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He Y.,Scientific Computing and Imaging Institute | Hussaini M.Y.,Florida State University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents an optimal unified combination rule within the framework of the Dempster-Shafer theory of evidence to combine multiple bodies of evidence. It is optimal in the sense that the resulting combined m-function has the least dissimilarity with the individual m-functions and therefore represents the greatest amount of information similar to that represented by the original m-functions. Examples are provided to illustrate the proposed combination rule. © Springer International Publishing Switzerland 2014. Source

Tierny J.,Telecom ParisTech | Daniels II J.,New York University | Nonato L.G.,University of Sao Paulo | Pascucci V.,Scientific Computing and Imaging Institute | Silva C.T.,New York University
IEEE Transactions on Visualization and Computer Graphics | Year: 2012

Creating high-quality quad meshes from triangulated surfaces is a highly nontrivial task that necessitates consideration of various application specific metrics of quality. In our work, we follow the premise that automatic reconstruction techniques may not generate outputs meeting all the subjective quality expectations of the user. Instead, we put the user at the center of the process by providing a flexible, interactive approach to quadrangulation design. By combining scalar field topology and combinatorial connectivity techniques, we present a new framework, following a coarse to fine design philosophy, which allows for explicit control of the subjective quality criteria on the output quad mesh, at interactive rates. Our quadrangulation framework uses the new notion of Reeb atlas editing, to define with a small amount of interactions a coarse quadrangulation of the model, capturing the main features of the shape, with user prescribed extraordinary vertices and alignment. Fine grain tuning is easily achieved with the notion of connectivity texturing, which allows for additional extraordinary vertices specification and explicit feature alignment, to capture the high-frequency geometries. Experiments demonstrate the interactivity and flexibility of our approach, as well as its ability to generate quad meshes of arbitrary resolution with high-quality statistics, while meeting the user's own subjective requirements. © 1995-2012 IEEE. Source

Dey T.K.,Ohio State University | Janoos F.,Harvard University | Levine J.A.,Scientific Computing and Imaging Institute
Engineering with Computers | Year: 2011

In medical imaging, the generation of surface representations of anatomical objects obtained by labeling images from various modalities, is a critical component for visualization, simulation, and analysis. The interfaces between labeled regions can meet at arbitrary angles and with complex topologies, causing most automatic meshing algorithms to fail. We apply a recent Delaunay refinement algorithm to generate high quality triangular meshes that approximate the interface surfaces. This algorithm has proven guarantees for meshing piecewise-smooth shapes and its implementation overhead is low. Consequently, the approach is applicable to labeled datasets generated from binary segmentations as well as from probabilistic segmentation algorithms. We show the effectiveness of this technique on data from a variety of medical fields and discuss its ability to control the quality and size of the output meshes. The same algorithm can be used to generate tetrahedral meshes of the segmentation space. © 2011 Springer-Verlag London Limited. Source

Dey T.K.,Ohio State University | Levine J.A.,Scientific Computing and Imaging Institute | Slatton A.,Ohio State University
Computer Graphics Forum | Year: 2010

The technique of Delaunay refinement has been recognized as a versatile tool to generate Delaunay meshes of a variety of geometries. Despite its usefulness, it suffers from one lacuna that limits its application. It does not scale well with the mesh size. As the sample point set grows, the Delaunay triangulation starts stressing the available memory space which ultimately stalls any effective progress. A natural solution to the problem is to maintain the point set in clusters and run the refinement on each individual cluster. However, this needs a careful point insertion strategy and a balanced coordination among the neighboring clusters to ensure consistency across individual meshes. We design an octtree based localized Delaunay refinement method for meshing surfaces in three dimensions which meets these goals. We prove that the algorithm terminates and provide guarantees about structural properties of the output mesh. Experimental results show that the method can avoid memory thrashing while computing large meshes and thus scales much better than the standard Delaunay refinement method. Journal compilation © 2010 The Eurographics Association and Blackwell Publishing Ltd. Source

Nichols J.A.,University of Utah | Roach K.E.,University of Utah | Fiorentino N.M.,University of Utah | Anderson A.E.,University of Utah | Anderson A.E.,Scientific Computing and Imaging Institute
Gait and Posture | Year: 2016

Evidence suggests that the tibiotalar and subtalar joints provide near six degree-of-freedom (DOF) motion. Yet, kinematic models frequently assume one DOF at each of these joints. In this study, we quantified the accuracy of kinematic models to predict joint angles at the tibiotalar and subtalar joints from skin-marker data. Models included 1 or 3 DOF at each joint. Ten asymptomatic subjects, screened for deformities, performed 1.0 m/s treadmill walking and a balanced, single-leg heel-rise. Tibiotalar and subtalar joint angles calculated by inverse kinematics for the 1 and 3 DOF models were compared to those measured directly in vivo using dual-fluoroscopy. Results demonstrated that, for each activity, the average error in tibiotalar joint angles predicted by the 1 DOF model were significantly smaller than those predicted by the 3 DOF model for inversion/eversion and internal/external rotation. In contrast, neither model consistently demonstrated smaller errors when predicting subtalar joint angles. Additionally, neither model could accurately predict discrete angles for the tibiotalar and subtalar joints on a per-subject basis. Differences between model predictions and dual-fluoroscopy measurements were highly variable across subjects, with joint angle errors in at least one rotation direction surpassing 10° for 9 out of 10 subjects. Our results suggest that both the 1 and 3 DOF models can predict trends in tibiotalar joint angles on a limited basis. However, as currently implemented, neither model can predict discrete tibiotalar or subtalar joint angles for individual subjects. Inclusion of subject-specific attributes may improve the accuracy of these models. © 2016 Elsevier B.V. Source

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