Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2012.6361007
DC FieldValue
dc.titlePathological myopia detection from selective fundus image features
dc.contributor.authorZhang, Z.
dc.contributor.authorCheng, J.
dc.contributor.authorLiu, J.
dc.contributor.authorSheri, Y.C.M.
dc.contributor.authorKong, C.C.
dc.contributor.authorMei, S.S.
dc.date.accessioned2014-06-19T05:38:52Z
dc.date.available2014-06-19T05:38:52Z
dc.date.issued2012
dc.identifier.citationZhang, Z.,Cheng, J.,Liu, J.,Sheri, Y.C.M.,Kong, C.C.,Mei, S.S. (2012). Pathological myopia detection from selective fundus image features. Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 : 1742-1745. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICIEA.2012.6361007" target="_blank">https://doi.org/10.1109/ICIEA.2012.6361007</a>
dc.identifier.isbn9781457721175
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/73747
dc.description.abstractWe explore feature selection methodology for automatic Pathological Myopia detection via learning from an optimal set of features. An mRMR optimized classifier is trained using the candidate feature set to find the optimized classifier. We tested the proposed methodology on eye records of approximately 800 subjects collected from a population study. The experimental results demonstrate that the new classifier is much efficient by using less than 25% of the initial candidate feature set. The ranked optimal feature set also enables the clinicians to better access the diagnostic process of the algorithm. The work is a further step towards the advancement of the automatic pathological myopia diagnosis. © 2012 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICIEA.2012.6361007
dc.sourceScopus
dc.subjectMinimum Redundancy-Maximum Relevancy (mRMR)
dc.subjectPathological Myopia
dc.subjectperipapillary atrophy (PPA)
dc.subjectSupport Vector Machines (SVM)
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/ICIEA.2012.6361007
dc.description.sourcetitleProceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
dc.description.page1742-1745
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 6, 2019

Page view(s)

87
checked on Dec 1, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.