Nonlinear Detection Research Articles

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.

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 filteringin simulated tissue mimicking phantom ultrasound B-mode images.

Effects of nonlinear signal detection on NMR relaxation time analysis

Accurate determination of multiple-component NMR relaxation times can be compromised by data acquisition nonlinearities which are not readily apparent from inspection of the relaxation data, The relaxation time estimates produced by two algorithms, DISCRETE and CONTIN, have been compared for simulated relaxation data corrupted by various amounts of nonlinearity for both linear and logarithmic data sampling. The accuracy of relaxation time estimates, particularly for two-component relaxation data, is seriously degraded by data nonlinearity. Recommendations are given for the maximum data nonlinearity for which accurate relaxation times can still be determined.