Joint destriping and segmentation of OCTA images

Xiyin Wu, Dongxu Gao, Bryan M. Williams, Amira Stylianides, Yalin Zheng, Zhong Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationMIUA 2019: 23rd Conference on Medical Image Understanding and Analysis, Liverpool, UK, July 24–26, 2019, Proceedings
EditorsYalin Zheng, Bryan M. Williams, Ke Chen
Number of pages13
ISBN (Electronic)9783030393434
ISBN (Print)9783030393427
Publication statusPublished - 24 Jul 2019
EventMIUA 2019: 23rd Conference on Medical Image Understanding and Analysis - Liverpool, United Kingdom
Duration: 24 Jul 201926 Jul 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


ConferenceMIUA 2019: 23rd Conference on Medical Image Understanding and Analysis
Country/TerritoryUnited Kingdom


  • vessels segmentation
  • destriping
  • OCTA


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