Detail publikace

Wavelet Thresholding Techniques in MRI Domain

Originální název

Wavelet Thresholding Techniques in MRI Domain

Anglický název

Wavelet Thresholding Techniques in MRI Domain

Jazyk

en

Originální abstrakt

This paper deals with MR image de-noising by using the wavelet analysis focusing on the wavelet thresholding techniques and the threshold estimation. Hard, soft, semi-soft and non-negative garrote thresholding techniques are described and applied to test images with two different threshold estimators; one uses the universal threshold and the second is derived from the Bayesian risk minimization. The results are compared according to three parameters: SNR, intensity contrast and intensity gradient.

Anglický abstrakt

This paper deals with MR image de-noising by using the wavelet analysis focusing on the wavelet thresholding techniques and the threshold estimation. Hard, soft, semi-soft and non-negative garrote thresholding techniques are described and applied to test images with two different threshold estimators; one uses the universal threshold and the second is derived from the Bayesian risk minimization. The results are compared according to three parameters: SNR, intensity contrast and intensity gradient.

BibTex


@inproceedings{BUT30311,
  author="Jiří {Přinosil} and Zdeněk {Smékal} and Karel {Bartušek}",
  title="Wavelet Thresholding Techniques in MRI Domain",
  annote="This paper deals with MR image de-noising by using the wavelet analysis focusing on the wavelet thresholding techniques and the threshold estimation. Hard, soft, semi-soft and non-negative garrote thresholding techniques are described and applied to test images with two different threshold estimators; one uses the universal threshold and the second is derived from the Bayesian risk minimization. The results are compared according to three parameters: SNR, intensity contrast and intensity gradient.",
  address="IEEE Computer Society Conference Publishing Services (",
  booktitle="Proceedings of the First International Conference on Biosciences BioSciencesWorld 2010",
  chapter="30311",
  howpublished="electronic, physical medium",
  institution="IEEE Computer Society Conference Publishing Services (",
  year="2010",
  month="march",
  pages="58--63",
  publisher="IEEE Computer Society Conference Publishing Services (",
  type="conference paper"
}