Paper Info
Title
Automatic de-noising of doppler ultrasound signals using matching pursuit method
Abstract
A novel de-noising method, called matching pursuit method, for improving the signal-to-noise ratio (SNR) of Doppler ultrasound blood flow signals is proposed. Using this method, the Doppler ultrasound signal is first decomposed into a linear expansion of waveforms, called time-frequency atoms, which are selected from a redundant dictionary named Gabor functions. Then a decay parameter-based algorithm is employed to determine the decomposition times. Finally, the de-noised Doppler signal is reconstructed using the selected components. The SNR improvements and the maximum frequency estimation precision with simulated Doppler blood flow signals have been used to evaluate a performance comparison based on the wavelet, the wavelet packets and the matching pursuit de-noising algorithms. From the simulation and clinical experiment results, it is concluded that the performance of the matching pursuit approach is the best for the Doppler ultrasound signal de-noising.
Year
DOI
Venue
2006
10.1007/11679363_65
ICA
Keywords
Field
DocType
matching pursuit method,snr improvement,doppler ultrasound signal,pursuit method,automatic de-noising,matching pursuit approach,matching pursuit,doppler ultrasound,de-noised doppler signal,performance comparison,simulated doppler blood flow,doppler ultrasound blood flow,doppler ultrasound signal de-noising,time frequency,signal to noise ratio,blood flow
Matching pursuit,Signal processing,Computer science,Discrete wavelet transform,Artificial intelligence,Doppler effect,Wavelet packet decomposition,Blind signal separation,Distributed computing,Wavelet,Pattern recognition,Signal-to-noise ratio,Speech recognition
Conference
Volume
ISSN
ISBN
3889
0302-9743
3-540-32630-8
Citations 
PageRank 
References 
1
0.36
4
Authors
5
Name
Order
Citations
PageRank
Yufeng Zhang111730.05
Le Wang210.36
Yali Gao3193.97
Jianhua Chen4329.00
xinling shi57415.34