Detail publikace

Tissue Segmentation of Brain MRI

Originální název

Tissue Segmentation of Brain MRI

Anglický název

Tissue Segmentation of Brain MRI

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT108629,
  author="Pavel {Dvořák} and Jan {Mikulka} and Karel {Bartušek}",
  title="Tissue Segmentation of Brain MRI",
  annote="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.",
  booktitle="38th International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="108629",
  doi="10.1109/TSP.2015.7296361",
  howpublished="online",
  year="2014",
  month="july",
  pages="735--738",
  type="conference paper"
}