Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
22640597
DOI
10.1016/j.compmedimag.2012.04.006
PII: S0895-6111(12)00078-X
Knihovny.cz E-resources
- MeSH
- Fluorescein Angiography methods MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Humans MeSH
- Optic Nerve Diseases pathology MeSH
- Nerve Net pathology MeSH
- Reproducibility of Results MeSH
- Retinal Vessels pathology MeSH
- Retinoscopy methods MeSH
- Pattern Recognition, Automated methods MeSH
- Sensitivity and Specificity MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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.
References provided by Crossref.org
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