ZHAO Yujie1， YAN Shangqu2， HE Jinglin1， LI Jixiang1， ZOU Xiao1， QIAN Shengyou1
（1. School of Physics and Electronics, Hunan Normal University, Changsha 410081, China；2. College of Electronic Science, National University of Defense Technology, Changsha 410073, China）
Abstract： High intensity focused ultrasound (HIFU) has been widely used in the biomedical field, and the noise processing for the HIFU echo signal has been a very critical problem. In order to obtain a purer and clearer HIFU echo signal, we propose a hybridt denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), minimum mean square variance criterion (MMSVC) and wavelet threshold (WT), which is called ICEEMDAN-MMSVC-WT. The ICEEMDAN can decompose the signal into a finite number of intrinsic mode functions (IMFs), which can avoid spurious modes and reduce the amount of noise contained in the modes. MMSVC is used to identify all IMFs by ICEEMDAN and divide these IMFs into two parts. The high frequency IMF components are denoised by WT first, and then combined with the low frequency IMF components to obtain the final denoised signal. In the experiments of simulation signal and actual HIFU echo signal, in comparison with other methods, the proposed denoising method retains the useful signal to the maximum extent, and removes the noise component largely, which has better denoising effect and application value.
Key words： high intensity focused ultrasound (HIFU); echo signal； improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)； minimum mean square variance criterion (MMSVC)； wavelet threshold (WT)
References
［1］NING Z Y, ZHU Z F, WANG H Y, et al. High-intensity focused ultrasound enhances the effect of bufalin by inducing apoptosis in pancreatic cancer cells. OncoTargets and Therapy, 2019, 12: 1161-1170.
［2］ZHOU Y, JI X, NIU J M, et al. Ultrasound-guided high-intensity focused ultrasound for devascularization of uterine fibroid: a feasibility study. Ultrasound in Medicine and Biology, 2021, 47(9): 2622-2635.
［3］LI X L, ZHU X G, HE S L, et al. High-intensity focused ultrasound in the management of adenomyosis: long-term results from a single center. International Journal of Hyperthermia, 2021, 38(1): 241-247.
［4］YAN S Q, ZHANG H, LIU B, et al. Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment. Chinese Physics B, 2021, 30(2): 694-700.
［5］SUN Z L, LU J G. An ultrasonic signal denoising method for EMU wheel trackside fault diagnosis system based on improved threshold function. IEEE Access, 2021, 9: 96244-96256.
［6］LIN P R, KUANG W C, LIU Y W, et al. Grouping and selecting singular spectrum analysis components for denoising via empirical mode decomposition approach. Circuits, Systems, and Signal Processing, 2019, 38(1): 356-370.
［7］WANG S J, HOU L, ZHANG J Q. Noise reduction using wavelet transform in ultrasonic flaw detection of small-diameter steel pipe with thick wall. Advanced Materials Research, 2012, 383-390: 4755-4761.
［8］ZHU Y L, XU C G, XIAO D G. Denoising ultrasonic echo signals with generalized S transform and singular value decomposition. Traitement du Signal, 2019, 36(2): 139-145.
［9］JIAO Y K, LI Z W, ZHU J C, et al. ABIDE: A novel scheme for ultrasonic echo estimation by combining CEEMD-SSWT method with EM algorithm. Sustainability, 2022, 14(4): 1960.
［10］FENG W, ZHOU X J, ZENG X, et al. Ultrasonic flaw echo enhancement based on empirical mode decomposition. Sensors, 2019, 19(2): 236.
［11］HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time 14series analysis. Proceedings of the Royal Society Amathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
［12］JIA Y H, LI G L, DONG X, et al. A novel denoising method for vibration signal of hob spindle based on EEMD and grey theory. Measurement, 2021, 169: 108490.
［13］SUN M M, LI Z C, LI Z N, et al. A noise attenuation method for weak seismic signals based on compressed sensing and CEEMD. IEEE Access, 2020, 8: 71951-71964.
［14］XIONG F, CHEN D Y. CEEMDAN-IMFx-PCA-CICA: An improved single-channel blind source separation in multimedia environment for motion artifact reduction in ambulatory ECG. Complex & Intelligent Systems, 2020. https://doi.org/10.1007/s40747-20-00188-7.
［15］COLOMINAS M A, SCHLOTTHAUER G, TORRES M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 2014, 14(11): 19-29.
