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 - 2019/7/24
Y1 - 2019/7/24
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 - vessels segmentation
KW - destriping
KW - OCTA
UR - https://dblp.org/db/conf/miua/miua2019.html#WuGWSZJ19
UR - https://dblp.org/db/conf/miua/2019
UR - https://livrepository.liverpool.ac.uk/3089637/
U2 - 10.1007/978-3-030-39343-4_36
DO - 10.1007/978-3-030-39343-4_36
M3 - Conference contribution
SN - 9783030393427
T3 - Communications in Computer and Information Science
SP - 423
EP - 435
BT - MIUA 2019: 23rd Conference on Medical Image Understanding and Analysis, Liverpool, UK, July 24–26, 2019, Proceedings
A2 - Zheng, Yalin
A2 - Williams, Bryan M.
A2 - Chen, Ke
PB - Springer
T2 - MIUA 2019: 23rd Conference on Medical Image Understanding and Analysis
Y2 - 24 July 2019 through 26 July 2019
ER -