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Parameterization and Feature Extraction for the Visualization of Tree-like Structures
Full paper
The study and visualization of vascular structures, using 3D models obtained from medical data, is an active field of research. Illustrative visualizations have been applied to this domain in multiple ways. Researchers have tried to make the geometric properties of vasculature more comprehensive and to augment the surface with representations of multivariate clinical data. Techniques that head beyond the application of color-maps or simple shading approaches require a sort of surface parameterization, i.e., texture coordinates, in order to overcome locality. When extracting 3D models, the computation of texture coordinates on the mesh is not always part of the data processing pipeline. We combine existing techniques to a simple, yet effective, parameterization approach that is suitable for tree-like structures. The parameterization is done w.r.t. to a pre-defined source vertex. For this, we present an automatic algorithm, that detects the root of a tree-structure. The parameterization is partly done in screen-space and recomputed per frame. However, the screen-space computation comes with positive features that are not present in object-space approaches. We show how the resulting texture coordinates can be used for varying hatching, contour parameterization, the display of decals, as an additional depth cue and feature extraction.
Estimation of Muscle Activity in One-Leg Stance from 3D Surface Deformation
Full paper
Muscular activity during human motion is usually quantified by measuring the electrical potential during muscle activation using electromyography (EMG). However, apart from producing electrical activity, muscular contraction of many skeletal muscles also induces subtle deformation of the skin surface. In this paper, we present a method to estimate muscular activation from such 3D skin deformation. To this end, we introduce a capture system that reconstructs the 3D motion of the skin from multi-view video data and simultaneously measures true muscle activity with EMG sensors. Our data reveals strong correlations between the skin deformation and muscular activity during one-leg stances. We propose a pose normalization procedure and a novel model based on Supervised Principal Component Regression that automatically segments individual muscles and estimates their activation from 3D surface deformation. Our evaluation shows that the model generalizes to varying body shapes and that the estimated activation closely fits the measured EMG data.
VisualFlatter – Visual Analysis of Uncertainties in the Projection of Biomedical Structures
Full paper
Projections of complex anatomical or biological structures from 3D to 2D are often used by visualization and domain experts to facilitate inspection and understanding. Representing complex structures, such as organs or molecules, in a simpler 2D way often requires less interaction, while enabling comparability. However, the most commonly employed projection methods introduce size or shape distortions, in the resulting 2D representations. While simple projections display known distortion patterns, more complex projection algorithms are not easily predictable. We propose the VisualFlatter, a visual analysis tool that enables visualization and domain experts to explore and analyze projection-induced distortions, in a structured way. Our tool provides a way to identify projected regions with semantically relevant distortions and allows users to comparatively analyze distortion outcomes, either from alternative projection methods or due to different setups through the projection pipeline. The user is given the ability to improve the initial projection configuration, after comparing different setups. We demonstrate the functionality of our tool using four scenarios of 3D to 2D projections, conducted with the help of domain or visualization experts working on different application fields. We also performed a wider evaluation with 13 participants, familiar with projections, to assess the usability and functionality of the Visual Flatter.
Global and Local Mesh Morphing for Complex Biological Objects from microCT Data
Short paper
We show how biologically coherent mesh models of animals can be created from microCT data to generate artificial yet naturally looking intermediate objects. The whole pipeline of processing algorithms is presented, starting from generating topologically equivalent surface meshes, followed by solving the correspondence problem, and, finally, creating a surface morphing. In this pipeline, we address all the challenges that are due to dealing with complex biological, non-isometric objects. For biological objects it is often particularly important to obtain deformations that look as realistic as possible. In addition, spatially non-uniform shape morphings that only change one part of the surface and keep the rest as stable as possible are of interest for evolutionary studies, since functional modules often change independently from one another. We use Poisson interpolation for this purpose and show that it is well suited to generate both global and local shape deformations.