Implementing Direct Volume Visualisation with Spatial Classification
Authors: D. Mueller, A.J. Maeder, P. O'Shea
Date: February 2005
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 medical diagnostic and therapy planning tasks. Volume rendering 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 (functions based on sample intensity and sample intensity gradient magnitude). However, such 2-D transfer functions inherently ignore spatial information. We present a novel modification to 3-D texture-based volume rendering allowing users to classify fuzzy-segmented, over-lapping regions with independent 2-D transfer functions. This modification improves direct volume rendering by allowing for more sophisticated classification using spatial information.
Publisher: Proceedings of the Australian Pattern Recognition Society (APRS) Workshop on Digital Image Computing (WDIC 2005), Brisbane, Australia, 12 February 2005, CD-ROM pp 49-53
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