Refine
Year of publication
Document Type
- ZIB-Report (30)
Language
- English (30)
Has Fulltext
- yes (30) (remove)
Is part of the Bibliography
- no (30)
Keywords
- DVR (2)
- 3D neural network (1)
- Cultural Heritage (1)
- DNA (1)
- Dense connectome (1)
- GPU acceleration (1)
- Gaussian Process (1)
- HCI (1)
- Interaction Techniques (1)
- Kendall (1)
- Kiosk application (1)
- Medical image segmentation (1)
- Optimal design of computer experiments (1)
- Picking (1)
- Picture/Image Generation, Display algorithms, Three-Dimensional Graphics and Realism, Raytracing (1)
- Procrustes analysis (1)
- RNA (1)
- Reconstruction (1)
- Shape Space (1)
- Sparse kernels (1)
- Surfaces (1)
- Volume Rendering (1)
- atomic radii (1)
- blue-noise (1)
- brushing & linking (1)
- cavity analysis (1)
- computational geometry (1)
- digitally reconstructed radiographs (1)
- direct volume rendering (1)
- dislocation, diffraction contrast, scanning transmission electron microscopy, stereoscopy, visualization (1)
- finite element meshes (1)
- iPad (1)
- image registration (1)
- interaction (1)
- interactive rendering (1)
- mesh deformation (1)
- molecular dynamics (1)
- multi-modal, intergrated data analysis, topology (1)
- picking (1)
- point set optimization (1)
- pointing (1)
- ray casting (1)
- ribonucleic acids (1)
- secondary & tertiary structures (1)
- shape space, shape trajectories, geodesic regression, longitudinal analysis, osteoarthritis (1)
- statistical shape and intensity models (1)
- triangulation (1)
- unfolding, papyri, computed tomography (1)
- virtual anatomy (1)
- volume rendering (1)
Institute
- Visual Data Analysis (30) (remove)
For medical diagnosis, visualization, and model-based therapy planning three-dimensional geometric reconstructions of individual anatomical structures are often indispensable. Computer-assisted, model-based planning procedures typically cover specific modifications of “virtual anatomy” as well as numeric simulations of associated phenomena, like e.g. mechanical loads, fluid dynamics, or diffusion processes, in order to evaluate a potential therapeutic outcome. Since internal anatomical structures cannot be measured optically or mechanically in vivo, three-dimensional reconstruction of tomographic image data remains the method of choice. In this work the process chain of individual anatomy reconstruction is described which consists of segmentation of medical image data, geometrical reconstruction of all relevant tissue interfaces, up to the generation of geometric approximations (boundary surfaces and volumetric meshes) of three-dimensional anatomy being suited for finite element analysis. All results presented herein are generated with amira ® – a highly interactive software system for 3D data analysis, visualization and geometry reconstruction.
Radiologists from all application areas are trained to read slice-based visualizations of 3D medical image data. Despite the numerous
examples of sophisticated three-dimensional renderings, especially all variants of direct volume rendering, such methods are
often considered not very useful by radiologists who prefer slice-based visualization. Just recently there have been attempts to bridge this
gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking that result in repositioning slices.
In this paper, we present a new volume picking technique that, in contrast to previous work, does not require pre-segmented data or metadata. The positions picked by our method are solely based on the data itself, the transfer function and,
most importantly, on the way the volumetric rendering is perceived by viewers. To demonstrate the usefulness of the proposed method
we apply it for automatically
repositioning slices in an abdominal MRI
scan, a data set from a flow simulation and a number of other volumetric scalar fields. Furthermore we discuss how the method can be implemented in combination with various different volumetric rendering techniques.
In this paper we describe VisiTrace, a novel technique to
draw 3D lines in 3D volume rendered images. It allows to
draw strokes in the 2D space of the screen to produce 3D
lines that run on top or in the center of structures actually
visible in the volume rendering. It can handle structures
that only shortly occlude the structure that has been visible at the starting point of the stroke and is able to ignore
such structures. For this purpose a shortest path algorithm
finding the optimal curve in a specially designed graph
data structure is employed. We demonstrate the usefulness of the technique by applying it to MRI data from
medicine and engineering, and show how the method can
be used to mark or analyze structures in the example data
sets, and to automatically obtain good views toward the
selected structures.
