Publication detail

3D LUNG SEGMENTATION SEGMENTATION USING MARKOV RANDOM FIELDS

CHMELÍK, J. JAN, J.

Original Title

3D LUNG SEGMENTATION SEGMENTATION USING MARKOV RANDOM FIELDS

Czech Title

3D SEGMENTACE PLIC S VYUŽITÍM MARKOVSKÝCH NÁHODNÝCH POLÍ

English Title

3D LUNG SEGMENTATION SEGMENTATION USING MARKOV RANDOM FIELDS

Type

conference paper

Language

en

Original Abstract

In this paper, Bayesian classification with Markov random fields is used for 3D Computed Tomography (3D CT) lung image segmentation and modified metropolis dynamic is employed as optimization algorithm. Lung tissue is well separated from the other tissues like a bones, muscles, surrounding soft tissue and fat. Segmentation is necessary for subsequent lung analysis (size, shape, lung contour, etc.), and lung blood-vessels, airways (bronchi, bronchioles) segmentation and tumour studies.

Czech abstract

V této práci je použita Bayesovská klasifikace s využitím Markovských náhodných polí pro segmentaci plic pořízených pomocí 3D počítačové tomografie; jako optimalizační algoritmus je použita modifikovaná metoda Metropolis Dynamic. Plicní tkáň je dobře oddělena od ostatních tkání, jako jsou kosti, svalová hmota, další okolní měkké tkáně a tuk. Tato segmentace je nezbytností pro následující analýzu plic (velikost, tvar, kontura plic, atd.), pro segmentaci plicních cév, dýchacích cest (průdušky, průdušinky) a pro nádorové studie.

English abstract

In this paper, Bayesian classification with Markov random fields is used for 3D Computed Tomography (3D CT) lung image segmentation and modified metropolis dynamic is employed as optimization algorithm. Lung tissue is well separated from the other tissues like a bones, muscles, surrounding soft tissue and fat. Segmentation is necessary for subsequent lung analysis (size, shape, lung contour, etc.), and lung blood-vessels, airways (bronchi, bronchioles) segmentation and tumour studies.

Keywords

Markov Random Fields, 3D Lung Segmentation, Bayesian Classification

RIV year

2014

Released

24.04.2014

Publisher

LITERA

Location

Brno

ISBN

978-80-214-4924-4

Book

Proceedings of the 20th Conference STUDENT EEICT 2014 Volume 3

Edition number

1

Pages from

217

Pages to

221

Pages count

5

BibTex


@inproceedings{BUT107618,
  author="Jiří {Chmelík} and Jiří {Jan}",
  title="3D LUNG SEGMENTATION SEGMENTATION USING MARKOV RANDOM FIELDS",
  annote="In this paper, Bayesian classification with Markov random fields is used for 3D Computed Tomography (3D CT) lung image segmentation and modified metropolis dynamic is employed as optimization algorithm. Lung tissue is well separated from the other tissues like a bones, muscles, surrounding soft tissue and fat. Segmentation is necessary for subsequent lung analysis (size, shape, lung contour, etc.), and lung blood-vessels, airways (bronchi, bronchioles) segmentation and tumour studies.",
  address="LITERA",
  booktitle="Proceedings of the 20th Conference STUDENT EEICT 2014 Volume 3",
  chapter="107618",
  howpublished="print",
  institution="LITERA",
  year="2014",
  month="april",
  pages="217--221",
  publisher="LITERA",
  type="conference paper"
}