TY - GEN
T1 - Multiscale motion analysis of the carotid artery wall from B-mode ultrasound
T2 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
AU - Tsiaparas, N. N.
AU - Gastounioti, A.
AU - Golemati, S.
AU - Nikita, K. S.
PY - 2013
Y1 - 2013
N2 - The incorporation of wavelet-based multiscale image decomposition in motion-estimation schemes has been shown to have a favourable impact on accuracy in tracking motion of the carotid artery wall from B-mode ultrasound image sequences. In this work, in an attempt to further enhance accuracy, we investigate the effects of different parameters of multiscale image decomposition. To this end, we optimize multiscale weighted least-squares optical flow (MWLSOF), a previously presented multiscale motion estimator, in terms of (a) the type of wavelet transform (WT) (discrete (DWT) and stationary (SWT) WTs), (b) the WT function and (c) the total number of levels of image decomposition. The optimization is performed in the context of an in silico data framework, consisting of simulated ultrasound image sequences of the carotid artery. We propose SWT, a high-order coiflet function (ex. coif5) and one level of multiscale image decomposition as the optimal parameterization for MWLSOF to achieve maximum accuracy in the particular application. Finally, we demonstrate the usefulness of an accurate motion estimator in real data experiments, by applying the optimized MWLSOF to real image data of patients with carotid atherosclerosis.
AB - The incorporation of wavelet-based multiscale image decomposition in motion-estimation schemes has been shown to have a favourable impact on accuracy in tracking motion of the carotid artery wall from B-mode ultrasound image sequences. In this work, in an attempt to further enhance accuracy, we investigate the effects of different parameters of multiscale image decomposition. To this end, we optimize multiscale weighted least-squares optical flow (MWLSOF), a previously presented multiscale motion estimator, in terms of (a) the type of wavelet transform (WT) (discrete (DWT) and stationary (SWT) WTs), (b) the WT function and (c) the total number of levels of image decomposition. The optimization is performed in the context of an in silico data framework, consisting of simulated ultrasound image sequences of the carotid artery. We propose SWT, a high-order coiflet function (ex. coif5) and one level of multiscale image decomposition as the optimal parameterization for MWLSOF to achieve maximum accuracy in the particular application. Finally, we demonstrate the usefulness of an accurate motion estimator in real data experiments, by applying the optimized MWLSOF to real image data of patients with carotid atherosclerosis.
UR - http://www.scopus.com/inward/record.url?scp=84894149488&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2013.6701676
DO - 10.1109/BIBE.2013.6701676
M3 - Conference contribution
AN - SCOPUS:84894149488
SN - 9781479931637
T3 - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
BT - 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
Y2 - 10 November 2013 through 13 November 2013
ER -