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|Title:||Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs||Authors:||Wong, D.W.K.
|Issue Date:||2010||Citation:||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. 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||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. © 2010 IEEE.||Source Title:||2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10||URI:||http://scholarbank.nus.edu.sg/handle/10635/109757||ISBN:||9781424441235||DOI:||10.1109/IEMBS.2010.5626466|
|Appears in Collections:||Staff Publications|
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