A global to local interactive exploration is provided using hierarchical camera views that are created for subtrees within the structure.
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The user can also interactively modify segment placement within the 2D embedding, and the overall embedding will update accordingly. The final embedding is generated by minimizing an energy function (the weights of which are user adjustable) based on branch length and the 2D angles, while avoiding intersections. Camera views are created for individual segments and are used to determine local bending angles at each node by projecting them to 2D. We present a novel camera view generation method which maximizes the visible geometric attributes (segment shape and relative placement between segments). Our method maintains the original geometry, without overlaps, to the best extent possible, allowing exploration of the topology within a single view. We describe a method to create a planar embedding of 3D treelike structures using their skeleton representations.
The growing complexity of spatial and structural information in 3D data makes data inspection and visualization a challenging task. We evaluate our method both qualitatively and quantitatively and demonstrate our results by constructing planar visualizations of line data (traced neurons) and volume data (CT vascular and bronchial data
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. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. 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.
This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. 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. 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.
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