Determination of Blood Flow Characteristics in Eye Vessels in Video Sequence
Accurately measuring blood flow in eye is an important challenge, as blood flow reflects the health of eye and is disrupted in many diseases. Existing techniques for measuring blood flow are limited due to the complex assumptions and calculations required. Digital image and video processing techniques started to be used for eye vessels analysis and evaluation during last decades. In this paper, we propose a method for determining the characteristics of blood flow in the vessels of eye conjunctiva, such as linear and volumetric blood speed, and topological characteristics of vascular net. The method first analyses image frame by frame sequentially and then builds integral optical flow for video sequence. Dynamic characteristics of eye vessels are introduced and calculated. These characteristics make it possible to determine changes in blood flow in eye vessels. We show the efficiency of our method in real eye vessels scenes.
C. J. Pournaras and C. E. Riva: Retinal blood flow evaluation. In Ophthalmologica, 2013, vol. 229, pp. 61-74.
T. E. Kornfield and E. A. Newman: Measurement of Retinal Blood Flow Using Fluorescently Labeled Red Blood Cells. In eNeuro 29, 2015, vol. 2, no. 2, DOI: https://doi.org/10.1523/ENEURO.0005-15.2015.
P. Ganesan and S. He and H. Xu: Analysis of retinal circulation using an image-based network model of retinal vasculature. In Microvascular Research, 2010, vol. 80, no. 1, pp. 99 – 109.
R. J. Winder and P. J. Morrow and I. N. McRitchie and J. R. Bailie and P. M. Hart: Algorithms for digital image processing in diabetic retinopathy. In Computerized Medical Imaging and Graphics, 2009, vol. 33, no. 8, рр. 608-622.
K. Buhler and P. Felkel and A. L. Cruz: Geometric methods for vessel visualization and quantification – a Survey. In Geometric Modelling for Scientific Visualization, 2003, pp. 399-421.
C. Kirbas and F. Quek: A review of vessel extraction techniques and algorithms. In ACM Computing, 2004, vol. 36, no. 2, pp. 81-121.
M. S. Mabrouk and N. H. Solouma and Y. M. Kadah: Survey of retinal image segmentation and registration. In International Journal on Graphics, Vision and Image Processing (GVIP), 2006, vol. 6, no. 2, pp. 1-11.
A. R. Rudnicka and C. G. Owen and S. A. Barman: Blood vessel segmentation methodologies in retinal images. In Computer Methods and Programs in Biomedicine, 2012, vol. 108, no. 1, pp. 407-433.
S. Abbasi-Sureshjani and B. H. Romeny and A. Sarti: Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering. In Journal of Mathematical Imaging and Vision, 2016, vol. 56, no. 1, pp. 158–172.
B. Al-Diri and A. Hunter and D. Steel: An active contour model for segmenting and measuring retinal vessels. In IEEE Transactions on Medical Imaging, 2009, vol. 28, no. 9, pp. 1488–1497.
H. S. Bhadauria1 and S. S. Bisht and A. Singh: Vessels Extraction from Retinal Images. In IOSR Journal of Electronics and Communication Engineering, 2013, vol. 6, no. 3, pp. 79-82.
J. De and H. Li and L. Cheng: Tracing retinal vessel trees by transductive inference. In BMC Bioinform, 2014, vol. 15, no. 1, 20, DOI: https://doi.org/10.1186/1471-2105-15-20.
K. K. Delibasis and A. I. Kechriniotis and C. Tsonos and N. Assimakis: Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. In Computer Methods and Programs in Biomedicine, 2010, vol. 100, no. 2, pp. 108–122.
I. S. Hephzi Punithavathi and P. Ganesh Kumar: Extraction of Blood Vessels for Retinal Image Analysis, In Middle-East Journal of Scientific Research, 2016, vol. 24, no. 1, pp. 450-457.
J. Almotiri and K. Elleithy and A. Elleithy: Retinal Vessels Segmentation Techniques and Algorithms: A Survey. In Applied Sciences, 2018, vol. 8, no. 2, 155; DOI: https://doi.org/10.3390/app8020155.
A. Nedzvedz and O. Nedzvedz and A. Glinsky and G. Karapetian and
I. Gurevich and V. Yashina: Detection of dynamical properties of flow in an eye vessels by video sequences analysis. In Proceediing of International Conference on Information and Digital Technologies, 2017, pp. 275-281.
D. Ciresan and D, A. Giusti and L. M. Gambardella and J. Schmidhuber: Deep neural networks segment neuronal membranes in electron microscopy images. In Proceeding of Advances in Neural Information Processing Systems, 2012, pp.2843–2851.
Ch. Chen and Sh.Ye and H. Chen and O. V. Nedzvedz and S. V. Ablameyko: Integral Optical Flow and its Application for Monitoring Dynamic Objects from a Video Sequence. In Journal of Applied Spectroscopy, 2017, vol. 84, no. 1, pp. 120-128.
G. Farneback: Two-frame motion estimation based on polynomial expansion. In Proceedings of the 13th Scandinavian Conference on Image Analysis, 2003, pp. 363–370.
This work is licensed under a Creative Commons Attribution 3.0 License.