Advanced deep learning for blood vessel segmentation in retinal fundus images

Lua Ngo; Jae Ho Han

doi:10.1109/IWW-BCI.2017.7858169

Advanced deep learning for blood vessel segmentation in retinal fundus images

Lua Ngo, Jae Ho Han

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

7 Citations (Scopus)

Abstract

Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-The-Art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.

Original language	English
Title of host publication	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	91-92
Number of pages	2
ISBN (Electronic)	9781509050963
DOIs	https://doi.org/10.1109/IWW-BCI.2017.7858169
Publication status	Published - 2017 Feb 16
Event	5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of Duration: 2017 Jan 9 → 2017 Jan 11

Publication series

Name	5th International Winter Conference on Brain-Computer Interface, BCI 2017

Other

Other	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Country/Territory	Korea, Republic of
City	Gangwon Province
Period	17/1/9 → 17/1/11

Bibliographical note

Funding Information:
This work was supported in part by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP (Institute for Information & communications Technology Promotion). This research was also supported in part by Korea University Future Research Grant.

Keywords

Biomedical optical imaging
Blood vessels
Fundus images
Image segmentation
Medical image processing

ASJC Scopus subject areas

Signal Processing
Human-Computer Interaction

Access to Document

10.1109/IWW-BCI.2017.7858169

Cite this

Ngo, L., & Han, J. H. (2017). Advanced deep learning for blood vessel segmentation in retinal fundus images. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 91-92). Article 7858169 (5th International Winter Conference on Brain-Computer Interface, BCI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858169

Advanced deep learning for blood vessel segmentation in retinal fundus images. / Ngo, Lua; Han, Jae Ho.
5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 91-92 7858169 (5th International Winter Conference on Brain-Computer Interface, BCI 2017).

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

Ngo, L & Han, JH 2017, Advanced deep learning for blood vessel segmentation in retinal fundus images. in 5th International Winter Conference on Brain-Computer Interface, BCI 2017., 7858169, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 91-92, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, Gangwon Province, Korea, Republic of, 17/1/9. https://doi.org/10.1109/IWW-BCI.2017.7858169

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abstract = "Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-The-Art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.",

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Advanced deep learning for blood vessel segmentation in retinal fundus images

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this