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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.
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)
Visualization of human anatomy in a 3D atlas requires both spatial and more abstract symbolic knowledge. Within our " intelligent volume " model which integrates these two levels, we developed and implemented a semantic network model for describing human anatomy. Concepts for structuring (abstraction levels, domains , views, generic and case-specific(More)
Objectives: A profound knowledge of anatomy and surgical landmarks of the temporal bone is a basic necessity for any otologic surgeon. Since this knowledge, so far, is mostly taught by limited temporal bone drilling courses, our objective was to create a system for virtual petrous bone surgery, which allows the realistic simulation of specific laterobasal(More)
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)