Abstract
Accurate retinal vessel segmentation technology plays a critical role due to the changes in retinal blood vessels can be used to diagnose certain diseases. In this paper, we propose an application based on a new convolutional neural network for extracting retinal vessel particularly capillaries, which preserves image details as much as possible with different feature information. We evaluated this model on the DRIVE databases. Our results indicate that the network outperforms most competing approaches in term of accuracy, sensitivity, specificity, F1-score, the area under the ROC curve (AUC).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)
Li, Q., Feng, B., Xie, L.P., et al.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)
Kassim, Y.M., Palaniappan, K.: Extracting retinal vascular networks using deep learning architecture. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1170–1174, IEEE (2017)
Long J, Shelhamer, E, Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
He, K., Zhang, X., Ren, S., et al.: Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp. 630–645. Springer, Cham (2016)
Chen, L.C., Papandreou, G., Schroff, F., et al.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Chen, L.C., Zhu, Y., Papandreou, G., et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Staal, J., Abrmoff, M.D., Niemeijer, M., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35–44 (2004)
Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Marn, D., Aquino, A., Gegndez-Arias, M.E., et al.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)
Fraz, M.M., Remagnino, P., Hoppe, A., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012)
Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J. Biomed. Health Inform. 19(3), 1118–1128 (2015)
Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 248–251, IEEE (2017)
Hu, K., Zhang, Z., Niu, X., et al.: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309, 179–191 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, L., Li, J., Zhang, W., Fu, D. (2020). A Retinal Vessel Segmentation Algorithm with Convolutional Neural Network. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_47
Download citation
DOI: https://doi.org/10.1007/978-981-15-0474-7_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0473-0
Online ISBN: 978-981-15-0474-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)