Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEMBS.2010.5626466
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dc.titleLearning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs
dc.contributor.authorWong, D.W.K.
dc.contributor.authorLiu, J.
dc.contributor.authorTan, N.M.
dc.contributor.authorYin, F.
dc.contributor.authorLee, B.H.
dc.contributor.authorWong, T.Y.
dc.date.accessioned2014-11-26T07:49:39Z
dc.date.available2014-11-26T07:49:39Z
dc.date.issued2010
dc.identifier.citationWong, 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. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 : 5355-5358. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2010.5626466
dc.identifier.isbn9781424441235
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/109757
dc.description.abstractThe 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. © 2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/IEMBS.2010.5626466
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentOPHTHALMOLOGY
dc.description.doi10.1109/IEMBS.2010.5626466
dc.description.sourcetitle2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
dc.description.page5355-5358
dc.identifier.isiut000287964005188
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