On modeling the tissue response from ultrasonic b-scan images

We model tissue as a collection of point scatterers embedded in a uniform media, and show that the higher-order statistics (HOS) of the scatterer spacing distribution can be estimated from digitized radio frequency (RF) scan line segments and be used in obtaining tissue signatures. We assume that RF echoes are non-Gaussian, on the grounds of empirical/theoretical justifications presented in the literature. Based on our model for tissue microstructure, we develop schemes for the estimation of resolvable periodicity as well as correlations among nonperiodic scatterers. Using HOS of the scattered signal, we define as tissue "color" a quantity that describes the scatterer spatial correlations, show how to evaluate it from the higher-order correlations of the digitized RF scan line segments, and investigate its potential as a tissue signature. The tools employed, i.e., HOS, were chosen as the most appropriate ones because they suppress Gaussian processes, such as the one arising from the diffused scatterers. HOS, unlike second-order statistics, also preserve the Fourierphase of the signature, the color of the tissue response. Working on simulated and clinical data, we show that the proposed periodicity estimation technique is superior to the widely used power spectrum and cepstrum techniques in terms of the accuracy of estimations. We also show that even when there is no significant periodicity in data, we are still able to characterize tissues using signatures based on the higher-order cumulant structure of the scatterer spacing distribution.