|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SPATIAL COMPOUNDING OF 3-D ULTRASOUND IMAGES
Robert Rohling, Andrew Gee and Laurence Berman
One of the most promising applications of 3-D ultrasound lies in the visualisation and volume estimation of internal 3-D structures. Unfortunately, the quality of the ultrasound data can be severely degraded by artifacts, especially speckle, making automatic analysis of the 3-D data sets very difficult. In this paper we investigate the use of 3-D spatial compounding to reduce speckle. We develop a new statistical theory to predict the improvement in signal to noise ratio with increased levels of compounding, and verify the predictions empirically. We also investigate how registration errors can affect automatic volume estimation of structures within the compounded 3-D data set. Having established the need to correct these errors, we present a novel reconstruction algorithm which uses landmarks to accurately register each B-scan as it is inserted into the voxel array. In a series of in-vitro and in-vivo trials, we demonstrate that 3-D spatial compounding is very effective for improving the signal to noise ratio, but correction of registration errors is essential.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.
|| Search | CUED | Cambridge University ||
2005 Cambridge University Engineering Dept
Information provided by milab-maintainer