Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.
Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual pos...
| Published in: | Journal of Medical Systems Vol. 40; no. 6; pp. 1 - 20 |
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| Main Authors: | , , , , , , , |
| Format: | algorithm pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925362&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925362 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2016 vid: 40 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925362 115925362 115925362 10.1007/s10916-016-0482-9 115925362 ppf: 1 ppct: 19 formats: fmt: @attributes: type: P tig: atl: Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images. aug: au: Haleem, Muhammad Han, Liangxiu Hemert, Jano Fleming, Alan Pasquale, Louis Silva, Paolo Song, Brian Aiello, Lloyd affil: Manchester Metropolitan University, School of Computing, Mathematics and Digital Technology Manchester M1 5GD UK sug: subj: Glaucoma Diagnosis Diagnosis, Computer Assisted Lasers Ophthalmoscopy Human ROC Curve Glaucoma Classification Funding Source ab: Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4 % and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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