Signal to noise ratio dependent postfilter combined with eigenspace-based minimum variance algorithm for ultrasound imaging

In order to improve the spatial resolution and contrast of ultrasound imaging, a signal to noise ratio-based postfilter hybrid eigenspace-based minimum variance algorithm is proposed. Firstly, the subspace dividing of signal is used to improve the contrast of the minimum variance method by projecting the weight vector of it into the signal subspace. Then, the factor of postfilter is calculated by the coherence of the signal, and a noise weighting factor based on signal to noise ratio is introduced. Finally, a signal to noise ratio-based postfilter combined with eigenspace-based minimum variance algorithm is obtained. In order to validate the proposed algorithm, we used FieldⅡ to simulate point target and the cyst phantom, and the experimental data of geabr_0 proposed by Michigan University was also used to imaging. Experiment results indicate that both the contrast and resolution are improved by the algorithm proposed,and the performance index is obviously superior to the traditional delay and sum method, minimum variance algorithm and the ESBMVwiener algorithm, and the algorithm is robust to noise.