Low-Frequency Ultrasound Thoracic Signal Processing Based on Music Algorithm and EMD Wavelet Thresholding

Ultrasound is widely used in biomedical imaging. High-frequency ultrasound at MHz is usually used for good resolution. However, ultrasound at this band cannot permeate human thorax, for it is strongly scattered and reflected by air inclusions in the thorax. Low-frequency ultrasound can bypass this limitation and capture useful information deep within the thorax. Hence, this paper presents a new signal processing method to enhance the signal quality of low-frequency ultrasound imaging by improving the signal-to-noise ratio (SNR) and reducing the mean square error (MSE). Specifically, we propose a solution that integrates the empirical mode decomposition (EMD)-based wavelet thresholding algorithm with the multiple signal classification (MUSIC) algorithm. The MUSIC algorithm pre-processes the low-frequency ultrasound signal prior to implementing the EMD-based wavelet threshold denoising algorithm for further noise reduction. We assess the effectiveness of our approach through simulation and experimental research. Results demonstrate substantial performance improvements in the quality of low-frequency ultrasound signals with SNR enhancement ranging from 2.2915 to 3.3358 dB in the simulation, and MSE decrease of 0.3018. Moreover, the experiment yields an SNR improvement range of 1.0274 to 2.9372 dB and a corresponding decrease of 0.2202 in MSE. In agreement with the simulation outcomes, these results confirm the efficacy of our proposed method as an efficient solution for improving the quality of low-frequency ultrasound signals.