusitalo, H. Kälviäinen, J. Pietilä, The DIARETDB1 diabetic
retinopathy database and evaluation protocol, BMVC, 2007, pp. 1–10.
[60] B. Harangi, I. Lazar, A. Hajdu, Automatic exudate detection using active contour model
and regionwise classification, Annual International Conference of the IEEE Engineering
in Medicine and Biology Society, IEEE, 2012, pp. 5951–5954.
[61] L. Giancardo, F. Meriaudeau, T.P. Karnowski, Y. Li, S. Garg, K.W. Tobin Jr, E. Chaum,
Exudate-based diabetic macular edema detection in fundus images using publicly
available datasets, Med. Image Anal. 16 (1) (2012) 216–226.