NONLINEAR ADAPTIVE FILTERS FOR SPECKLE SUPPRESSION IN ULTRASONIC IMAGES

A novel approach to suppression of ultrasonic speckle based on a combi nation 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) ho mogeneous statistics. For each of the regions a minimum mean-squared-e rror (MMSE) L-filter is designed, by using the histogram of grey level s as an estimate of the parent distribution of the noisy observations and a suitable estimate of the (assumed constant) original signal in t he corresponding region, Thus, a bank of L-filters results; with each of them corresponding to and operating on a different image region. Si mulation results from both simulated and real B-mode ultrasonic images are presented, which verify the (qualitative and quantitative) superi ority of our technique over a number of commonly used speckle filters.