Ultrasound elastography is a non-invasive method which images the elasticity of soft-tissues. To make an image, pre and after a small compression, ultrasound radio frequency (RF) signals are acquired and the time delays between them are estimated. The first differentiation of displacement estimations is called elastogram. In this study, we are going to make an elastogram using the processing method named empirical mode decomposition (EMD). EMD is an analytic technique which decomposes a complicated signal to a collection of simple signals called intrinsic mode functions (IMFs). The idea of paper is using these IMFs instead of primary RF signals. To implement the algorithms two different datasets selected. The first one was data from a sandwich structure of normal and cooked tissue. The second dataset consisted of around 180 frames acquired from a malignant breast tumor. For displacement estimating, two different methods, cross-correlation and wavelet transform, were used too and for evaluating the quality, two conventional parameters, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculated for each image. Results show that in both methods after using EMD the quality improves. In first dataset and cross correlation technique CNR and SNR improve about 16 dB and 9 dB respectively. In same dataset by using wavelet technique, the parameters show 14 dB and 10 dB improvement respectively. In second dataset (breast tumor data) CNR and SNR in cross correlation method improve 18 dB and 7 dB and in wavelet technique improve 17 dB and 6 dB respectively.
Keywords: Elastogram; elastography; empirical mode decomposition; intrinsic mode function.