This paper presents an algorithm called surfseek for selecting surfaces
on the most visible features in direct volume rendering (DVR). The
algorithm is based on a previously published technique (WYSIWYP) for
picking 3D locations in DVR. The new algorithm projects a surface patch
on the DVR image, consisting of multiple rays. For each ray the algorithm
uses WYSIWYP or a variant of it to find the candidates for the
most visible locations along the ray. Using these candidates the algorithm
constructs a graph and computes a minimum cut on this graph. The minimum
cut represents a very visible but relatively smooth surface. In the
last step the selected surface is displayed. We provide examples for the
results in real-world dataset as well as in artificially generated datasets.
In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spatiotemporal fields, to assess different simulations and to compare simulations with observations. A significant information reduction is possible by focusing on geometric-topological features of the fields or on derived meteorological objects. Due to the huge size of the data sets, spatial features have to be extracted in time slices and traced over time. Fields with chaotic component, i.e. without 1:1 spatiotemporal correspondences, can be compared by looking upon statistics of feature properties. Feature extraction, however, requires a clear mathematical definition of the features – which many meteorological objects still lack. Traditionally, object extractions are often heuristic, defined only by implemented algorithms, and thus are not comparable. This work surveys our framework designed for efficient development of feature tracking methods and for testing new feature definitions. The framework supports well-established visualization practices and is being used by atmospheric researchers to diagnose and compare data.
Statistical methods to design computer experiments usually rely on a Gaussian process (GP) surrogate model, and typically aim at selecting design points (combinations of algorithmic and model parameters) that minimize the average prediction variance, or maximize the prediction accuracy for the hyperparameters of the GP surrogate.
In many applications, experiments have a tunable precision, in the sense that one software parameter controls the tradeoff between accuracy and computing time (e.g., mesh size in FEM simulations or number of Monte-Carlo samples).
We formulate the problem of allocating a budget of computing time over a finite set of candidate points for the goals mentioned above. This is a continuous optimization problem, which is moreover convex whenever the tradeoff function accuracy vs. computing time is concave.
On the other hand, using non-concave weight functions can help to identify sparse designs. In addition, using sparse kernel approximations drastically reduce the cost per iteration of the multiplicative weights updates that can be used to solve this problem.
This work introduces a novel streamline seeding technique based on dual streamlines that are orthogonal to the vector field, instead of tangential. The greedy algorithm presented here produces a net of orthogonal streamlines that is iteratively refined resulting in good domain coverage and a high degree of continuity and uniformity. The algorithm is easy to implement and efficient, and it naturally extends to curved surfaces.
In this paper, we propose a new method for optimizing the blue noise characteristics of point sets. It is based on Procrustes analysis, a technique for adjusting shapes to each other by applying optimal elements of an appropriate transformation group. We adapt this technique to the problem at hand and introduce a very simple, efficient and provably convergent point set optimizer.
For Kendall’s shape space we determine analytically Jacobi fields and parallel transport, and compute geodesic regression. Using the derived expressions, we can fully leverage the geometry via Riemannian optimization and reduce the computational expense by several orders of magnitude. The methodology is demonstrated by performing a longitudinal statistical analysis of epidemiological shape data.
As application example we have chosen 3D shapes of knee bones, reconstructed from image data of the Osteoarthritis Initiative. Comparing subject groups with incident and developing osteoarthritis versus normal controls, we find clear differences in the temporal development of femur shapes. This paves the way for early prediction of incident knee osteoarthritis, using geometry data only.
In many applications, geodesic hierarchical models are adequate for the study of temporal observations. We employ such a model derived for manifold-valued data to Kendall's shape space. In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics and apply the approach for the estimation of group trends and statistical testing of 3D shapes derived from an open access longitudinal imaging study on osteoarthritis.