Detail publikace

Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling

Originální název

Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling

Anglický název

Retinal Nerve Fiber Layer Analysis via Markov Random Fields Texture Modelling

Jazyk

en

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.

Anglický 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.

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",
  annote="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.",
  address="EURASIP",
  booktitle="18th European Signal Processing Conference (EUSIPCO-2010)",
  chapter="29969",
  edition="EURASIP",
  howpublished="online",
  institution="EURASIP",
  year="2010",
  month="august",
  pages="1650--1654",
  publisher="EURASIP",
  type="conference paper"
}