Abstract
The automatic detection of retinal blood vessels by computer aided techniques plays an important role in the diagnosis of diabetic retinopathy, glaucoma, and macular degeneration. In this paper we present a semantically flexible feature fusion network that employs residual skip connections between adjacent neurons to improve retinal vessel detection. This yields a method that can be trained employing residual learning. To illustrate the utility of our method for retinal blood vessel detection, we show results on two publicly available data sets, i.e. DRIVE and STARE. In our experimental evaluation we include widely used evaluation metrics and compare our results with those yielded by alternatives elsewhere in the literature. In our experiments, our method is quite competitive, delivering a margin of sensitivity and accuracy improvement as compared to the alternatives under consideration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The dataset is widely available at https://drive.grand-challenge.org/.
- 2.
The dataset can be accessed at https://cecas.clemson.edu/~ahoover/stare/probing/index.html.
References
Khawaja, A., Khan, T.M., Khan, M.A.U., Nawaz, S.J.: A multi-scale directional line detector for retinal vessel segmentation. Sensors 19(22), 4949 (2019)
Khawaja, A., Khan, T.M., Naveed, K., Naqvi, S.S., Rehman, N.U., Junaid Nawaz, S.: An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser. IEEE Access 7, 164344–164361 (2019)
Klein, R., Klein, B.E., Moss, S.E.: Visual impairment in diabetes. Ophthalmology 91(1), 1–9 (1984)
Soomro, T.A., Khan, T.M., Khan, M.A.U., Gao, J., Paul, M., Zheng, L.: Impact of ICA-based image enhancement technique on retinal blood vessels segmentation. IEEE Access 6, 3524–3538 (2018)
Zhang, J., Li, H., Nie, Q., Cheng, L.: A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection. Comput. Med. Imag. Graph. 38(6), 517–525 (2014)
Memari, N., Saripan, M.I.B., Mashohor, S., Moghbel, M.: Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment. J. Med. Biol. Eng. 1–19 (2018)
Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8, 01 (2018)
Thakoor, K.A., Li, X., Tsamis, E., Sajda, P., Hood, D.C.: Enhancing the accuracy of glaucoma detection from oct probability maps using convolutional neural networks. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2036–2040 (2019)
Zeng, X., Chen, H., Luo, Y., Ye, W.: Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7, 30 744–30 753 (2019)
Muraoka, Y., et al.: Morphologic and functional changes in retinal vessels associated with branch retinal vein occlusion. Ophthalmology 120(1), 91–99 (2013)
Cicinelli, M.V., et al.: Optical coherence tomography angiography in dry age-related macular degeneration. Surv. Ophthalmol. 63(2), 236–244 (2018)
Traustason, S., Jensen, A.S., Arvidsson, H.S., Munch, I.C., Søndergaard, L., Larsen, M.: Retinal oxygen saturation in patients with systemic hypoxemia. Invest. Ophthalmol. Vis. Sci. 52(8), 5064 (2011)
Jiang, Y., Tan, N., Peng, T.: Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access 7, 64 483–64 493 (2019)
Jiang, Y., Zhang, H., Tan, N., Chen, L.: Automatic retinal blood vessel segmentation based on fully convolutional neural networks. Symmetry 11, 1112 (2019)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning (2017)
Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Staal, J., Abramoff, 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)
Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)
Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16–27 (2016)
Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: International Symposium on Biomedical Imaging, pp. 248–251 (2017)
Yan, Z., Yang, X., Cheng, K.T.: Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans. Biomed. Eng. 1 (2018)
Jiang, Y., Tan, N., Peng, T., Zhang, H.: Retinal vessels segmentation based on dilated multi-scale convolutional neural network. IEEE Access 7, 76 342–76 352 (2019)
Adapa, D., et al.: A supervised blood vessel segmentation technique for digital fundus images using zernike moment based features. PLOS ONE 15(3), 1–23 (2020)
Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., Romeny, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)
Soomro, T.A., Afifi, A.J., Gao, J., Hellwich, O., Zheng, L., Paul, M.: Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Syst. Appl. 134, 36–52 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, T.M., Robles-Kelly, A., Naqvi, S.S. (2020). A Semantically Flexible Feature Fusion Network for Retinal Vessel Segmentation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-63820-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63819-1
Online ISBN: 978-3-030-63820-7
eBook Packages: Computer ScienceComputer Science (R0)