Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound

Abstract

Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology 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 echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). 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 (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US 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.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

“…Also the penetrating nature of IVUS imaging technique helps for the visualization of the blood flow in the artery. Intravascular Ultrasound images used in this work are grayscale images and these are collected from the available resources [7]. The corresponding manually segmented ground truth images are also available.…”

“…Jose C. Seabra et al [7] proposed a technique for plaque characterization in IVUS data based on a mixture of Rayleigh distributions. The coefficients and parameters of the mixture model are used as features for describing fibrotic, lipidic, and calcified plaques.…”

“…But this is limited to thin layer and due to the presence of speckle noise the visual and automatic analysis in ultrasound images gets hindered. Jose C. Seabra et al [7] proposed a method for plaque characterization in IVUS data based on a mixture of Rayleigh distributions. The coefficients and parameters of the mixture model are used as features for describing fibrotic, lipidic, and calcified plaques and is classified using AdaBoost classifier.…”

Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

“…A typical scenario is to use mixture models [1][2][3] to smoothly model histograms. For example, Gaussian Mixture Models (GMMs) can be used to convert grey-valued images into binary images by building a GMM fitting the image intensity histogram and then choosing the binarization threshold as the average of the Gaussian means [1].…”

“…For example, Gaussian Mixture Models (GMMs) can be used to convert grey-valued images into binary images by building a GMM fitting the image intensity histogram and then choosing the binarization threshold as the average of the Gaussian means [1]. Similarly, Rayleigh Mixture Models (RMMs) are often used in ultrasound imagery [2] to model histograms, and perform segmentation by classification. When using mixtures, a fundamental primitive is to define a proper statistical distance between them.…”