This article describes a combination of interactive classification and super-sampling visualization algorithms that greatly enhances the realism of 3-D reconstructions of the Visible Human data sets. Objects are classified on the basis of ellipsoidal regions in RGB space. The ellipsoids are used for super-sampling in the visualization process.
For high quality rendering of objects segmented from tomographic volume data the precise location of the boundaries of adjacent objects in subvoxel resolution is required. We describe a new method that determines the membership of a given sample point to an object by reclassifying the sample point using interpolation of the original intensity values and… (More)
Multi-slice images obtained from computer tomog-raphy and magnetic resonance imaging represent a three-dimensional image volume. For its visu-alization we use a ray-casting algorithm working on a gray scMe voxel data model. This model is extended by additional attributes such as membership to an organ or a second imaging modality ("generalized voxel… (More)
Computerized three-dimensional models of the human body, based on the Visible Human Project of the National Library of Medicine, so far do not reflect the rich anatomical detail of the original cross-sectional images. In this paper, a spatial/symbolic model of the inner organs is developed, which is based on more than 1000 cryosections and congruent fresh… (More)