Maximizing image variance in rendering of volumetric data sets

Abstract

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.