Publication detail

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

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

Original Title

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

Type

journal article - other

Language

English

Original Abstract

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.

Keywords

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

Authors

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

RIV year

2012

Released

3. 9. 2012

Publisher

Elsevier

Location

Amsterdam (worldwide)

ISBN

0895-6111

Periodical

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Year of study

2012

Number

6

State

United States of America

Pages from

431

Pages to

441

Pages count

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"
}