Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging
(PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality
more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that
target the early detection of problematic flow conditions. Segmentation is one component of this type of application that
can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem
that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and
can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present
a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour
modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise.
The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital
heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the
adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF
significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these
results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should
be used instead of scalar techniques.