Detail publikace

Retinal Image Analysis Aimed at Blood Vessel Tree Segmentation and Early Detection of Neural-Layer Deterioration

JAN, J. ODSTRČILÍK, J. GAZÁREK, J. KOLÁŘ, R.

Originální název

Retinal Image Analysis Aimed at Blood Vessel Tree Segmentation and Early Detection of Neural-Layer Deterioration

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

retinal imaging, fundus-camera, retinal vessel tree, retinal neural fibre layer, image segmentation, 2D matched filtering, texture analysis, 2D spectra, edge maps

Autoři

JAN, J.; ODSTRČILÍK, J.; GAZÁREK, J.; KOLÁŘ, R.

Rok RIV

2012

Vydáno

3. 9. 2012

Nakladatel

Elsevier

Místo

Amsterdam (worldwide)

ISSN

0895-6111

Periodikum

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Ročník

2012

Číslo

6

Stát

Spojené státy americké

Strany od

431

Strany do

441

Strany počet

11

BibTex

@article{BUT92587,
  author="Jiří {Jan} and Jan {Odstrčilík} and Jiří {Gazárek} and Radim {Kolář}",
  title="Retinal Image Analysis Aimed at Blood Vessel Tree Segmentation and Early Detection of Neural-Layer Deterioration",
  journal="COMPUTERIZED MEDICAL IMAGING AND GRAPHICS",
  year="2012",
  volume="2012",
  number="6",
  pages="431--441",
  issn="0895-6111"
}