Self-similar texture characterization using a Fourier-domain maximum likelihood estimation method

A Maximum Likelihood Estimator (MLE) has been applied to estimating the Hurst parameter H on a self-similar texture image. Much of the work done so far has concentrated on the spatial domain. In this paper, we propose an approximate MLE method for estimating H in the Fourier domain. This method saves computational time and can be applied to estimating the parameter H directly from the Fourier-domain raw data collected by the Magnetic Resonance Imaging (MRI) scanner. We use synthetic fractal datasets and a human tibia image to study the performance of our method.