Detail publikace

Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling

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

Originální název

Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The texture analysis of the retinal nerve fiber layer (RNFL) in colour fundus images is a promising tool for early glau-coma diagnosis. This paper describes model-based method for detection of changes in the RNFL. The method utilizes Gaussian Markov random fields (GMRF) and the least-square error (LSE) estimate for the local RNFL texture modelling. The model parameters are used as a texture fea-tures and non-linear classifier based on the Bayesian rule is used for classification of healthy and glaucomatous RNFL tissue. The proposed features are tested in the sense of clas-sification errors and also they are applied for segmentation of RNFL defects in high-resolution colour fundus-camera images. The results are also compared with the Optical Co-herence Tomography images regarded as a gold standard for our application due to the possibility of RNFL thickness measurement.

Klíčová slova

glaucoma, retinal nerve fiber layer, retinal vessels segmentation, texture analysis, pattern recognition

Autoři

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

Rok RIV

2010

Vydáno

24. 8. 2010

Nakladatel

EURASIP

ISSN

2076-1465

Periodikum

18th European Signal Processing Conference (EUSIPCO-2010)

Stát

Dánské království

Strany od

1650

Strany do

1654

Strany počet

4

BibTex

@inproceedings{BUT29969,
  author="Jan {Odstrčilík} and Radim {Kolář} and Vratislav {Harabiš} and Jiří {Gazárek} and Jiří {Jan}",
  title="Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling",
  booktitle="18th European Signal Processing Conference (EUSIPCO-2010)",
  year="2010",
  series="EURASIP",
  journal="18th European Signal Processing Conference (EUSIPCO-2010)",
  number="18",
  pages="1650--1654",
  publisher="EURASIP",
  issn="2076-1465"
}