［16］THURAISINGHAM R A. Revisiting ICEEMDAN and EEG rhythms. Biomedical Signal Processing and Control, 2021, 68: 102701.
［17］FENG L, LI J, LI C Q, et al. A blind source separation method using denoising strategy based on ICEEMDAN and improved wavelet threshold. Mathematical Problems in Engineering, 2022, 2022: 1-9.
［18］KONSTANTIN D, DOMINIQUE Z. Variational mode decomposition. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
［19］KAUR C, BISHT A, SINGH P, et al. EEG signal denoising using hybrid approach of variational mode decomposition and wavelets for depression. Biomedical Signal Processing and Control, 2021, 65: 102337.
［20］LI G H, YANG Z C, YANG H. Noise reduction method of underwater acoustic signals based on uniform phase empirical mode decomposition, amplitude-aware permutation entropy, and pearson correlation coefficient. Entropy, 2018, 20(12): 918.
［21］LI Y X, LI Y A, CHEN X, et al. A new underwater acoustic signal denoising technique based on CEEMDAN, mutual information, permutation entropy, and wavelet threshold denoising. Entropy, 2018, 20(8): 563.
［22］LI Y X, LI Y A, CHEN X, et al. Research on ship-radiated noise denoising using secondary Variational Mode Decomposition and correlation coefficient. Sensors, 2018, 18(1): 48.
［23］GUO R, ZHAO Z Q, WANG T, et al. Degradation state recognition of piston pump based on ICEEMDAN and XGBoost. Applied SciencesBasel, 2020, 10(18): 6593.
［24］LI Y X, WANG L. A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise, minimum mean square variance criterion and least mean square adaptive filter. Defence Technology, 2020, 16(3): 543-554.
［25］LU Q, PANG L X, HUANG H Q, et al. High-G calibration denoising method for High-G MEMS accelerometer based on EMD and wavelet threshold. Micromachines, 2019, 10(2): 134.
［26］HU H P, AO Y, YAN H C, et al. Signal denoising based on wavelet threshold denoising and optimized variational mode decomposition. Journal of Sensors, 2021, 2021(4): 1-23.
［27］LIU N, ZHANG R W, SU Z, et al. Research on wavelet threshold denoising method for UWB tunnel personnel motion location. Mathematical Problems in Engineering, 2020, 2020: 1-14.
［28］MATZ V, SMID R, STARMAN S, et al. Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing. Ultrasonics, 2009, 49(8): 752-759.
基于ICEEMDAN结合MMSVC和WT的HIFU回波信号联合去噪算法
赵雨洁1， 颜上取2， 贺京琳1， 李吉祥1， 邹孝1， 钱盛友1
（1. 湖南师范大学 物理与电子科学学院， 湖南 长沙 410081；2. 国防科技大学 电子科学学院， 湖南 长沙 410073）
摘要：高强度聚焦超声(High intensity focused ultrasound, HIFU)已广泛应用于生物医学领域， 其回波信号中的噪声处理是一个非常关键的问题。 为了获得更纯净、更清晰的HIFU回波信号， 提出了一种基于改进的完全自适应噪声集成经验模态分解(Improved complete ensemble empirical mode decomposition with adaptive noise， ICEEMDAN)、最小均方方差准则(Minimum mean square variance criterion, MMSVC)和小波阈值(Wavelet threshold, WT)的联合去噪方法。 ICEEMDAN将信号分解为有限个本征模态函数(Intrinsic mode functions, IMF)， 从而避免杂散模态， 减少模态中所含的噪声。 MMSVC用于识别被ICEEMDAN分解得到的所有IMF， 并将这些IMF分为两部分， 高频IMF部分通过WT进行去噪， 之后与低频IMF分量重构得到最终去噪信号。 在仿真信号的实验中， 与其他方法相比， 本文所描述的基于ICEEMDAN-MMSVC-WT的降噪方法最大限度地保留了有用信号， 大量去除了噪声成分， 因而具有更好的去噪效果和应用价值。
关键词：高强度聚焦超声； 回波信号；改进的完全自适应噪声集成经验模态分解；最小均方方差准则；小波阈值
引用格式：ZHAO Yujie， YAN Shangqu， HE Jinglin， et al． A hybrid denoising algorithm for HIFU echo signal based on ICEEMDAN combined with MMSVC and WT． Journal of Measurement Science and Instrumentation， 2023， 14（1）： 35-44. DOI： 10．3969／j．issn．1674-8042．2023．01．005
[full text view]
