Modeling ultrasound images with the generalized K model

In this paper we interpret the statistics of ultrasonic backscatter in the framework of a normal variance-mean mixture model. This is done by considering the complex envelope of the echo signal as a double stochastic circular Gaussian variable, in which both the variance and the mean are linearly scaled by a stochastic factor Z. By assuming Z to be Γ distributed, we re-derive the generalized K distribution, and present a new iterative algorithm for estimating its parameters. We also derive a maximum a posteriori (MAP) filter based on the generalized K model. The appropriateness of the generalized K model in representing the local amplitude statistics of medical ultrasound images, and the filtering performance of the the new MAP filter, are tested in some preliminary experiments.