Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features.
Glaucoma is a silent progressive eye disease that is among the leading causes of irreversible blindness. Early detection and proper treatment of glaucoma can limit severe vision impairments associated with advanced stages of the disease. Periodic automatic screening can help in the early detection o...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 151 - 159 |
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| Autor principal: | |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142164496&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164496 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164496 142164496 142164496 10.1007/s10278-019-00189-0 142164496 ppf: 151 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features. aug: au: Abdel-Hamid, Lamiaa affil: Electronics & Communications Department, Faculty of Engineering, Misr International University, Cairo, Egypt sug: subj: Glaucoma Diagnosis Retina Health Screening Image Processing, Computer Assisted Methods Algorithms Early Diagnosis Early Intervention Diagnostic Imaging Human ab: Glaucoma is a silent progressive eye disease that is among the leading causes of irreversible blindness. Early detection and proper treatment of glaucoma can limit severe vision impairments associated with advanced stages of the disease. Periodic automatic screening can help in the early detection of glaucoma while reducing the workload on expert ophthalmologists. In this work, a wavelet-based glaucoma detection algorithm is proposed for real-time screening systems. A combination of wavelet-based statistical and textural features computed from the detected optic disc region is used to determine whether a retinal image is healthy or glaucomatous. Two public datasets having different resolutions were considered in the performance analysis of the proposed algorithm. An accuracy of 96.7% and area under receiver operating curve (AUC) of 94.7% were achieved for the high-resolution dataset. Analysis of the wavelet-based statistical and textural features using three different methods showed their relevance for glaucoma detection. Furthermore, the proposed algorithm is shown to be suitable for real-time applications as it requires less than 3 s for processing the high-resolution retinal images. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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