A Ridge Ensemble Empirical Mode Decomposition Approach to Clutter Rejection for Ultrasound Color Flow Imaging

2013 ◽  
Vol 60 (6) ◽  
pp. 1477-1487 ◽  
Author(s):  
Zhiyuan Shen ◽  
Naizhang Feng ◽  
Yi Shen ◽  
Chin-Hui Lee
2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250013
Author(s):  
ZHIYUAN SHEN ◽  
NAIZHANG FENG ◽  
YI SHEN

Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method to solve the problem of mode mixing caused by empirical mode decomposition (EMD). It is shown that the decomposition error tends to zero, as ensemble number increases to infinity in EEMD. In this paper, a novel EEMD-based ridge regression model (REEMD) is proposed, which solves the problem of mode mixing and achieves less decomposition error compared with the EEMD. When the ensemble number is small, the weights of outliers are constraint to zero to reduce the decomposition error in REEMD and the result of REEMD is asymptotic to that of EEMD, as the ensemble number increases. The proposed REEMD is suitable for tissue clutter rejection in color flow imaging system. Simulation shows that reasonable flow-frequency estimations can be achieved by REEMD and the estimation error limits to zero, as the flow frequency increases.


Ultrasonics ◽  
2006 ◽  
Vol 44 ◽  
pp. e303-e305 ◽  
Author(s):  
Pei-Dong Wang ◽  
Yi Shen ◽  
Nai-Zhang Feng

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.


Author(s):  
L. Lovstakken ◽  
S. Bjaerum ◽  
K. Kristoffersen ◽  
R. Haaverstad ◽  
H. Torp

2011 ◽  
Vol 186 ◽  
pp. 141-145
Author(s):  
Cong Sheng Xie ◽  
Dong C. Liu

Traditional clutter rejection methods are based on the hypothesis that the clutter signal originates from stationary or moving tissue with a constant velocity, and they are not valid when there exists considerable acceleration of tissue motion. In this paper, we propose an advanced clutter filter adapted to accelerated tissue motion. It uses the instantaneous frequency to realize the DC remove, and uses the bandwidth of the clutter signal to choose the cutoff frequency of the wall filter, Simulation shows this advanced adaptive filter can efficiently attenuate the non-stationary clutter signals and improves the accuracy and the flexibility of the blood flow estimation.


Sign in / Sign up

Export Citation Format

Share Document