Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
22640597
DOI
10.1016/j.compmedimag.2012.04.006
PII: S0895-6111(12)00078-X
Knihovny.cz E-zdroje
- MeSH
- fluoresceinová angiografie metody MeSH
- interpretace obrazu počítačem metody MeSH
- lidé MeSH
- nemoci zrakového nervu patologie MeSH
- nervová síť patologie MeSH
- reprodukovatelnost výsledků MeSH
- retinální cévy patologie MeSH
- retinoskopie metody MeSH
- rozpoznávání automatizované metody MeSH
- senzitivita a specificita MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.
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