Automatic de-noising of doppler ultrasound signals using matching pursuit method

  • Authors:

  • Yufeng Zhang;Le Wang;Yali Gao;Jianhua Chen;Xinling Shi

  • Affiliations:

  • Department of Electronic Engineering, Yunnan University, Kunming, Yunnan Province, The People’s Republic of China;Department of Electronic Engineering, Yunnan University, Kunming, Yunnan Province, The People’s Republic of China;Department of Electronic Engineering, Yunnan University, Kunming, Yunnan Province, The People’s Republic of China;Department of Electronic Engineering, Yunnan University, Kunming, Yunnan Province, The People’s Republic of China;Department of Electronic Engineering, Yunnan University, Kunming, Yunnan Province, The People’s Republic of China

  • Venue:
  • ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
  • Year:
  • 2006

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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.