|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SPECKLE DETECTION IN ULTRASOUND IMAGES USING FIRST ORDER STATISTICS
R. W. Prager, A. H. Gee, G. M. Treece and L. Berman
It is necessary to identify speckled regions in ultrasound images to control adaptive speckle suppression algorithms, for tissue characterisation, and to estimate the elevational separation of B-scans by speckle decorrelation. Previous authors have proposed classification techniques based on second order powers of the homodyned k-distribution, or lower order powers of the more limited k-distribution. In this paper we explore the speckle discrimination properties of statistics based on arbitrary powers of the ultrasound echo envelope signal using a combination of simulations and theoretical results from the homodyned k-distribution. We conclude that statistics based on powers less than one are surprisingly less effective than some higher powers. A simple discriminant function for speckle is evaluated quantitatively in simulation and qualitatively on sample B-scan images.
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2005 Cambridge University Engineering Dept
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