Brain-inspired algorithms for retinal image analysis

Author:

ter Haar Romeny Bart M.ORCID,Bekkers Erik J.,Zhang Jiong,Abbasi-Sureshjani Samaneh,Huang Fan,Duits Remco,Dashtbozorg Behdad,Berendschot Tos T. J. M.,Smit-Ockeloen Iris,Eppenhof Koen A. J.,Feng Jinghan,Hannink Julius,Schouten Jan,Tong Mengmeng,Wu Hanhui,van Triest Han W.,Zhu Shanshan,Chen Dali,He Wei,Xu Ling,Han Ping,Kang Yan

Funder

NWO-He

European Foundation for the Study of Diabetes

EU Marie Curie

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Reference66 articles.

1. Abbasi-Sureshjani, S., Smit-Ockeloen, I., Zhang, J., ter Haar Romeny, B.M.: Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images. In: Kamel, M., Campilho, A. (eds.) Image Analysis and Recognition, Volume 9164 of Lecture Notes in Computer Science, pp. 325–334. Springer (2015)

2. Alexander, D., van Leeuwen, C.: Mapping of contextual modulation in the population response of primary visual cortex. Cogn. Neurodyn. 4, 124 (2012)

3. Aliahmad, R.B., Kumar, D.K., Sarossy, M.G., Jain, R.: Relationship between diabetes and grayscale fractal dimensions of retinal vasculature in the Indian population. BMC Ophthalmol. 14(1), 152–172 (2014)

4. Aquino, A., Gegundez-Arias, M.E., Marin, D.: Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE TMI 29(11), 1860–9 (2010)

5. Azzopardi, G., Petkov, N.: Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Comput. Anal. Images Patterns 34(8), 922–933 (2013)

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