Machine Learning And Pattern Classification In Identification Of Indigenous Retinal Pathology

dc.creatorJelinek H.F.
dc.creatorRocha A.
dc.creatorCarvalho T.
dc.creatorGoldenstein S.
dc.creatorWainer J.
dc.date2011
dc.date2015-06-30T20:27:23Z
dc.date2015-11-26T14:49:42Z
dc.date2015-06-30T20:27:23Z
dc.date2015-11-26T14:49:42Z
dc.date.accessioned2018-03-28T22:00:48Z
dc.date.available2018-03-28T22:00:48Z
dc.identifier9781424441211
dc.identifierProceedings Of The Annual International Conference Of The Ieee Engineering In Medicine And Biology Society, Embs. , v. , n. , p. 5951 - 5954, 2011.
dc.identifier1557170X
dc.identifier10.1109/IEMBS.2011.6091471
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84055192129&partnerID=40&md5=671cbd94c36d445180a5f30eb64ebee4
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/108004
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/108004
dc.identifier2-s2.0-84055192129
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1253986
dc.descriptionDiabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance. © 2011 IEEE.
dc.description
dc.description
dc.description5951
dc.description5954
dc.descriptionMitchell, P., Foran, S., Wong, T.Y., Chua, B., Patel, I., Ojaimi, E., (2008) Guidelines for the Management of Diabetic Retinopathy, , Canberra: NHMRC
dc.descriptionJelinek, H.F., Cornforth, D., Cree, M., Cesar R M, J., Leandro, J.J.G., Soares, J.V.B., Mitchell, P., Automated characterisation of diabetic retinopathy using mathematical morphology: A pilot study for community health (2003) NSW Primary Health Care Research and Evaluation Conference, p. 48. , Sydney
dc.descriptionCree, M.J., Olson, J.A., McHardy, K., Sharp, P., Forrester, J., A fully automated comparative microaneurysm digital detection system (1997) Eye, 11, pp. 622-628
dc.descriptionKarperien, A.L., Jelinek, H.F., Leandro, J.J.G., Soares, J.V.B., Cesar R M, J., Luckie, A., Automated detection of proliferative retinopathy in clinical practice (2008) Clinical Ophthalmology, 2, pp. 109-122
dc.descriptionWang, H., Hsu, W., Goh, K.G., Lee, M.L., An effective approach to detect lesions in colour retinal images (2000) IEEE Int. Conf. in Computer Vision and Pattern Recognition, pp. 181-187
dc.descriptionStreeter, L., Cree, M.J., Microaneurysm detection in colour fundus images (2003) Image and Vision Computing, pp. 280-284
dc.descriptionGoatman, K.A., Cree, M.J., Olson, J.A., Sharp, P.F., Forrester, J.V., Automated measurement of microaneurysm turnover (2003) Investigative Ophthalmology and Visual Science, 44, pp. 5335-5341
dc.descriptionCree, M.J., Gamble, E., Cornforth, D.J., Colour normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in colour retinal images (2005) Workshop on Digital Image Computing, pp. 163-169. , Brisbane, Australia
dc.descriptionValle, E., Cord, M., Philipp-Foliguet, S., High-dimensional descriptor indexing for large multimedia databases (2008) ACM Intl. Conf. on Information and Knowledge Management, pp. 739-748
dc.descriptionBay, H., Tuytelaars, T., Gool, L.V., SURF: Speeded up robust features (2006) European Conf. on Computer Vision, pp. 1-14
dc.descriptionViola, P., Jones, M., Robust real-time face detection (2004) Intl. Journa of Computer Vision, 52, pp. 137-154
dc.descriptionRocha, A., Carvalho, T., Goldenstein, S., Wainer, J., (2011) Points of Interest and Visual Dictionary for Retina Pathology Detection, , Technical Report IC-11-07, Institute of Computing, Univ. of Campinas, Campinas, Brazil
dc.languageen
dc.publisher
dc.relationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
dc.rightsfechado
dc.sourceScopus
dc.titleMachine Learning And Pattern Classification In Identification Of Indigenous Retinal Pathology
dc.typeActas de congresos


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