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Zhiyan Liu

Publications and source records attributed to Zhiyan Liu.

3 recordsLinked to original sources
PubMed2006

Noise reduction for doppler ultrasound signal based on the adapted local cosine transform and the garrote thresholding method.

In this paper, a novel approach, using the adapted local cosine transform combined with the non-negative garrote thresholding, is proposed to remove noise from the Doppler ultrasound signal. In the proposed approach, the local cosine transform is first performed on the signal of interest followed by a search algorithm to select the best basis. Then the coefficients of the obtained best basis are thresholded based on the non-negative garrote thresholding method. By means of the thresholded coefficients of the best basis, the signal is reconstructed. In the simulation study, the estimation precisions of the mean frequency waveform and the spectral width waveform are studied for the signal after denoising. The simulation and clinical results have shown that the proposed approach is superior to ones based on the wavelet transform, especially under low signal-to-noise ratio (SNR) circumstances.

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PubMed2006

[Quadrature Doppler ultrasound signal denoising based on adapted local cosine transform].

The spectrogram of Doppler ultrasound signal has been widely used in clinical diagnosis. The additional frequency components arising from internal or external noise to the system will produce adverse effects on its subjective and quantitative analysis. A novel approach based on the adapted local cosine transform and the non-negative Garrote thresholding method was proposed to remove noise from quadrature Doppler signal. At first, the directional information was extracted from the quadrature signal. And then the denoising method based on the adapted local cosine transform is performed on the forward and backward flow signals, respectively. At last, the estimated signal was reconstructed from the denoised signals using Hilbert transform. In the simulation study, both the mean frequency and spectral width waveform were studied for the denoised signal. The simulation results had shown that this approach was superior to that based on the wavelet transform, especially under low SNR conditions.

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