Statistical Geometrical Features for Microaneurysm Detection.
Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of featu...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 2; pp. 224 - 235 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2018
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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=128715846&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128715846 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2018 vid: 31 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128715846 128715846 128715846 10.1007/s10278-017-0008-0 128715846 ppf: 224 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Statistical Geometrical Features for Microaneurysm Detection. aug: au: Manjaramkar, Arati Kokare, Manesh affil: Department of Information Technology, SGGS Institute of Engineering & Technology, Nanded, Maharashtra 431606, India sug: subj: Microaneurysm Diagnosis Models, Statistical Sensitivity and Specificity Databases, Health Diabetic Retinopathy ab: Automated microaneurysm (MA) detection is still an open challenge due to its small size and similarity with blood vessels. In this paper, we present a novel method which is simple, efficient, and real-time for segmenting and detecting MA in color fundus images (CFI). To do this, a novel set of features based on statistics of geometrical properties of connected regions, that can easily discriminate lesion and non-lesion pixels are used. For large-scale evaluation proposed method is validated on DIARETDB1, ROC, STARE, and MESSIDOR dataset. It proves robust with respect to different image characteristics and camera settings. The best performance was achieved on per-image evaluation on DIARETDB1 dataset with sensitivity of 88.09 at 92.65% specificity which is quite encouraging for clinical use. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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