Computed tomography and magnetic resonance imaging in the medical
area deliver huge amounts of data, doctors have to handle in short time.
This data can be visualised efficiently with direct volume rendering. Consequentially
most direct volume rendering applications on the market are
specialised on medical tasks or integrated in medical visualisation environments.
Highly evolved applications for tasks like diagnosis or surgery
simulation are available in this area.
In the last years however another group detected the use of computed
tomography. Companies like phoenix|x-ray, founded in 1999 produce ctscanners
especially dedicated to industrial applications like non destructive
material testing (NDT). Of course an application like NDT has different
demands to the visualisation than a typical medical application. For
example a typical task for non destructive testing would be to highlight
air inclusions in a casting. These inclusions usually cover a very small area
and are very hard to classify only based on their density value as this would
also highlight the air around the casting.
This research paper presents multiple approaches to improve the rendering
of industrial ct-data, most of them basing on higher dimensional
transfer functions. Therefore the existing volume renderer application was
extended with a user interface to create such transfer functions and existing
render modes were adapted to profit from the new transfer functions.
These approaches are especially suited to improve the visualisation of surfaces
and material boundaries as well as inclusions. The resulting renderings
make it very easy to identify these features while preserving interactive
framerates.

Clipping into a cast housing. Left: unshaded clipping plane. The Surface is completely scrambled, no information regarding the clipping surface can be gathered Right: shaded clipping plane. The surface is clearly visible. Inclusions can easily be found.

2D Transfer Function and different histograms Left: the panel. Right: density / gradient magnitude histogram, blobs on the bottom represent homogeneous areas, connecting arches the boundaries of two areas

Left: Mirrored lo-hi histogram, blobs on the bisector represent homogenous areas, blobs in the upper or lower half represent one side of a boundary. Right: Density / feature size histogram, every line in the histogram represents areas of similar sizes.

Shaded DVR renderings of a weld seam Left:  Using density / gradient magnitude based transfer function. Right: Using Lo Hi transfer function. Results are very similar, however lo-hi computation takes a lot preprocessing time, but transfer functions are a bit easier to create

Feature Size based Rendering of a cast housing. Left: Shaded DVR rendering with 1D density transfer function (gradient magnitude modulated). Right: Shaded DVR rendering with 2D density / feature size transfer function for the inclusions and a 1D density based transfer function for the housing itsel(gradient magnitude modulated).