Segmentation of optic nerve head images
© 2017 IEEE. Segmentation of the optic nerve head or optic disc in digital retinal fundus photographs is a non-invasive procedure that plays an important role in early detection of abnormalities of the eyes, particularly glaucoma diseases. Developing an automatic system, we employ image processing t...
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th-mahidol.423222019-03-14T15:03:22Z Segmentation of optic nerve head images Punsiri Boonyakiat Piyanuch Silapachote Mahidol University Computer Science © 2017 IEEE. Segmentation of the optic nerve head or optic disc in digital retinal fundus photographs is a non-invasive procedure that plays an important role in early detection of abnormalities of the eyes, particularly glaucoma diseases. Developing an automatic system, we employ image processing techniques coupled with graph cut algorithms from combinatorial optimization. Avoiding the need of manual pre-segmentation for constructing an initial graph, a supervised learning approach is effectively and efficiently applied. Crucial information is extracted from a set of labeled binary masks and integrated into weight assignments of the edges of the graph. We associate the characteristically bell-shape of a Gaussian distribution with the rounded circular-shape of the optic disc. Our approach was validated and evaluated on the RIM-ONE open database. Segmentation is successful on 91.12% of the entire 169 images, achieving 91% sensitivity and 88% accuracy. 2018-12-21T07:19:10Z 2019-03-14T08:03:22Z 2018-12-21T07:19:10Z 2019-03-14T08:03:22Z 2017-09-05 Conference Paper Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering, JCSSE 2017. (2017) 10.1109/JCSSE.2017.8025902 2-s2.0-85031749152 https://repository.li.mahidol.ac.th/handle/123456789/42322 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85031749152&origin=inward |
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© 2017 IEEE. Segmentation of the optic nerve head or optic disc in digital retinal fundus photographs is a non-invasive procedure that plays an important role in early detection of abnormalities of the eyes, particularly glaucoma diseases. Developing an automatic system, we employ image processing techniques coupled with graph cut algorithms from combinatorial optimization. Avoiding the need of manual pre-segmentation for constructing an initial graph, a supervised learning approach is effectively and efficiently applied. Crucial information is extracted from a set of labeled binary masks and integrated into weight assignments of the edges of the graph. We associate the characteristically bell-shape of a Gaussian distribution with the rounded circular-shape of the optic disc. Our approach was validated and evaluated on the RIM-ONE open database. Segmentation is successful on 91.12% of the entire 169 images, achieving 91% sensitivity and 88% accuracy. |
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Mahidol University |
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Mahidol University Punsiri Boonyakiat Piyanuch Silapachote |
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Conference or Workshop Item |
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Punsiri Boonyakiat Piyanuch Silapachote |
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Punsiri Boonyakiat |
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Segmentation of optic nerve head images |
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Segmentation of optic nerve head images |
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Segmentation of optic nerve head images |
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Segmentation of optic nerve head images |
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Segmentation of optic nerve head images |
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segmentation of optic nerve head images |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/42322 |
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