Parameter estimation of the homodyned K distribution based on neural networks and trainable fractional-order moments
This work addresses parameter estimation for a distribution used in ultrasound imaging and optics, but it is incremental as it builds on existing machine learning methods with a novel twist.
The authors tackled the problem of estimating parameters of the homodyned K distribution, which models scattering in fields like ultrasound imaging, by proposing a neural network approach that uses trainable fractional-order moments, resulting in accurate parameter estimation.
Homodyned K (HK) distribution has been widely used to describe the scattering phenomena arising in various research fields, such as ultrasound imaging or optics. In this work, we propose a machine learning based approach to the estimation of the HK distribution parameters. We develop neural networks that can estimate the HK distribution parameters based on the signal-to-noise ratio, skewness and kurtosis calculated using fractional-order moments. Compared to the previous approaches, we consider the orders of the moments as trainable variables that can be optimized along with the network weights using the back-propagation algorithm. Networks are trained based on samples generated from the HK distribution. Obtained results demonstrate that the proposed method can be used to accurately estimate the HK distribution parameters.