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 ve WoS nebo Scopus
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
Dokumenty
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"
}