  For medical volume visualization, one of the most important tasks is to
reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the
coronary arteries, without obscuring them with less significant parts. These
volume datasets contain different materials which are difficult to extract and
visualize with 1D transfer functions based solely on the attenuation
coefficient. Multi-dimensional transfer functions allow a much more precise
classification of data which makes it easier to separate different surfaces
from each other. Unfortunately, setting up multi-dimensional transfer functions
can become a fairly complex task, generally accomplished by trial and error.
This paper explains neural networks, and then presents an efficient way to
speed up visualization process by semi-automatic transfer function generation.
We describe how to use neural networks to detect distinctive features shown in
the 2D histogram of the volume data and how to use this information for data
classification.
