Joint Destriping and Segmentation of OCTA Images

Wu, Xiyin and Gao, Dongxu and Williams, Bryan M. and Stylianides, Amira and Zheng, Yalin and Jin, Zhong (2020) Joint Destriping and Segmentation of OCTA Images. In: Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings. Communications in Computer and Information Science . Springer, GBR, pp. 423-435. ISBN 9783030393427

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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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600
Subjects:
ID Code:
142837
Deposited By:
Deposited On:
09 Jun 2021 21:00
Refereed?:
Yes
Published?:
Published
Last Modified:
14 Oct 2021 06:20