Abstract
This paper describes a new multi-scale approach for the extraction of iso-surfaces from volume datasets. The goal is to automatically identify iso-surfaces that best approximate the boundary surfaces at different levels of details. Using histogram analysis, iso-values are extracted from histograms of boundary voxels defined by gradient thresholding or zero-crossing boundaries. Multi-scale smoothing of the histogram using Gaussian filters of various sizes allows the iso-surfaces to be defined hierarchically over a scale space map. It provides an interactive environment and volume navigation tools for the exploration of large volume datasets by visualizing the layers of the volume space in a multi-scale manner.
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