Optimum nonlinear signal detection and estimation in the presence of ultrasonic speckle.

A unified approach to the design of nonlinear filters for speckle suppression in ultrasound B-mode images is presented. The detection of the (lesion) signal is formulated as a binary hypothesis-testing problem. The structure of the optimal decision rules is derived both in the case where the lesion signal is assumed either a constant or random variable. In the case of a constant signal, the maximum likelihood (ML) estimator and the optimal L-estimator are derived. In the case of a random lesion signal, the maximum a posteriori probability estimator of the lesion signal has also been found. Experimental results verify the superiority of the proposed ML-estimator and the L-estimator over the straightforward choice of an arithmetic mean for speckle filtering in simulated tissue mimicking phantom ultrasound B-mode images.