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In this paper we present a Markov Random Field (MRF) texture model for Radio-Frequency (RF) ultrasound data. The model is characterized by a set of parameters representing a powerful feature descriptor for the ultrasound envelope data. It combines the generalized gamma distribution (GGD) that describes global statistics of the envelope of backscattered echo with a local spatial interaction using MRF. We first construct the generalized gamma-MRF (GG-MRF) model and illustrate the role of its parameters. Applicability and performance are demonstrated on Choroidal melanoma.

International audience; The aim of this paper is to propose a new Markov Random Field (MRF) model for the backscattered ultrasonic echo in order to retrieve information about backscatter characteristics, such as the density, the scatterer amplitude, the scatterer spacing and the direction of interaction. The model combines the Nakagami distribution that describes the envelope of backscattered echo with spatial interaction using MRF.We first construct the Nakagami-MRF model and illustrate the role of its parameters by some synthetic simulations. Then, to enhance the ability of this MRF model to retrieve information on the spatial backscatter distribution, we compare the parameter values estimated on simulated radio-frequency (RF) envelope image for different tissue scatterers characteristics (density, amplitude, spacing, spatial orientation). It follows that the first parameter is related to the density and the amplitude, and, the interaction parameters are related to the scatterer spacing and the orientation.