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Description
  • This work focuses on segmentation of magnetic resonance images of brain. The segmentation is based on assumption that in magnetic resonance images with high signal-to-noise ratio, the noise can be approximated by Gaussian. The method is tested on stand-alone simulated 2D MR images of healthy brain. The comparison between T1-weighted, T2-weighted and multiparametric images is performed. The proposed algorithm is used to segment brain images into three different tissues. For the proposed method, the best results were achieved for stand-alone T1-weighted images, while stand-alone T2-weighted images show the worst results. The achieved results slightly vary for particular tissue.
  • This work focuses on segmentation of magnetic resonance images of brain. The segmentation is based on assumption that in magnetic resonance images with high signal-to-noise ratio, the noise can be approximated by Gaussian. The method is tested on stand-alone simulated 2D MR images of healthy brain. The comparison between T1-weighted, T2-weighted and multiparametric images is performed. The proposed algorithm is used to segment brain images into three different tissues. For the proposed method, the best results were achieved for stand-alone T1-weighted images, while stand-alone T2-weighted images show the worst results. The achieved results slightly vary for particular tissue. (en)
Title
  • Tissue Segmentation of Brain MRI
  • Tissue Segmentation of Brain MRI (en)
skos:prefLabel
  • Tissue Segmentation of Brain MRI
  • Tissue Segmentation of Brain MRI (en)
skos:notation
  • RIV/00216305:26220/14:PU109849!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, P(GAP102/12/1104), P(LD14091), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 50614
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/14:PU109849
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Brain, Gaussian Mixture Model, GMM, Image segmentation, Magnetic Resonance. (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B9B0A3811A72]
http://linked.open...v/mistoKonaniAkce
  • Berlín
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • 2014 37th International Conference on Telecommunications and Signal Processing (id 21150)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Bartušek, Karel
  • Dvořák, Pavel
  • Mikulka, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 978-80-214-4983-1
http://localhost/t...ganizacniJednotka
  • 26220
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