Fast detection of meaningful isosurfaces for volume data visualization

Automatic detection of meaningful isosurfaces is important for producing informative visualizations of volume data, especially when no information about the data origin and imaging protocol is available. We propose a computationally efficient method for the automated detection of intensity transitions in volume data. In this approach, the dominant transitions correspond to clear maxima in cumulative Laplacian-weighted gray value histograms. Only one pass through the data volume is required to compute the histogram. Several other features which may be useful for exploration of data of unknown origin can be efficiently computed in a similar manner. The detected intensity transitions can be used for setting of visualization parameters for surface rendering, as well as for direct volume rendering of 3D datasets. When using surface rendering, the detected dominant intensity transition values correspond to the optimal surface isovalues for extraction of boundaries of the objects of interest. In direct volume rendering, such transitions are important for generation of the transfer functions, which are used to assign visualization properties to data voxels and determine the appearance of the rendered image. The proposed method is illustrated by examples with synthetic data as well as real biomedical datasets.