Graph-Cut Segmentation of Retinal Layers from OCT Images

Bashir Isa Dodo, Yongmin Li, Khalid Eltayef, Xiaohui Liu

2018

Abstract

The segmentation of various retinal layers is vital for diagnosing and tracking progress of medication of various ocular diseases. Due to the complexity of retinal structures, the tediousness of manual segmentation and variation from different specialists, many methods have been proposed to aid with this analysis. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts normally pre-process the images or model the segmentation to handle the obstruction but it still remains an area of active research, especially in relation to the graph based algorithms. In this paper we present an automatic retinal layer segmentation method, which is comprised of fuzzy histogram hyperbolization and graph cut methods to segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and centre of foveal region. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistency of the retinal structures in all regions.

Paper Citation

in Harvard Style

Dodo B., Li Y., Eltayef K. and Liu X. (2018). Graph-Cut Segmentation of Retinal Layers from OCT Images. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING; ISBN 978-989-758-278-3, SciTePress, pages 35-42. DOI: 10.5220/0006580600350042

in Bibtex Style

@conference{bioimaging18,
author={Bashir Isa Dodo and Yongmin Li and Khalid Eltayef and Xiaohui Liu},
title={Graph-Cut Segmentation of Retinal Layers from OCT Images},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING},
year={2018},
pages={35-42},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006580600350042},
isbn={978-989-758-278-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING
TI - Graph-Cut Segmentation of Retinal Layers from OCT Images
SN - 978-989-758-278-3
AU - Dodo B.
AU - Li Y.
AU - Eltayef K.
AU - Liu X.
PY - 2018
SP - 35
EP - 42
DO - 10.5220/0006580600350042
PB - SciTePress