Detail publikace

Multi-parametric segmentation of MR images of the Brain

DVOŘÁK, P. BARTUŠEK, K.

Originální název

Multi-parametric segmentation of MR images of the Brain

Český název

Multi-parametric segmentation of MR images of the Brain

Typ

článek ve sborníku

Jazyk

cs

Originální abstrakt

This work deals with segmentation of magnetic resonance images. For better distinguishing between particular tissues, particular properties of tissues and their manifestation in different types of imaging are used. Specifically, T1 and T2 images are used. The segmentation is based on the approximation of more dimensional histograms. Since the noise distribution in MR images is close to Gaussian distribution for large signal-to-noise ratio, the approximation is done by Gaussian Mixture Model, where the number of components is determined using Bayesian Information Criterion and Elbow method.

Český abstrakt

This work deals with segmentation of magnetic resonance images. For better distinguishing between particular tissues, particular properties of tissues and their manifestation in different types of imaging are used. Specifically, T1 and T2 images are used. The segmentation is based on the approximation of more dimensional histograms. Since the noise distribution in MR images is close to Gaussian distribution for large signal-to-noise ratio, the approximation is done by Gaussian Mixture Model, where the number of components is determined using Bayesian Information Criterion and Elbow method.

Klíčová slova

GMM, image segmentation, MRI, multi-parametric image segmentation, tissue classification.

Rok RIV

2013

Vydáno

27.05.2013

Místo

Smolenice

ISBN

9788096967254

Kniha

9th International Conference on Measurement

Strany od

125

Strany do

128

Strany počet

4

BibTex 


@inproceedings{BUT99893,
  author="Pavel {Dvořák} and Karel {Bartušek}",
  title="Multi-parametric segmentation of MR images of the Brain",
  annote="This work deals with segmentation of magnetic resonance images. For better distinguishing between particular tissues, particular properties of tissues and their manifestation in different types of imaging are used. Specifically, T1 and T2 images are used. The segmentation is based on the approximation of more dimensional histograms. Since the noise distribution in MR images is close to Gaussian distribution for large signal-to-noise ratio, the approximation is done by Gaussian Mixture Model, where the number of components is determined using Bayesian Information Criterion and Elbow method.",
  booktitle="9th International Conference on Measurement",
  chapter="99893",
  howpublished="print",
  year="2013",
  month="may",
  pages="125--128",
  type="conference paper"
}