Publication detail

Wavelet Thresholding Techniques in MRI Domain

PŘINOSIL, J. SMÉKAL, Z. BARTUŠEK, K.

Original Title

Wavelet Thresholding Techniques in MRI Domain

English Title

Wavelet Thresholding Techniques in MRI Domain

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

MR images, wavelet analysis, thresholding techniques, threshold estimation.

RIV year

2010

Released

07.03.2010

Publisher

IEEE Computer Society Conference Publishing Services (

ISBN

978-0-7695-3968-3

Book

Proceedings of the First International Conference on Biosciences BioSciencesWorld 2010

Edition number

1

Pages from

58

Pages to

63

Pages count

6

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"
}