![]() SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). ![]() A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging.
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