Home > Research > Publications & Outputs > Joint Destriping and Segmentation of OCTA Images

Text available via DOI:

View graph of relations

Joint Destriping and Segmentation of OCTA Images

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Close
Publication date1/01/2020
Host publicationMedical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings
EditorsYalin Zheng, Bryan M. Williams, Ke Chen
PublisherSpringer
Pages423-435
Number of pages13
ISBN (Print)9783030393427
<mark>Original language</mark>English
Event23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 - Liverpool, United Kingdom
Duration: 24/07/201926/07/2019

Conference

Conference23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/TerritoryUnited Kingdom
CityLiverpool
Period24/07/1926/07/19

Publication series

NameCommunications in Computer and Information Science
Volume1065 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/TerritoryUnited Kingdom
CityLiverpool
Period24/07/1926/07/19

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.