Path line decomposition for visualizing three-dimensional unsteady vector fields.
详细信息
- 作者：Lee ; Daren Andrew.
- 学历：Doctor
- 年：2001
- 导师：Karplus, Walter J.
- 毕业院校：University of California
- 专业：Computer Science.
- ISBN：0493151109
- CBH：3005940
- Country：USA
- 语种：English
- FileSize：6150315
- Pages：198
文摘
Intracranial aneurysms, balloon-like expansions arising from a weakened cerebral vessel, are the primary cause of subarachnoid hemorrhaging or bleeding in the brain. The blood irritates the vessels on the surface of the brain causing them to close, resulting in conditions such as a stroke. Difficulties in identifying which aneurysms will grow and rupture arise because the physicians lack important 3D anatomic and flow dynamic information. Through simulation, this data can be captured, but visualization of large simulated data sets becomes cumbersome and tedious, often resulting in visual clutter, ambiguity, and confusion.;To address these visualization issues, we developed an automated algorithm that decomposes the patterns of 3D, unsteady blood flow into behavioral components to reduce the visual complexity while retaining the structure and information of the original data. Our novel approach analyzes path lines, the trajectories of particles released in the flow. The path lines are grouped together using clustering techniques based on spatial locality and shape similarity criteria. Our decomposition methodology applies an initial clustering to identify global similarity followed by an adaptive thresholding procedure to locally refine each component grouping. The adaptive thresholding technique is based on the convergence of path line groups to obtain the largest and tightest cluster. The decomposed path line components can then be visualized individually or superimposed together to formulate a rich understanding of the flow patterns in the aneurysm.;Our path line decomposition visualization technique was applied to the blood flow simulation of an aneurysm extracted from real patient data. A panel of expert interventional radiologists and neurosurgeons evaluated the quality and clinical usefulness of the visualization results. Our preliminary study indicates that the decomposed groupings retain a high degree of likeness and correctness to the original data. The study also showed that the path line decomposition technique has the potential to be useful in conveying flow information in a clinical setting.