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Proceedings Paper

Exploration of 3D medical data using combined emission and reflection volume rendering
Author(s): Klaus D. Toennies; Sandra Bock
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Paper Abstract

Visualization aids in the evaluation of a 3-D data set, if a -- perhaps imperfect -- separability of different objects by their function value is assumed. An interpretation without explicit analysis can be carried out by reflection/transmission (RT) volume rendering where voxels, depending on their surface probability, reflect and transmit light. Color coding voxels in RT rendering in order to attribute the surface to a specific object is not easy because the voxel value depends on the unknown objects meeting at the surface. This problem does not arise, if an emission/absorption (EA) method is applied. Emission and absorption of colored light are associated with the probability of a voxel belonging to a certain object. We combined both approaches to exploit the RT renderer's ability to reveal surface details and the EA renderer's ability to differentiate objects. A user-defined operator decides whether a voxel belongs to the background, to a surface, or to an object. Background voxels are skipped. Object voxels contribute to accumulated light emission. Surface voxels reflect light from an outside light source. Color is attributed only to object voxels, but it is possible to let colored object voxels shine through semi-transparent surface voxels. CT sequences were visualized in order to display structures such as the ventricular system or areas of pathological processes. Although the differentiation of these structures was not complete, the display showed enough detail to convey the relevant shape to the user.

Paper Details

Date Published: 15 April 1996
PDF: 8 pages
Proc. SPIE 2707, Medical Imaging 1996: Image Display, (15 April 1996);
Show Author Affiliations
Klaus D. Toennies, Technische Univ. Berlin (Germany)
Sandra Bock, Technische Univ. Berlin (Germany)

Published in SPIE Proceedings Vol. 2707:
Medical Imaging 1996: Image Display
Yongmin Kim, Editor(s)

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