Soft tissue structure modelling for use in orthopaedic applications and musculoskeletal biomechanics

We present our methodology for the three-dimensional anatomical and geometrical description of soft tissues, relevant for orthopaedic surgical applications and musculoskeletal biomechanics. The technique involves the segmentation and geometrical description of muscles and neurovascular structures from high-resolution computer tomography scanning for the reconstruction of generic anatomical models. These models can be used for quantitative interpretation of anatomical and biomechanical aspects of different soft tissue structures. This approach should allow the use of these data in other application fields, such as musculoskeletal modelling, simulations for radiation therapy, and databases for use in minimally invasive, navigated and robotic surgery

A Two-dimensional HLLC Riemann Solver for Conservation Laws : Application to Euler and MHD Flows

In this paper we present a genuinely two-dimensional HLLC Riemann solver. On logically rectangular meshes, it accepts four input states that come together at an edge and outputs the multi-dimensionally upwinded fluxes in both directions. This work builds on, and improves, our prior work on two-dimensional HLL Riemann solvers. The HLL Riemann solver presented here achieves its stabilization by introducing a constant state in the region of strong interaction, where four one-dimensional Riemann problems interact vigorously with one another. A robust version of the HLL Riemann solver is presented here along with a strategy for introducing sub-structure in the strongly-interacting state. Introducing sub-structure turns the two-dimensional HLL Riemann solver into a two-dimensional HLLC Riemann solver. The sub-structure that we introduce represents a contact discontinuity which can be oriented in any direction relative to the mesh. The Riemann solver presented here is general and can work with any system of conservation laws. We also present a second order accurate Godunov scheme that works in three dimensions and is entirely based on the present multidimensional HLLC Riemann solver technology. The methods presented are cost-competitive with traditional higher order Godunov schemes

3-D modeling from range imagery: an incremental method with a planning component

In this paper we present a method for automatically constructing a CAD model of an unknown object from range images. The method is an incremental one that interleaves a sensing operation that acquires and merges information into the model with a planning phase to determine the next sensor position or "view". This is accomplished by integrating a system for 3-D model acquisition with a sensor planner. The model acquisition system provides facilities for range image acquisition, solid model construction and model merging: both mesh surface and solid representations are used to build a model of the range data from each view, which is then merged with the model built from previous sensing operations. The planning system utilizes the resulting incomplete model to plan the next sensing operation by finding a sensor viewpoint that will improve the fidelity of the model. Experimental results are presented for a complex part that includes polygonal faces, curved surfaces, and large self-occlusions

A robotic system for 3D model acquisition from multiple range images

This paper describes a robotic system that builds a 3D CAD model of an object incrementally from multiple range images. It motivates the generation of a solid model at each stage of the modeling process, allowing the use of well-defined geometric algorithms to perform the merging and integration task. The data from each imaging operation is represented by a mesh, which is then extruded in the viewing direction to form a solid model. These solids are merged as they are acquired into a composite model of the object. We describe an algorithm that builds a solid model from a mesh surface and present experimental results of reconstructing a complex object. In addition, we discuss an approach to completely automating the model acquisition process by integration with previous sensor-planning results

Computing a Compact Spline Representation of the Medial Axis Transform of a 2D Shape

We present a full pipeline for computing the medial axis transform of an arbitrary 2D shape. The instability of the medial axis transform is overcome by a pruning algorithm guided by a user-defined Hausdorff distance threshold. The stable medial axis transform is then approximated by spline curves in 3D to produce a smooth and compact representation. These spline curves are computed by minimizing the approximation error between the input shape and the shape represented by the medial axis transform. Our results on various 2D shapes suggest that our method is practical and effective, and yields faithful and compact representations of medial axis transforms of 2D shapes.Comment: GMP14 (Geometric Modeling and Processing