  Many state-of-the art visualization techniques must be tailored to the
specific type of dataset, its modality (CT, MRI, etc.), the recorded object or
anatomical region (head, spine, abdomen, etc.) and other parameters related to
the data acquisition process. While parts of the information (imaging modality
and acquisition sequence) may be obtained from the meta-data stored with the
volume scan, there is important information which is not stored explicitly
(anatomical region, tracing compound). Also, meta-data might be incomplete,
inappropriate or simply missing.
  This paper presents a novel and simple method of determining the type of
dataset from previously defined categories. 2D histograms based on intensity
and gradient magnitude of datasets are used as input to a neural network, which
classifies it into one of several categories it was trained with. The proposed
method is an important building block for visualization systems to be used
autonomously by non-experts. The method has been tested on 80 datasets, divided
into 3 classes and a "rest" class.
  A significant result is the ability of the system to classify datasets into a
specific class after being trained with only one dataset of that class. Other
advantages of the method are its easy implementation and its high computational
performance.
