Volume reconstruction of freehand three-dimensional ultrasound using median filters

Abstract

Objectives: This paper aims to apply median filters for reducing interpolation error and improving the quality of 3D images in a freehand 3D ultrasound (US) system. Background and motivation: Freehand 3D US imaging has been playing an important role in obtaining the entire 3D impression of tissues and organs. Reconstructing a sequence of irregularly located 2D US images (B-scans) into a 3D data set is one of the key procedures for visualization and data analysis. Methods: In this study, we investigated the feasibility of using median filters for the reconstruction of 3D images in a freehand 3D US system. The B-scans were collected using a 7.5 MHz ultrasound probe. Four algorithms including the standard median (SM), Gaussian weighted median (GWM) and two types of distance-weighted median (DWM) filters were proposed to filter noises and compute voxel intensities. Qualitative and quantitative comparisons were made among the results of different methods based on the image set captured in freehand from the forearm of a healthy subject. A leave-one-out approach was used to demonstrate the performance of the median filters for predicting the removed B-scan pixels. Results: Compared with the voxel nearest-neighbourhood (VNN) and distance-weighted (DW) interpolation methods, the four median filters reduced the interpolation error by 8.0–24.0% and 1.2–21.8%, respectively, when 1/4 to 5 B-scans was removed from the raw B-scan sequence. Conclusions: In summary, the median filters can improve the quality of volume reconstruction by reducing the interpolation errors and facilitate the following image analyses in clinical applications.