  High-dimensional transfer function design is widely used to provide
appropriate data classification for direct volume rendering of various
datasets. However, its design is a complicated task. Parallel coordinate plot
(PCP), as a powerful visualization tool, can efficiently display
high-dimensional geometry and accurately analyze multivariate data. In this
paper, we propose to combine parallel coordinates with dimensional reduction
methods to guide high-dimensional transfer function design. Our pipeline has
two major advantages: (1) combine and display extracted high-dimensional
features in parameter space; and (2) select appropriate high-dimensional
parameters, with the help of dimensional reduction methods, to obtain
sophisticated data classification as transfer function for volume rendering. In
order to efficiently design high-dimensional transfer functions, the
combination of both parallel coordinate components and dimension reduction
results is necessary to generate final visualization results. We demonstrate
the capability of our method for direct volume rendering using various CT and
MRI datasets.
