Abstract
As an innovative retinal imaging technology, optical coherence tomography angiography (OCTA) can resolve and provide important information of fine retinal vessels in a non-invasive and non-contact way. The effective analysis of retinal blood vessels is valuable for the investigation and diagnosis of vascular and vascular-related diseases, for which accurate segmentation is a vital first step. OCTA images are always affected by some stripe noises artifacts, which will impede correct segmentation and should be removed. To address this issue, we present a two-stage strategy for stripe noise removal by image decomposition and segmentation by an active contours approach. We then refine this into a new joint model, which improves the speed of the algorithm while retaining the quality of the segmentation and destriping. We present experimental results on both simulated and real retinal imaging data, demonstrating the effective performance of our new joint model for segmenting vessels from the OCTA images corrupted by stripe noise.
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
References
Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009)
Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition-modeling, algorithms, and parameter selection. Int. J. Comput. Vision 67(1), 111–136 (2006)
Bresson, X., Esedoḡlu, S., Vandergheynst, P., Thiran, J.P., Osher, S.: Fast global minimization of the active contour/snake model. J. Math. Imaging Vis. 28(2), 151–167 (2007)
Brownrigg, D.: The weighted median filter. Commun. ACM 27(8), 807–818 (1984)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)
Chang, Y., Yan, L., Wu, T., Zhong, S.: Remote sensing image stripe noise removal: From image decomposition perspective. IEEE Trans. Geosci. Remote Sens. 54(12), 7018–7031 (2016)
De Carlo, T.E., Romano, A., Waheed, N.K., Duker, J.S.: A review of optical coherence tomography angiography (octa). Int. J. Retina Vitreous 1(1), 5 (2015)
Eladawi, N., Elmogy, M., Helmy, O., Aboelfetouh, A., Riad, A., Sandhu, H., Schaal, S., El-Baz, A.: Automatic blood vessels segmentation based on different retinal maps from octa scans. Comput. Biol. Med. 89, 150–161 (2017)
Eladawi, N., et al.: Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (octa) images. Med. Phys. 45(10), 4582–4599 (2018)
Fazel, M.: Matrix rank minimization with applications. Ph.D. thesis, Stanford University (2002)
Moult, E., et al.: Ultrahigh-speed swept-source oct angiography in exudative amd. Ophthalmic Surg. Lasers Imaging Retina 45(6), 496–505 (2014)
Sheng, B., et al.: Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans. Cybern. 99, 1–13 (2018)
Spaide, R.F., Fujimoto, J.G., Waheed, N.K., Sadda, S.R., Staurenghi, G.: Optical coherence tomography angiography. Progress Retinal Eye Res. 64, 1–55 (2018)
Sum, K., Cheung, P.Y.: Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans. Biomed. Eng. 55(1), 358–360 (2008)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (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)
Zhu, C., et al.: Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput. Med. Imaging Graph. 55, 68–77 (2017)
Acknowledgments
This work is partially supported by National Natural Science Foundation of China under Grant nos. 61872188, U1713208, 61602244, 61672287, 61702262, 61773215, and Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant no. KYCX18_0426.
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
Wu, X., Gao, D., Williams, B.M., Stylianides, A., Zheng, Y., Jin, Z. (2020). Joint Destriping and Segmentation of OCTA Images. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-39343-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39342-7
Online ISBN: 978-3-030-39343-4
eBook Packages: Computer ScienceComputer Science (R0)