Implementing Direct Volume Visualisation with Spatial Classification

Abstract

Direct volume rendering (DVR) provides medical users with insight into datasets by creating a 3-D representation from a set of 2-D image slices (such as CT or MRI). This visualisation technique has been used to aid various medi-cal diagnostic and therapy planning tasks. Volume render-ing has recently become faster and more affordable with the advent of 3-D texture-mapping on commodity graphics hardware. Current implementations of the DVR algorithm on such hardware allow users to classify sample points (known as "voxels") using 2-D transfer functions (func-tions based on sample intensity and sample intensity gradi-ent magnitude). However, such 2-D transfer functions in-herently ignore spatial information. We present a novel modification to 3-D texture-based volume rendering allow-ing users to classify fuzzy-segmented, overlapping regions with independent 2-D transfer functions. This modification improves direct volume rendering by allowing for more sophisticated classification using spatial information.