Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs

D. W.K. Wong; J. Liu; N. M. Tan; F. Yin; B. H. Lee; T. Y. Wong

doi:10.1109/IEMBS.2010.5626466

Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs

D. W.K. Wong, J. Liu, N. M. Tan, F. Yin, B. H. Lee, T. Y. Wong

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

14 Citations (Scopus)

Abstract

The optic disc is an important feature in the retina. We propose a method for the detection of the optic disc based on a supervised learning scheme. The method employs pixel and local neighbourhood features extracted from the ROI of a digital retinal fundus photograph. A support vector machine based classification mechanism is used to classify each image point as belonging to the cup and retina. The proposed method is evaluated on a sample image set of 68 retinal fundus images. The results show a high correlation (r>0.9) with the ground truth segmentation, with an overlap error of 6.02%, and found to be comparable to the inter-observer variability based on an independent second observer segmentation of the same data set.

Original language	English
Title of host publication	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages	5355-5358
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2010.5626466
Publication status	Published - 2010
Externally published	Yes
Event	2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010

Publication series

Name	2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Conference

Conference	2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/Territory	Argentina
City	Buenos Aires
Period	31/08/10 → 4/09/10

ASJC Scopus subject areas

Biomedical Engineering
Computer Vision and Pattern Recognition
Signal Processing
Health Informatics

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10.1109/IEMBS.2010.5626466

Cite this

Wong, D. W. K., Liu, J., Tan, N. M., Yin, F., Lee, B. H., & Wong, T. Y. (2010). Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 (pp. 5355-5358). Article 5626466 (2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10). https://doi.org/10.1109/IEMBS.2010.5626466

Wong, D. W.K. ; Liu, J. ; Tan, N. M. et al. / Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. 2010. pp. 5355-5358 (2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10).

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title = "Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs",

abstract = "The optic disc is an important feature in the retina. We propose a method for the detection of the optic disc based on a supervised learning scheme. The method employs pixel and local neighbourhood features extracted from the ROI of a digital retinal fundus photograph. A support vector machine based classification mechanism is used to classify each image point as belonging to the cup and retina. The proposed method is evaluated on a sample image set of 68 retinal fundus images. The results show a high correlation (r>0.9) with the ground truth segmentation, with an overlap error of 6.02%, and found to be comparable to the inter-observer variability based on an independent second observer segmentation of the same data set.",

author = "Wong, {D. W.K.} and J. Liu and Tan, {N. M.} and F. Yin and Lee, {B. H.} and Wong, {T. Y.}",

note = "Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 ; Conference date: 31-08-2010 Through 04-09-2010",

year = "2010",

doi = "10.1109/IEMBS.2010.5626466",

language = "English",

isbn = "9781424441235",

series = "2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10",

pages = "5355--5358",

booktitle = "2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10",

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Wong, DWK, Liu, J, Tan, NM, Yin, F, Lee, BH & Wong, TY 2010, Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs. in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10., 5626466, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, pp. 5355-5358, 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, Buenos Aires, Argentina, 31/08/10. https://doi.org/10.1109/IEMBS.2010.5626466

Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs. / Wong, D. W.K.; Liu, J.; Tan, N. M. et al.
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. 2010. p. 5355-5358 5626466 (2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10).

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

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AB - The optic disc is an important feature in the retina. We propose a method for the detection of the optic disc based on a supervised learning scheme. The method employs pixel and local neighbourhood features extracted from the ROI of a digital retinal fundus photograph. A support vector machine based classification mechanism is used to classify each image point as belonging to the cup and retina. The proposed method is evaluated on a sample image set of 68 retinal fundus images. The results show a high correlation (r>0.9) with the ground truth segmentation, with an overlap error of 6.02%, and found to be comparable to the inter-observer variability based on an independent second observer segmentation of the same data set.

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Wong DWK, Liu J, Tan NM, Yin F, Lee BH, Wong TY. Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. 2010. p. 5355-5358. 5626466. (2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10). doi: 10.1109/IEMBS.2010.5626466

Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs

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