title: Heterogeneous modeling of medical image data using B-spline functions.
abstract: Biomedical data visualization and modeling rely predominately on manual processing and utilization of voxel- and facet-based homogeneous models. Biological structures are naturally heterogeneous and it is important to incorporate properties, such as material composition, size and shape, into the modeling process. A method to approximate image density data with a continuous B-spline surface is presented. The proposed approach generates a density point cloud, based on medical image data to reproduce heterogeneity across the image, through point densities. The density point cloud is ordered and approximated with a set of B-spline curves. A B-spline surface is lofted through the cross-sectional B-spline curves preserving the heterogeneity of the point cloud dataset. Preliminary results indicate that the proposed methodology produces a mathematical representation capable of capturing and preserving density variations with high fidelity.
authors.affiliation: Moffitt Cancer Research Center, University of South Florida, Tampa 33620, USA.
authors.forename: Olya
authors.initials: O
authors.lastname: Grove
authors.forename: Khairan
authors.initials: K
authors.lastname: Rajab
authors.forename: A
authors.initials: A
authors.lastname: Les Piegl
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D009716:Numerical Analysis, Computer-Assisted