Rayleigh mixture model for plaque characterization in intravascular ultrasound

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modelling of plaque echo-morphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex and the optimization procedure to estimate the plaque echo-morphology is more difficult. Here we propose to model the tissue echo-morphology by means of a mixture of Rayleigh distributions, known as Rayleigh Mixture Model. The problem formulation is still simple but its ability to describe complex textural patterns is very powerful. In this paper we present a method for the automatic estimation of the RMM mixture parameters by means of the Expectation Maximization algorithm which aims at characterizing tissue echomorphology in ultrasound. The performance of the proposed model is evaluated with a database of in-vitro IVUS cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.