An algorithm is presented for rendering of volumetric data sets. The aim of the algorithm is to maximize the image variance in a volumetric rendering where a three-dimensional data set is projected onto a view plane through the perspective mapping. The pixel values in the rendered image are associated with a variable size attribute vector extracted along a line in the volumetric data set. Several algorithms are presented for transforming this variable size attribute vector into a fixed size attribute vector. The fixed size attribute vectors provide a multispectral image representation, which is processed with the Karhunen-Loève transformation in order to separate the information content into orthogonal components that are ordered according to the associated eigenvalues. The components in the Karhunen-Loève transform can be displayed individually
as intensity images or three components can be selected and mapped into a coloring scheme such as the hue-saturation-value color model.