Недзьведь О. В., Абламейко С. В., Недзьведь А. М., Глинский А. В., Карапетян Г. М., Анисимов А. А., Гуревич И. Б., Яшина В. В. АЛГОРИТМ ОПРЕДЕЛЕНИЯ ХАРАКТЕРИСТИК КРОВОТОКА В СОСУДАХ ГЛАЗНОГО ДНА ПО ВИДЕОПОСЛЕДОВАТЕЛЬНОСТИ. Информатика. 2018;15(1):92-102.
1. Algorithms for digital image processing in diabetic retinopathy / R. J. Winder [et al.] // Computerized Medical Imaging and Graphics. – 2009. – Vol. 33(8). – P. 608–622.
2. Felkel, P. Vessel tracking in peripheral CTA datasets – an overview / P. Felkel, R. Wegenkittl, A. Kanitsar // Computer Graphics (Spring Conference on). – Budmerice, Slovakia, 2001. – P. 232–239.
3. Buhler, K. Geometric methods for vessel visualization and quantification – a Survay / K. Buhler, P. Felkel, A. L. Cruz // Geometric Modelling for Scientific Visualization. – Berlin, Heidelberg: Springer, 2003. – P. 399–421.
4. Kirbas, C. A review of vessel extraction techniques and algorithms / C. Kirbas, F. Quek // ACM Computing. – 2004. – Vol. 36(2). – P. 81–121.
5. Mabrouk, M. S. Survey of retinal image segmentation and registration / M. S. Mabrouk, N. H. Solouma, Y. M. Kadah // International Journal on Graphics, Vision and Image Processing. – 2006. – Vol. 6(2). – P. 1–11.
6. Algorithms for the automated detection of diabetic retinopathy using digital fundus images : a review / O. Faust [et al.] // Journal of Medical Systems. – 2012. – Vol. 36(1). – P. 145–57.
7. Blood vessel segmentation methodologies in retinal images / M. M. Fraz [et al.] // Comput Methods Programs Biomed. – 2012. – Vol. 108(1). – P. 407–433.
8. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes / M. D. Abrаmoff [et al.] // Diabetes Care. – 2008. – Vol. 31(2). – P. 193–198.
9. Deep neural net-works segment neuronal membranes in electron microscopy images / D. C. Ciresan [et al.] // Advances in Neural Information Processing Systems 25 (NIPS 2012). – Harrahs and Harveys: Curran Associates, Inc., 2012. – P. 2852–2860.
10. Retinal vessel measurement: comparison between observer and computer driven methods / R. S. Newsom [et al.] // Graefes Arch. Clin. Exp. Ophthalmol. – 1992. – Vol. 230(3). – P. 221–225.
11. FlowNet: Learning Optical Flow with Convolutional Networks / A. Dosovitskiy [et al.] // Computer Vision (ICCV), IEEE Intern. Conf. – Chile, 2015. – P. 2758–2766.
12. Barron, J. L. Performance of optical flow techniques / J. L. Barron, D. J. Fleet, S. Beauchemin // International Journal of Computer Vision. – 1994. – Vol. 12(1). – P. 43–77.
13. Farnebäck, G. Two-Frame Motion Estimation Based on Polynomial Expansion / G. Farnebäck // Proceedings of the 13th Scandinavian Conf. on Image Analysis. – Halmstad, Sweden, 2003. – P. 363–370.
14. Detection of dynamical properties of flow in an eye vessels by video sequences analysis / A. Nedzved [et al.] // Intern. Conf. on Information and Digital Technologies. – Zilino, Slovakia, 2017. – P. 275–281.
