Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100231
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFaust, Oliveren
dc.contributor.authorAcharya, U. Rajendraen
dc.contributor.authorNg, Eddie Yin-Kweeen
dc.contributor.authorNg, Kwan-Hoongen
dc.contributor.authorSuri, Jasjit S.en
dc.date.accessioned2013-09-23T07:20:21Zen
dc.date.accessioned2019-12-06T20:18:57Z-
dc.date.available2013-09-23T07:20:21Zen
dc.date.available2019-12-06T20:18:57Z-
dc.date.copyright2010en
dc.date.issued2010en
dc.identifier.citationFaust, O., Acharya, U. R., Ng, E. Y. K., Ng, K.-H., & Suri, J. S. (2010). Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review. Journal of Medical Systems, 36(1), 145-157.en
dc.identifier.urihttps://hdl.handle.net/10356/100231-
dc.description.abstractDiabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.en
dc.language.isoenen
dc.relation.ispartofseriesJournal of medical systemsen
dc.titleAlgorithms for the automated detection of diabetic retinopathy using digital fundus images : a reviewen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.1007/s10916-010-9454-7en
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:MAE Journal Articles

SCOPUSTM   
Citations

150
checked on Sep 6, 2020

WEB OF SCIENCETM
Citations

120
checked on Sep 22, 2020

Page view(s)

301
checked on Sep 24, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.