Automated basal cell carcinoma detection in high-definition optical coherence tomography

Annan Li; Jun Cheng; Ai Ping Yow; Ruchir Srivastava; Damon Wing Kee Wong; Hong Liang Tey; Jiang Liu

doi:10.1109/EMBC.2016.7591332

Automated basal cell carcinoma detection in high-definition optical coherence tomography

Annan Li
, Jun Cheng
, Ai Ping Yow
, Ruchir Srivastava
, Damon Wing Kee Wong
, Hong Liang Tey
, Jiang Liu

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

9 Scopus citations

Abstract

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2885-2888
Number of pages	4
ISBN (Electronic)	9781457702204
DOIs	https://doi.org/10.1109/EMBC.2016.7591332
State	Published - 13 Oct 2016
Externally published	Yes
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/Territory	United States
City	Orlando
Period	16/08/16 → 20/08/16

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1109/EMBC.2016.7591332

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Li, A., Cheng, J., Yow, A. P., Srivastava, R., Wong, D. W. K., Tey, H. L., & Liu, J. (2016). Automated basal cell carcinoma detection in high-definition optical coherence tomography. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 2885-2888). Article 7591332 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591332

Li, Annan ; Cheng, Jun ; Yow, Ai Ping et al. / Automated basal cell carcinoma detection in high-definition optical coherence tomography. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2885-2888 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{5e62537015954e8a9c32b15ca7c90c12,

title = "Automated basal cell carcinoma detection in high-definition optical coherence tomography",

abstract = "Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.",

author = "Annan Li and Jun Cheng and Yow, \{Ai Ping\} and Ruchir Srivastava and Wong, \{Damon Wing Kee\} and Tey, \{Hong Liang\} and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

year = "2016",

month = oct,

day = "13",

doi = "10.1109/EMBC.2016.7591332",

language = "英语",

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.",

pages = "2885--2888",

booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",

address = "美国",

}

Li, A, Cheng, J, Yow, AP, Srivastava, R, Wong, DWK, Tey, HL & Liu, J 2016, Automated basal cell carcinoma detection in high-definition optical coherence tomography. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7591332, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers Inc., pp. 2885-2888, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7591332

Automated basal cell carcinoma detection in high-definition optical coherence tomography. / Li, Annan; Cheng, Jun; Yow, Ai Ping et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2885-2888 7591332 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

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

TY - GEN

T1 - Automated basal cell carcinoma detection in high-definition optical coherence tomography

AU - Li, Annan

AU - Cheng, Jun

AU - Yow, Ai Ping

AU - Srivastava, Ruchir

AU - Wong, Damon Wing Kee

AU - Tey, Hong Liang

AU - Liu, Jiang

PY - 2016/10/13

Y1 - 2016/10/13

N2 - Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.

AB - Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.

UR - https://www.scopus.com/pages/publications/85009145352

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DO - 10.1109/EMBC.2016.7591332

M3 - 会议稿件

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AN - SCOPUS:85009145352

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 2885

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BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

Y2 - 16 August 2016 through 20 August 2016

ER -

Li A, Cheng J, Yow AP, Srivastava R, Wong DWK, Tey HL et al. Automated basal cell carcinoma detection in high-definition optical coherence tomography. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2885-2888. 7591332. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7591332

Automated basal cell carcinoma detection in high-definition optical coherence tomography

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