Feature extraction from fetal blood flow Doppler ultrasound images using wavelet transforms

Wavelet transformations of images have the valuable properties of providing robust data compression and image details that can be used for discerning signal features at different resolutions. With the approach presented here, such features, represented by wavelet coefficients, are extracted from the variations of blood velocity waveforms obtained from Doppler ultrasound images of fetal umbilical arteries. The reliability of these features are tested and proven by image reconstruction from wavelet coefficients thresholded according to the required resolution. The obtained reliable features form the training samples of an artificial neural network to be used in intelligent diagnostics for fetal surveillance.<>