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.
Algorithms ; Artifacts ; Blood Flow Velocity ; physiology ; Humans ; Neural Networks (Computer) ; Rheology ; Signal Processing, Computer-Assisted ; Ultrasonography, Doppler, Pulsed ; methods