NDLI: Two-dimensional least-squares estimation for motion tracking in ultrasound elastography

Content Provider	IEEE Xplore Digital Library
Author	Basarab, A. Gueth, P. Liebgott, H. Delachartre, P.
Copyright Year	2007
Description	Author affiliation: CNRS UMR 5220, Lyon (Basarab, A.; Gueth, P.)
Abstract	This paper proposes a method of 2-D translations estimation using an a priori signal model. Two analytical signals defined with multidimensional Hilbert transform are considered and shown to have linear phases with respect to the translations to estimate. A least squares estimator (LSE) is then developed to adjust the measured phases of the complex signals to their theoretical forms. Moreover, the LSE provides an analytical solution to the 2-D translation estimation problem. The estimator is then included in a block matching method for motion tracking with ultrasound images. We compared our results with those obtained with a classical sum of absolute differences (SAD) cost function. We show that with our method there is no need of interpolating the images. Thus, for images at the original resolution level, the results obtained with the proposed estimator are largely more accurate than with SAD. Moreover, we show that using SAD on images with resolution five times higher provide roughly the same results as with our method, but the processing time is ten times higher in this case.
Starting Page	2155
Ending Page	2158
File Size	316913
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4352749
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion estimation Tracking Ultrasonic imaging Phase estimation Least squares approximation Image resolution Signal analysis Multidimensional systems Phase measurement Ultrasonic variables measurement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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