Intracranial aneurysms are the primary cause of non-traumatic subarachnoid hemorrhage. Difficulties in identifying which aneurysms will grow and rupture arise because the physicians lack important anatomic and hemodynamic information. Through simulation, this data can be captured, but visualization of large simulated data sets becomes cumbersome, often resulting in visual clutter and ambiguity. To address these visualization issues, we developed an algorithm that extracts a skeleton of the patterns in 3D, time-dependent blood flow. The algorithm decomposes the blood flow into "bare-bones" components that can be visualized individually or superimposed together to formulate an understanding of the flow patterns in the aneurysm.