Level Set Segmentation of Retinal OCT Images

Bashir Isa Dodo, Yongmin Li, XiaoHui Liu, Muhammad Isa Dodo

2019

Abstract

Optical coherence tomography (OCT) yields high-resolution images of the retina. Reliable identification of the retinal layers is necessary for the extraction of clinically useful information used for tracking the progress of medication and diagnosis of various ocular diseases. Many automatic methods have been proposed to aid with the analysis of retinal layers, mainly, due to the complexity of retinal structures, the cumbersomeness of manual segmentation and variation from one specialist to the other. However, a common drawback suffered by existing methods is the challenge of dealing with image artefacts and inhomogeneity in pathological structures. In this paper, we embed prior knowledge of the retinal architecture derived from the gradient information, into the level set method to segment seven (7) layers of the retina. Mainly, we start by establishing the region of interest (ROI).The gradient edges obtained from the ROI are used to initialise curves for the layers, and the layer topology is used in constraining the evolution process towards the actual layer boundaries based on image forces. Experimental results show our method obtains curves that are close to the manual layers labelled by experts.

Paper Citation

in Harvard Style

Isa Dodo B., Li Y., Liu X. and Dodo M. (2019). Level Set Segmentation of Retinal OCT Images. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 2: BIOIMAGING; ISBN 978-989-758-353-7, SciTePress, pages 49-56. DOI: 10.5220/0007577600490056

in Bibtex Style

@conference{bioimaging19,
author={Bashir Isa Isa Dodo and Yongmin Li and XiaoHui Liu and Muhammad Isa Dodo},
title={Level Set Segmentation of Retinal OCT Images},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 2: BIOIMAGING},
year={2019},
pages={49-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007577600490056},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 2: BIOIMAGING
TI - Level Set Segmentation of Retinal OCT Images
SN - 978-989-758-353-7
AU - Isa Dodo B.
AU - Li Y.
AU - Liu X.
AU - Dodo M.
PY - 2019
SP - 49
EP - 56
DO - 10.5220/0007577600490056
PB - SciTePress