Tumors are typically analyzed as a single unit, despite their biologically heterogeneous nature. This limits correlations that can be drawn between regional variation and treatment outcome. Furthermore, despite the availability of high resolution 3D medical imaging techniques, local outcomes, (e.g. tumor growth), are not easily measured. This paper proposes a method that uses streamlines to divide a 3D region of interest (e.g. tumor) into units where local properties can be measured over the paths of growth. Parameters such as directional length and mean intensity can be measured locally at sequential time points and then compared. The method is evaluated on synthetic objects, simulated tumors, and medical images of brain tumors. The evaluations suggest that the method is suitable for mapping amorphous dynamic objects.