Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming
| Autor: | Geoffrey D. Rubin, Yongkweon Jeon, Joong-Ho Won, Jarrett Rosenberg, Sandy Napel, Sungroh Yoon |
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| Rok vydání: | 2013 |
| Předmět: |
Optimization problem
Computer science Graphics processing unit Solid modeling Sensitivity and Specificity Article Pattern Recognition Automated User-Computer Interface CUDA Imaging Three-Dimensional Data visualization Image Interpretation Computer-Assisted Computer Graphics Humans Computer vision Aorta Abdominal Integer programming business.industry Angiography Reproducibility of Results Numerical Analysis Computer-Assisted Signal Processing Computer-Assisted Image Enhancement Computer Graphics and Computer-Aided Design Visualization Signal Processing Computer Vision and Pattern Recognition Artificial intelligence General-purpose computing on graphics processing units business Algorithms Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 19:81-93 |
| ISSN: | 1077-2626 |
| DOI: | 10.1109/tvcg.2012.25 |
| Popis: | Direct projection of three-dimensional branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single two-dimensional stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm. |
| Databáze: | OpenAIRE |
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