Appropriate indicator of modeling error for threshold-based model selection in statistics-based ultrasound tissue characterization

Abstract

Analysis of the envelope statistics of ultrasound echo signals contributes to quantitative tissue characterization in medical ultrasound. Many probability distribution model functions have been studied, and the model function that should be used for tissue characterization depends on the type of disease, even in the same organ. Thus, an appropriate model selection is important for an accurate diagnosis. In this study, we aimed to select a model using threshold processing for modeling errors instead of a simple selection by minimizing the modeling error. For this purpose, we compared several indicators of modeling errors using random number simulations, ultrasonic simulation, and phantom experiment. The results validated that the Mahalanobis distance of moments is an appropriate indicator because it enables the use of a constant threshold value, regardless of the type of model function and data length.