Li Haiyan, Ma Yue, Zhang Yufeng, Shu Xinling
School of Information Science and Engineering, Yunnan University, Kunming 650091, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Oct;28(5):886-90.
To reduce background noise and Dopplar speckle in the spectrogram of ultrasound Doppler blood flow signals, a novel method, called Matching Pursuit with threshold decaying pulse coupled neural network (MP-PCNN), has been proposed. The proposed method used an iterative algorithm, which decomposed the ultrasound Doppler signals into linear expansion of atoms in a time-frequency dictionary by using the Matching Pursuit (MP) for de-noising the ultrasound Doppler signal. Subsequently, a simplified unidirectional pulse coupled neural network was applied to calculate the firing matrix of the denoised spectrogram. The Doppler speckles were located and removed through analyzing and processing the PCNN firing matrix. Experiments were conducted on simulation signals which SNRs were 0dB, 5dB and 10dB. The result showed that the MP-PCNN performed effectively in reducing noise, eliminating Doppler speckles, and achieved better performance than exiting noise and speckle suppression algorithm for Doppler ultrasound blood flow spectrogram.
为降低超声多普勒血流信号频谱图中的背景噪声和多普勒斑点,提出了一种名为带阈值衰减脉冲耦合神经网络的匹配追踪(MP-PCNN)的新方法。该方法采用迭代算法,通过匹配追踪(MP)将超声多普勒信号分解为时频字典中原子的线性展开,以对超声多普勒信号进行去噪。随后,应用简化的单向脉冲耦合神经网络计算去噪频谱图的点火矩阵。通过对PCNN点火矩阵进行分析和处理来定位和去除多普勒斑点。对信噪比分别为0dB、5dB和10dB的模拟信号进行了实验。结果表明,MP-PCNN在降低噪声、消除多普勒斑点方面表现有效,并且在多普勒超声血流频谱图的噪声和斑点抑制方面比现有算法具有更好的性能。