In this report, we explore the strengths and limitations of principal component analysis (PCA) and independent component analysis (ICA) for clutter and noise filtering in ultrasonic peripheral perfusion imaging. The advantages of pre-filtering spatial registration to reduce the bandwidth of coherent clutter motion is also considered. PCA methods excel when the echo covariance exhibits a significant blood-scattering component orthogonal to the tissue clutter component. This situation exists in peripheral perfusion imaging when the echo signals are temporally stationary and normally distributed. ICA methods separate non-orthogonal blood-clutter echo components often found in moving clutter, but only for echo signals with either non-normal-amplitude distributions or nonstationary normal distributions. When clutter movement is large and spatially coherent, echo registration followed by PCA filtering can be ideal. Effective filtering is essential for contrast-free ultrasonic perfusion imaging of muscle tissues in the extremities of patients at risk for developing peripheral artery diseases. Statistical filter performance is examined using simulation and echo data from an in vivo ischemic hindlimb mouse model.