title: Segmentation of the visible human for high-quality volume-based visualization.
abstract: 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.
authors.affiliation: Institute of Mathematics and Computer Science in Medicine (IMDM), University Hospital Hamburg-Eppendorf, Germany. schiemann@uke.uni-hamburg.de
authors.forename: T
authors.initials: T
authors.lastname: Schiemann
authors.forename: U
authors.initials: U
authors.lastname: Tiede
authors.forename: K H
authors.initials: KH
authors.lastname: Höhne
mesh_terms: D000005:Abdomen / Q000033:anatomy & histology*; D000465:Algorithms; D019412:Anatomy, Cross-Sectional; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008953:Models, Anatomic; D009141:Musculoskeletal System / Q000033:anatomy & histology*; D014057:Tomography, X-Ray Computed