Ultrasound Surface Extraction Using Radial Basis Functions

Abstract

Data acquired from ultrasound examinations is of interest not only for the physician, but also for the patient. While the physician uses the ultrasound data for diagnostic purposes the patient might be more interested in beautiful images in the case of prenatal imaging. Ultrasound data is noisy by nature and visually compelling 3D renderings are not always trivial to produce. This paper presents a technique which enables extraction of a smooth surface mesh from the ultrasound data by combining previous research in ultrasound processing with research in point cloud surface reconstruction. After filtering the ultrasound data using Variational Classification we extract a set of surface points. This set of points is then used to train an Adaptive Compactly Supported Radial Basis Functions system, a technique for surface reconstruction of noisy laser scan data. The resulting technique can be used to extract surfaces with adjustable smoothness and resolution and has been tested on various ultrasound datasets.