In color flow imaging, it is a challenging work to accurately extract blood flow information from ultrasound Doppler echoes dominated by the strong clutter components. In this paper, we provide an in-depth analysis of ridge ensemble empirical mode decomposition (R-EEMD) and compare it with the conventional empirical mode decomposition (EMD) framework. R-EEMD facilitates nonuniform and trial-dependent weights obtained by an optimization procedure during ensemble combination and results in less decomposition errors when compared with the conventional ensemble empirical mode decomposition techniques. A theoretic result is then extended to demonstrate that R-EEMD has an ability to solve the mode mixing problem frequently encountered in EMD and improve the decomposition performance with adequate noise strength when separating a composite two-tone signal. Based on the proposed R-EEMD framework, a novel clutter rejection filter for ultrasound color flow imaging is designed. In a series of simulations, the R-EEMD-based filter achieves a significant improvement on blood flow velocity estimation over the state-of-the-art regression filters and decomposition-based filters, such as eigen-based and EMD filters. An experiment on human carotid artery data also verifies that the R-EEMD algorithm achieves minimum clutter energy and maximum blood-to-clutter energy ratio among all the tested techniques.