In this paper, we present a novel method of visualizing flow in blood vessels. Our approach reads unstructured tetrahedral
data, resamples it, and uses slice based 3D texture volume rendering. Due to the sparse structure of blood vessels, we
utilize an octree to efficiently store the resampled data by discarding empty regions of the volume. We use animation to
convey time series data, wireframe surface to give structure, and utilize the StarCAVE, a 3D virtual reality environment, to
add a fully immersive element to the visualization.
Our tool has great value in interdisciplinary work, helping scientists collaborate with clinicians, by improving the
understanding of blood flow simulations. Full immersion in the flow field allows for a more intuitive understanding of the
flow phenomena, and can be a great help to medical experts for treatment planning.
We propose a new algorithm for automatic viewpoint selection for volume data sets. While most previous algorithms
depend on information theoretic frameworks, our algorithm solely focuses on the data itself without off-line rendering
steps, and finds a view direction which shows the data set's features well. The algorithm consists of two main steps:
feature selection and viewpoint selection. The feature selection step is an extension of the 2D Harris interest point detection
algorithm. This step selects corner and/or high-intensity points as features, which captures the overall structures and local
details. The second step, viewpoint selection, takes this set and finds a direction that lays out those points in a way
that the variance of projected points is maximized, which can be formulated as a Principal Component Analysis (PCA)
problem. The PCA solution guarantees that surfaces with detected corner points are less likely to be degenerative, and it
minimizes occlusion between them. Our entire algorithm takes less than a second, which allows it to be integrated into
real-time volume rendering applications where users can modify the volume with transfer functions, because the optimized
viewpoint depends on the transfer function.
The design of transfer functions for volume rendering is a difficult task. This is particularly true for multichannel
data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for
transfer function design. Our new method provides a framework to combine multiple approaches and pushes
the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of
transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually
in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties
of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear
dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle
Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly
improve the transfer function design process without compromising visualization accuracy. In this publication
we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal
microscopy data.
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