Heterogeneous modeling of medical image data using B-spline functions

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Bio-modeling, tissue density variations, heterogeneous modeling, B-spline surfaces

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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.

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Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, v. 226, issue 10, p. 737-751