Based on wavelet domain Hidden Markov model, a novel speckle suppression method for medical ultrasound images is presented which combines Bayesian estimation and homomorphic filtering.
在此基于小波域隐马尔可夫树模型，将贝叶斯估计和同态滤波思想有机结合，提出一种新的医学超声图像去噪方法。
A nonlinear filtering method based on principle component analysis (PCA) was proposed according to the statistical characteristics of the Doppler ultrasound blood flow signal and wall thump signal.
根据超声多普勒血流信号和血管壁搏动信号的统计特性，提出了一种基于主元分析的非线性滤波方法。
This novel method, together with the traditional high-pass filtering method, is applied to both computer simulated Doppler ultrasound signals and collected human carotid Doppler ultrasound signals.
对计算机仿真的超声多普勒信号和采集的人体颈总动脉多普勒信号分别应用该方法，并和传统的高通滤波器方法进行比较。
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