Self-similar texture characterization using Wigner-Ville distribution

Author

Wen, C.-Y. ; Acharya, R.

Author_Institution

Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA

Volume

3

fYear

1996

fDate

16-19 Sep 1996

Firstpage

141

Abstract

Fractals have been successfully used to model “natural” shapes and forms. While using the fractal model, the most important procedure is measuring the fractal parameter H (the Hurst coefficient), which is directly related to the fractal dimension. The Wigner-Ville distribution (WVD) is a time-frequency representation with excellent time and frequency resolutions. We propose a one dimensional WVD method to measure the fractal parameter H. Synthetic fractal images and a human tibia image were used to compare the performance of the WVD method to that of the maximum likelihood estimator (MLE) method and the power spectra method

Keywords

Wigner distribution; biomedical NMR; edge detection; fractals; image representation; image resolution; image texture; medical image processing; time-frequency analysis; 1D WVD method; Hurst coefficient; MLE method; MRI scanner; Wigner-Ville distribution; fractal dimension; fractal model; fractal parameter; fractals; frequency resolution; human tibia image; maximum likelihood estimator; natural forms; natural shapes; power spectra method; scanning lines; self-similar texture characterization; synthetic fractal images; time resolution; time-frequency representation; Biomedical imaging; Biomedical measurements; Brownian motion; Fractals; Humans; Magnetic resonance imaging; Maximum likelihood estimation; Shape; Time frequency analysis; Tomography;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560390

Filename

560390