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Proceedings Paper

Automatic assessment of ultrasound image usability
Author(s): Luca Valente; Gareth Funka-Lea; Jeffrey Stoll
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Paper Abstract

We present a novel and efficient approach for evaluating the quality of ultrasound images. Image acquisition is sensitive to skin contact and transducer orientation and requires both time and technical skill to be done properly. Images commonly suffer degradation due to acoustic shadows and signal attenuation, which present as regions of low signal intensity masking anatomical details and making the images partly or totally unusable. As ultrasound image acquisition and analysis becomes increasingly automated, it is beneficial to also automate the estimation of image quality. Towards this end, we present an algorithm that classifies regions of an image as usable or un-usable. Example applications of this algorithm include improved compounding of free-hand 3D ultrasound volumes by eliminating unusable data and improved automatic feature detection by limiting detection to only usable areas. The algorithm operates in two steps. First, it classifies the image into bright areas, likely to have image content, and dark areas, likely to have no content. Second, it classifies the dark areas into unusable (i.e. due to shadowing and/or signal loss) and usable (i.e. anatomically accurate dark regions, such as with a blood vessel) sub-areas. The classification considers several factors, including statistical information, gradient intensity and geometric properties such as shape and relative position. Relative weighting of factors was obtained through the training of a Support Vector Machine. Classification results for both human and phantom images are presented and compared to manual classifications. This method achieves 91% sensitivity and 91% specificity for usable regions of human scans.

Paper Details

Date Published: 14 March 2011
PDF: 7 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623Y (14 March 2011); doi: 10.1117/12.878339
Show Author Affiliations
Luca Valente, Siemens Corp. Research (United States)
Gareth Funka-Lea, Siemens Corp. Research (United States)
Jeffrey Stoll, Siemens Healthcare (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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