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Joint Destriping and Segmentation of OCTA Images. / Wu, Xiyin; Gao, Dongxu
; Williams, Bryan M. et al.
Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. ed. / Yalin Zheng; Bryan M. Williams; Ke Chen. Springer, 2020. p. 423-435 (Communications in Computer and Information Science; Vol. 1065 CCIS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Wu, X, Gao, D
, Williams, BM, Stylianides, A, Zheng, Y & Jin, Z 2020,
Joint Destriping and Segmentation of OCTA Images. in Y Zheng, BM Williams & K Chen (eds),
Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. Communications in Computer and Information Science, vol. 1065 CCIS, Springer, pp. 423-435, 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, Liverpool, United Kingdom,
24/07/19.
https://doi.org/10.1007/978-3-030-39343-4_36APA
Wu, X., Gao, D.
, Williams, B. M., Stylianides, A., Zheng, Y., & Jin, Z. (2020).
Joint Destriping and Segmentation of OCTA Images. In Y. Zheng, B. M. Williams, & K. Chen (Eds.),
Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings (pp. 423-435). (Communications in Computer and Information Science; Vol. 1065 CCIS). Springer.
https://doi.org/10.1007/978-3-030-39343-4_36Vancouver
Wu X, Gao D
, Williams BM, Stylianides A, Zheng Y, Jin Z.
Joint Destriping and Segmentation of OCTA Images. In Zheng Y, Williams BM, Chen K, editors, Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. Springer. 2020. p. 423-435. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-39343-4_36
Author
Bibtex
@inproceedings{9686fdce31684c3f87b7f82833e7e432,
title = "Joint Destriping and Segmentation of OCTA Images",
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.",
keywords = "Destriping, OCTA, Vessels segmentation",
author = "Xiyin Wu and Dongxu Gao and Williams, {Bryan M.} and Amira Stylianides and Yalin Zheng and Zhong Jin",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-39343-4_36",
language = "English",
isbn = "9783030393427",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "423--435",
editor = "Yalin Zheng and Williams, {Bryan M.} and Ke Chen",
booktitle = "Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings",
note = "23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 ; Conference date: 24-07-2019 Through 26-07-2019",
}
RIS
TY - GEN
T1 - Joint Destriping and Segmentation of OCTA Images
AU - Wu, Xiyin
AU - Gao, Dongxu
AU - Williams, Bryan M.
AU - Stylianides, Amira
AU - Zheng, Yalin
AU - Jin, Zhong
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Destriping
KW - OCTA
KW - Vessels segmentation
UR - http://www.scopus.com/inward/record.url?scp=85079097291&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39343-4_36
DO - 10.1007/978-3-030-39343-4_36
M3 - Conference contribution/Paper
AN - SCOPUS:85079097291
SN - 9783030393427
T3 - Communications in Computer and Information Science
SP - 423
EP - 435
BT - Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings
A2 - Zheng, Yalin
A2 - Williams, Bryan M.
A2 - Chen, Ke
PB - Springer
T2 - 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Y2 - 24 July 2019 through 26 July 2019
ER -
