Nonlinear adaptive filters for speckle suppression in ultrasonic images

A novel approach to suppression of ultrasonic speckle based on a combination of segmentation and optimum L-filtering is presented. With the aid of a suitable modification of the learning vector quantizer (LVQ) neural network, the image is segmented in regions of(approximately) homogeneous statistics. For each of the regions a minimum mean-squared-error (MMSE) L-filter is designed, by using the histogram of grey levels as an estimate of the parent distribution of the noisy observations and a suitable estimate of the (assumed constant) original signal in the corresponding region, Thus, a bank of L-filters results; with each of them corresponding to and operating on a different image region. Simulation results from both simulated and real B-mode ultrasonic images are presented, which verify the (qualitative and quantitative) superiority of our technique over a number of commonly used speckle filters.