基于小波阈值的胸腔低频超声信号降噪处理

Denoising of Low-frequency Ultrasound Signal in Thoracic Cavity Based on Wavelet Threshold

采用低频超声胸腔成像技术对实际的人体胸腔进行信号采集时,超声换能器接收到的胸腔信号中会含有大量的噪声干扰,较大地影响了成像效果。因此文章通过COMSOL平台建立二维胸腔模型,在接收到的胸腔信号中添加信噪比为2dB高斯白噪声作为模拟噪声。通过小波阈值降噪技术对含噪的胸腔信号进行处理,发现db7小波基在分解层数为1层,阈值准则为minimaxi,软阈值处理函数时可以有效降噪,提高数据的信噪比。

When low-frequency ultrasound thoracic imaging technology is used for signal acquisition of the actual human thoracic cavity, the thoracic cavity signal received by the ultrasound transducer will contain a large amount of noise interference, which greatly affects the imaging effect. Therefore, in this paper, a two-dimensional thoracic cavity model is established by COMSOL platform, and the received thoracic cavity signal is added with 2 dB Gaussian white noise with a signal-to-noise ratio of 2 dB as the analog noise. The noise-containing thoracic cavity signal is processed by wavelet threshold noise reduction technology, and it is found that the db7 wavelet basis can effectively reduce the noise and improve the signal-to-noise ratio of the data when the number of decomposition layers is 1, the threshold criterion is minimaxi, and the soft threshold is used to process the function.