Statistical evaluation of clutter filters in color flow imaging.

The filter used to separate blood signals from the tissue clutter signal is an important part of a color flow system. In this paper, statistical detection theory is used to evaluate the quality of the most commonly used clutter filters. The probability of falsely classifying a sample volume as containing blood is kept below a specified threshold. With this constraint, the probability of correctly detecting blood is calculated for all the filters. Using a measured clutter signal, we found that polynomial regression filters and projection-initialized IIR filters are best among the commonly used filters. The probability of correctly detecting blood with velocity 10.1 cm/s was 0.32 for both these filters. The corresponding value for the optimal detector was 0.81, whereas a regression filter that depends on the clutter signal statistics achieved a blood detection probability of 0.72.