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Conference Paper: Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis

TitleSliding window and regression based cup detection in digital fundus images for glaucoma diagnosis
Authors
Issue Date2011
PublisherSpringer
Citation
14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, 18-22 September 2011. In Fichtinger, G, Martel, A, Peters, T, et al. (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III, p. 1-8. Berlin: Springer, 2011 How to Cite?
AbstractWe propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An ε-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA -∈light clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems. © 2011 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/321447
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 6893
LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics

 

DC FieldValueLanguage
dc.contributor.authorXu, Yanwu-
dc.contributor.authorXu, Dong-
dc.contributor.authorLin, Stephen-
dc.contributor.authorLiu, Jiang-
dc.contributor.authorCheng, Jun-
dc.contributor.authorCheung, Carol Y.-
dc.contributor.authorAung, Tin-
dc.contributor.authorWong, Tien Yin-
dc.date.accessioned2022-11-03T02:18:59Z-
dc.date.available2022-11-03T02:18:59Z-
dc.date.issued2011-
dc.identifier.citation14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Toronto, Canada, 18-22 September 2011. In Fichtinger, G, Martel, A, Peters, T, et al. (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III, p. 1-8. Berlin: Springer, 2011-
dc.identifier.isbn9783642236259-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/321447-
dc.description.abstractWe propose a machine learning framework based on sliding windows for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary structural image cue for clinically identifying glaucoma. This localization uses a bundle of sliding windows of different sizes to obtain cup candidates in each disc image, then extracts from each sliding window a new histogram based feature that is learned using a group sparsity constraint. An ε-SVR (support vector regression) model based on non-linear radial basis function (RBF) kernels is used to rank each candidate, and final decisions are made with a non-maximal suppression (NMS) method. Tested on the large ORIGA -∈light clinical dataset, the proposed method achieves a 73.2% overlap ratio with manually-labeled ground-truth and a 0.091 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. The high accuracy of this framework on images from low-cost and widespread digital fundus cameras indicates much promise for developing practical automated/assisted glaucoma diagnosis systems. © 2011 Springer-Verlag.-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofMedical Image Computing and Computer-Assisted Intervention - MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 6893-
dc.relation.ispartofseriesLNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics-
dc.titleSliding window and regression based cup detection in digital fundus images for glaucoma diagnosis-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-642-23626-6_1-
dc.identifier.pmid22003677-
dc.identifier.scopuseid_2-s2.0-80053524664-
dc.identifier.spage1-
dc.identifier.epage8-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-

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