Abstract
Over the last decades, various methods have been employed in medical images analysis. Some state-of-the-arts techniques such as deep learning have been recently applied to medical images analysis. This research proposes the application of deep learning technique in performing segmentation of retinal blood vessels. Analyzing and segmentation of retina vessels has assisted in diagnosis and monitoring of some diseases. Diseases such as age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis and choroidal neovascularization can be effectively managed by the analysis of retinal vessels images. In this work, a Deep Convolutional Encoder-Decoder Architecture for the segmentation of retinal vessels images is proposed. The proposed method is a deep learning system composed of an encoder and decoder mechanism allows a low resolution image set of retinal vessels to be analyzed by set of convolutional layers in the encoder unit before been sent into a decoder unit for final segmented output. The proposed system was evaluated using some evaluation metrics such as dice coefficient, jaccard index and mean of intersection. The review of the existing works was also carried out. It could be shown that the proposed system outperforms many existing methods in the segmentation of retinal vessels images.
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Akbar, S., Akram, M.U., Sharif, M., Tariq, A., Khan, S.A.: Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy. Artif. Intell. Med. 90, 15–24 (2018). https://doi.org/10.1016/j.artmed.2018.06.004
Triwijoyo, B.K., Pradipto, Y.D.: Detection of hypertension retinopathy using deep learning and boltzmann machines detection of hypertension retinopathy using deep learning and boltzmann machines. J. Phys: Conf. Ser. 801, 1–7 (2017). https://doi.org/10.1088/1742-6596/755/1/011001
Oluwatobi, A.N., et al.: Vascular networks segmented from retinal images of hypertensive retinopathy and glaucoma patients. J. Eng. Appl. Sci. (2019, in press)
Pound, M.P., et al.: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6, gix083 (2017)
Gopinath, K., Rangrej, S.B., Sivaswamy, J.: A deep learning framework for segmentation of retinal layers from OCT images. arXiv preprint arXiv:1806.08859 (2018)
Guo, S., Gao, Y., Wang, K., Li, T.: Deeply supervised neural network with short connections for retinal vessel segmentation. arXiv preprint arXiv:1803.03963 (2018)
Fu, W., Breininger, K., Würfl, T., Ravikumar, N., Schaffert, R., Maier, A.: Frangi-Net: a neural network approach to vessel segmentation. arXiv preprint arXiv:1711.03345 (2017)
Ben-Cohen, A., et al.: Retinal layers segmentation using fully convolutional network in OCT images. RSIP Vision (2017)
Wang, X., et al.: Retina blood vessel segmentation using a U-net based Convolutional neural network. In: Procedia Computer Science: International Conference on Data Science (ICDS 2018), Beijing, China, 8–9 June 2018 (2018)
Melinščak, M., Prentašić, P., Lončarić, S.: Retinal vessel segmentation using deep neural networks. In: VISAPP 2015 (10th International Conference on Computer Vision Theory and Applications) (2015)
Badar, M., Shahzad, M., Fraz, M.M.: Simultaneous segmentation of multiple retinal pathologies using fully convolutional deep neural network. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds.) MIUA 2018. CCIS, vol. 894, pp. 313–324. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95921-4_29
Kang, S.H., Park, H.S., Jang, J., Jeon, K.: Deep neural networks for the detection and segmentation of the retinal fluid in OCT images. National Institute for Mathematical Sciences, Daejeon, Korea, 34047
Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8(2), 155 (2018)
Li, M., Ma, Z., Liu, C., Zhang, G., Han, Z.: Robust Retinal blood vessel segmentation based on reinforcement local descriptions. Biomed. Res. Int. 2017, 9 (2017)
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)
Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132–139. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_16
Fan, Z., et al.: A hierarchical image matting model for blood vessel segmentation in fundus images. In: Computer Vision and Pattern Recognition, pp. 1–10 (2017). http://arxiv.org/abs/1701.00892
Hassan, M., Amin, M., Murtza, I., Khan, A., Chaudhry, A.: Robust hidden Markov model based intelligent blood vessel detection of fundus images. Comput. Methods Programs Biomed., 193–201 (2017). http://doi.org/10.1016/j.cmpb.2017.08.023
Güleryüz, M.Ş., Ulusoy, İ.: Retinal vessel segmentation using convolutional neural networks. In: IEEE 26th Signal Processing and Communication Applications Conference, pp. 1–4 (2018)
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Adeyinka, A.A., Adebiyi, M.O., Akande, N.O., Ogundokun, R.O., Kayode, A.A., Oladele, T.O. (2019). A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_15
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DOI: https://doi.org/10.1007/978-3-030-24308-1_15
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