Title :
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
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;
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
DOI :
10.1109/ICIP.1996.560390
