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TISSUE CHARACTERIZATION BY ULTRASOUND SPECKLE
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 033052, 2003 ASAE Annual Meeting . (doi: 10.13031/2013.15001) @2003Authors: Zhanqi Cheng, Jinglu Tan, Jeannie Kozak, Moses Hdeib
Keywords: speckle analysis, ultrasound images, texture descriptor, tissue characterization, segmentation, mutifractal, run length, morphological operation
Ultrasound speckle, which is caused by the interference of the backscattered ultrasound
signal, is considered to degrade image quality and is therefore unwanted in images. A variety of
research involving the statistical analysis of speckle has been performed and methods have thus
been developed to suppress or remove ultrasound speckle from images. Recent evidence has however shown that speckle contains useful information and is part of the signal. Research is
therefore currently needed to use speckle to assist recognition of formerly unrecognizable objects.
It is often difficult to differentiate various soft tissues in ultrasound images due to the similarities in
their physical (elastic) properties. Since speckle texture in ultrasound images is believed to contain
information about internal tissue structures, local area texture analysis was performed on speckle
images. Pixel-based features were obtained from multi-scale resolution analysis and pixel value run
length analysis. The features were analyzed for their ability to discriminate tissues. After feature
selection, multivariate classification techniques were used to differentiate beef muscle from pork
muscle in animal ultrasound images. Morphological operations were applied to produce better
segmentation. The neural network algorithm classified 84.3% of pixels into the correct class, while
using only the ultrasound signal in a similar algorithm correctly classified a mere 71.7%. These
results indicate that the developed algorithms can be used in soft tissue differentiation and can be
potentially used in medical ultrasound image to help with diagnosis.