Publication detail

Multi-parametric segmentation of MR images of the Brain

DVOŘÁK, P. BARTUŠEK, K.

Original Title

Multi-parametric segmentation of MR images of the Brain

Czech Title

Multi-parametric segmentation of MR images of the Brain

Language

cs

Original Abstract

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.

Czech abstract

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.

Documents

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"
}