Abstract
The quantitative analysis of retinal blood vessels is important for the management of vascular disease and tackling problems such as locating blood clots. Such tasks are hampered by the inability to accurately trace back problems along vessels to the source. This is due to the unresolved challenge of distinguishing automatically between vessel branchings and vessel crossings. In this paper, we present a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings. We achieve a high accuracy of 94% for junction detection and 88% for classification. Combined with work in segmentation, this method has the potential to facilitate automated localisation of blood clots and other disease symptoms leading to improved management of eye disease through aiding or replacing a clinicians diagnosis.
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References
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Chutatape, O., Zheng, L., Krishnan, S.M.: Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 6, pp. 3144–3149. IEEE (1998)
Cun, Y.L., Boser, B., Denker, J.S., Howard, R.E., Habbard, W., Jackel, L.D., Henderson, D.: Advances in Neural Information Processing Systems 2, pp. 396–404. Citeseer (1990)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
Gonzalez, R., Wintz, P.: Digital Image Processing (1977)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
Krizhevsky, A.: Learning multiple layers of features from tiny images. https://www.cs.toronto.edu/ kriz/learning-features-2009-TR.pdf
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/
MacGillivray, T., Trucco, E., Cameron, J., Dhillon, B., Houston, J., Van Beek, E.: Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions. Br. J. Radiol. 87(1040), 20130832 (2014)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Zhao, Y., Rada, L., Chen, K., Harding, S.P., Zheng, Y.: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imaging 34(9), 1797–1807 (2015)
Acknowledgement
H. Pratt acknowledges PhD funding from Fight for Sight charity. This project is funded in part by the National Institute for Health Research’s i4i Programme. This paper summarises independent research funded by the National Institute for Health Research (NIHR) under its i4i Programme (Grant Reference Number II-LA-0813-20005). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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Pratt, H., Williams, B.M., Ku, J., Coenen, F., Zheng, Y. (2017). Automatic Detection and Identification of Retinal Vessel Junctions in Colour Fundus Photography. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_3
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