A new Markov random field model based on κ-distribution for textured ultrasound image

The aim of this paper is to propose a new Markov Random Field (MRF) for textured ultrasound image which use is more relevant than the use of the classic MRF, such as the gaussian markovian model. The main difference is that our model is based on K-distribution. We have built this K-MRF with reference to the Product Model. This latter means that the observed intensities of ultrasound image are the product of a degraded perfect image by a multiplicative noise called speckle. When the construct of K-MRF is already described, we propose in this paper a validation on synthetic and medical B-scan textured image. The synthetic textures are obtained by simulating the K-MRF. For medical texture, we estimate the parameters of the model from tissues. The estimated parameters are simulated and compared to medical texture. The resemblance is a first validation of the K-MRF and the tissue can be then characterized by the parameters of the model.