2D wavelet-based spectra with applications
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.
Year of publication: |
2011
|
---|---|
Authors: | Nicolis, Orietta ; Ramírez-Cobo, Pepa ; Vidakovic, Brani |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 55.2011, 1, p. 738-751
|
Publisher: |
Elsevier |
Keywords: | Scaling Wavelets Self-similarity 2D wavelet spectra |
Saved in:
Saved in favorites
Similar items by person
-
A 2D wavelet-based multiscale approach with applications to the analysis of digital mammograms
Ramírez-Cobo, Pepa, (2013)
-
Vidakovic, Brani, (2004)
-
Spatio-temporal analysis of the avalanche hazard in the North of Italy
Nicolis, Orietta, (2013)
- More ...