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Green Image
Issue No. 01 - Jan. (2013 vol. 19)
ISSN: 1077-2626
pp: 81-93
S. Napel , Dept. of Radiol., Stanford Univ., Stanford, CA, USA
G. D. Rubin , Dept. of Radiol., Stanford Univ., Stanford, CA, USA
Sungroh Yoon , Dept. of Electr. & Comput. Eng., Seoul Nat. Univ., Seoul, South Korea
J. K. Rosenberg , Dept. of Radiol., Stanford Univ., Stanford, CA, USA
Yongkweon Jeon , Dept. of Electr. & Comput. Eng., Seoul Nat. Univ., Seoul, South Korea
Joong-Ho Won , Sch. of Ind. Manage. Eng., Korea Univ., Seoul, South Korea
ABSTRACT
Direct projection of 3D 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 2D 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.
INDEX TERMS
proteins, biomedical MRI, computerised tomography, data visualisation, geometry, graphics processing units, integer programming, linear programming, medical image processing, parallel processing, GPU, uncluttered single-image visualization, vascular structures, GPGPU technology, general-purpose graphics processing unit technology, commodity-level parallelism, abdominal aortic vessel tree, anatomical variants, protein structure prediction problem, geometry optimization technique, USIV, 2D stylistic image, magnetic resonance angiography, computed tomography, tomographic images, abdominal aorta visualization, structure visualization, 3D branching structures direct projection, integer linear programming-based formulation, Visualization, Three dimensional displays, Biomedical imaging, Optimization, Context, Solid modeling, Graphics processing unit, CUDA, proteins, biomedical MRI, computerised tomography, data visualisation, geometry, graphics processing units, integer programming, linear programming, medical image processing, parallel processing, GPU, uncluttered single-image visualization, vascular structures, GPGPU technology, general-purpose graphics processing unit technology, commodity-level parallelism, abdominal aortic vessel tree, anatomical variants, protein structure prediction problem, geometry optimization technique, USIV, 2D stylistic image, magnetic resonance angiography, computed tomography, tomographic images, abdominal aorta visualization, structure visualization, 3D branching structures direct projection, integer linear programming-based formulation, Visualization, Three dimensional displays, Biomedical imaging, Optimization, Context, Solid modeling, Graphics processing unit, parallelization, Single-image visualization, abdominal aorta, side-chain placement, integer linear programming, GPGPU
CITATION
S. Napel, G. D. Rubin, Sungroh Yoon, J. K. Rosenberg, Yongkweon Jeon, Joong-Ho Won, "Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming", IEEE Transactions on Visualization & Computer Graphics, vol. 19, no. , pp. 81-93, Jan. 2013, doi:10.1109/TVCG.2012.25
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