2D wavelet-based spectra with applications

Orietta Nicolis, Pepa Ramírez-Cobo, Brani Vidakovic

Resultado de la investigación: Article

19 Citas (Scopus)

Resumen

A wavelet-based spectral method for estimating the (directional) Hurst parameter in isotropic and anisotropic non-stationary fractional Gaussian fields is proposed. The method can be applied to self-similar images and, in general, to d-dimensional data which scale. In the application part, the problems of denoising 2D fractional Brownian fields and classification of digital mammograms to benign and malignant are considered. In the first application, a Bayesian inference calibrated by information from the wavelet-spectral domain is used to separate the signal from the noise. In the second application, digital mammograms are classified into benign and malignant based on the directional Hurst exponents which prove to be discriminatory summaries.

Idioma originalEnglish
Páginas (desde-hasta)738-751
Número de páginas14
PublicaciónComputational Statistics and Data Analysis
Volumen55
N.º1
DOI
EstadoPublished - 1 ene 2011

Huella dactilar

Mammogram
Wavelets
Fractional
Gaussian Fields
Hurst Parameter
Hurst Exponent
Bayesian inference
Spectral Methods
Denoising

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Citar esto

Nicolis, Orietta ; Ramírez-Cobo, Pepa ; Vidakovic, Brani. / 2D wavelet-based spectra with applications. En: Computational Statistics and Data Analysis. 2011 ; Vol. 55, N.º 1. pp. 738-751.
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2D wavelet-based spectra with applications. / Nicolis, Orietta; Ramírez-Cobo, Pepa; Vidakovic, Brani.

En: Computational Statistics and Data Analysis, Vol. 55, N.º 1, 01.01.2011, p. 738-751.

Resultado de la investigación: Article

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