Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor

Qiaoling Shang; Yitian Zhao; Zhili Chen; Huaying Hao; Fei Li; Xiulan Zhang; Jiang Liu

doi:10.1109/EMBC.2019.8857336

Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor

Qiaoling Shang
, Yitian Zhao
, Zhili Chen
, Huaying Hao
, Fei Li
, Xiulan Zhang
, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Morphological changes in the iris are one of the major causes of angle-closure glaucoma, and an anteriorly-bowed iris may be further associated with greater risk of disease progression from primary angle-closure suspect (PACS) to chronic primary angle-closure glaucoma (CPCAG). In consequence, the automated detection of abnormalities in the iris region is of great importance in the management of glaucoma. In this paper, we present a new method for the extraction of the iris region by using a local phase tensor-based curvilinear structure enhancement method, and apply it to anterior segment optical coherence tomography (AS-OCT) imagery in the presence of occludable iridocorneal angle. The proposed method is evaluated across a dataset of 200 anterior chamber angle (ACA) images, and the experimental results show that the proposed method outperforms existing state-of-the-art method in applicability, effectiveness, and accuracy.

Original language	English
Title of host publication	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4745-4749
Number of pages	5
ISBN (Electronic)	9781538613115
DOIs	https://doi.org/10.1109/EMBC.2019.8857336
Publication status	Published - Jul 2019
Externally published	Yes
Event	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: 23 Jul 2019 → 27 Jul 2019

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/Territory	Germany
City	Berlin
Period	23/07/19 → 27/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2019.8857336

Cite this

Shang, Q., Zhao, Y., Chen, Z., Hao, H., Li, F., Zhang, X., & Liu, J. (2019). Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 4745-4749). Article 8857336 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857336

Shang, Qiaoling ; Zhao, Yitian ; Chen, Zhili et al. / Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4745-4749 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{7b93673a3e54464a9cd8fdcbb24a163f,

title = "Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor",

abstract = "Morphological changes in the iris are one of the major causes of angle-closure glaucoma, and an anteriorly-bowed iris may be further associated with greater risk of disease progression from primary angle-closure suspect (PACS) to chronic primary angle-closure glaucoma (CPCAG). In consequence, the automated detection of abnormalities in the iris region is of great importance in the management of glaucoma. In this paper, we present a new method for the extraction of the iris region by using a local phase tensor-based curvilinear structure enhancement method, and apply it to anterior segment optical coherence tomography (AS-OCT) imagery in the presence of occludable iridocorneal angle. The proposed method is evaluated across a dataset of 200 anterior chamber angle (ACA) images, and the experimental results show that the proposed method outperforms existing state-of-the-art method in applicability, effectiveness, and accuracy.",

author = "Qiaoling Shang and Yitian Zhao and Zhili Chen and Huaying Hao and Fei Li and Xiulan Zhang and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",

year = "2019",

month = jul,

doi = "10.1109/EMBC.2019.8857336",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",

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}

Shang, Q, Zhao, Y, Chen, Z, Hao, H, Li, F, Zhang, X & Liu, J 2019, Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019., 8857336, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 4745-4749, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 23/07/19. https://doi.org/10.1109/EMBC.2019.8857336

Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor. / Shang, Qiaoling; Zhao, Yitian; Chen, Zhili et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4745-4749 8857336 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor

AU - Shang, Qiaoling

AU - Zhao, Yitian

AU - Chen, Zhili

AU - Hao, Huaying

AU - Li, Fei

AU - Zhang, Xiulan

AU - Liu, Jiang

PY - 2019/7

Y1 - 2019/7

N2 - Morphological changes in the iris are one of the major causes of angle-closure glaucoma, and an anteriorly-bowed iris may be further associated with greater risk of disease progression from primary angle-closure suspect (PACS) to chronic primary angle-closure glaucoma (CPCAG). In consequence, the automated detection of abnormalities in the iris region is of great importance in the management of glaucoma. In this paper, we present a new method for the extraction of the iris region by using a local phase tensor-based curvilinear structure enhancement method, and apply it to anterior segment optical coherence tomography (AS-OCT) imagery in the presence of occludable iridocorneal angle. The proposed method is evaluated across a dataset of 200 anterior chamber angle (ACA) images, and the experimental results show that the proposed method outperforms existing state-of-the-art method in applicability, effectiveness, and accuracy.

AB - Morphological changes in the iris are one of the major causes of angle-closure glaucoma, and an anteriorly-bowed iris may be further associated with greater risk of disease progression from primary angle-closure suspect (PACS) to chronic primary angle-closure glaucoma (CPCAG). In consequence, the automated detection of abnormalities in the iris region is of great importance in the management of glaucoma. In this paper, we present a new method for the extraction of the iris region by using a local phase tensor-based curvilinear structure enhancement method, and apply it to anterior segment optical coherence tomography (AS-OCT) imagery in the presence of occludable iridocorneal angle. The proposed method is evaluated across a dataset of 200 anterior chamber angle (ACA) images, and the experimental results show that the proposed method outperforms existing state-of-the-art method in applicability, effectiveness, and accuracy.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 23 July 2019 through 27 July 2019

ER -

Shang Q, Zhao Y, Chen Z, Hao H, Li F, Zhang X et al. Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4745-4749. 8857336. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8857336

Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor

Abstract

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UN SDGs

ASJC Scopus subject areas

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