Refine
Year of publication
Document Type
- In Proceedings (165)
- Article (134)
- Book chapter (16)
- Other (14)
- In Collection (9)
- Book (3)
- Poster (3)
Has Fulltext
- no (344) (remove)
Institute
- Visual and Data-centric Computing (326)
- Visual Data Analysis (318)
- Visual Data Analysis in Science and Engineering (227)
- Therapy Planning (74)
- Image Analysis in Biology and Materials Science (61)
- Numerical Mathematics (21)
- Vergleichende Visualisierung (12)
- Computational Medicine (4)
- Geometric Data Analysis and Processing (4)
- Distributed Algorithms and Supercomputing (2)
3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model
(2009)
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.
We consider the spectral proper orthogonal decomposition (SPOD) for experimental data of a turbulent swirling jet. This newly introduced method combines the advantages of spectral methods, such as Fourier decomposition or dynamic mode decomposition, with the energy-ranked proper orthogonal decomposition (POD). This poster visualizes how the modal energy spectrum transitions from the spectral purity of Fourier space to the sparsity of POD space. The transition is achieved by changing a single parameter – the width of the SPOD filter. Each dot in the 3D space corresponds to an SPOD mode pair, where the size and color indicates its spectral coherence. What we notice is that neither the Fourier nor the POD spectrum achieves a clear separation of the dynamic phenomena. Scanning through the graph from the front plane (Fourier) to the back plane (POD), we observe how three highly coherent SPOD modes emerge from the dispersed Fourier spectrum and later branch out into numerous POD modes.
The spatial properties of these three individual SPOD modes are displayed in the back of the graph using line integral convolution colored by vorticity. The first two modes correspond to single-helical global instabilities that are well known for these flows. Their coexistence, however, has not been observed until now. The third mode is of double- helical shape and has not been observed so far. For this considered data set and many others, the SPOD is superior in identification of coherent structures in turbulent flows. Hopefully, it gives access to new fluid dynamic phenomena and enriches the available methods.
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 employ the approach for longitudinal analysis of 2D rat skulls shapes as well as 3D shapes derived from an imaging study on osteoarthritis. Particularly, we perform hypothesis test and estimate the mean trends.
Large longitudinal studies provide lots of valuable information, especially in medical applications. A problem which must be taken care of in order to utilize their full potential is that of correlation between intra-subject measurements taken at different times. For data in Euclidean space this can be done with hierarchical models, that is, models that consider intra-subject and between-subject variability in two different stages. Nevertheless, data from medical studies often takes values in nonlinear manifolds. Here, as a first step, geodesic hierarchical models have been developed that generalize the linear ansatz by assuming that time-induced intra-subject variations occur along a generalized straight line in the manifold. However, this is often not the case (e.g., periodic motion or processes with saturation). We propose a hierarchical model for manifold-valued data that extends this to include trends along higher-order curves, namely Bézier splines in the manifold.
To this end, we present a principled way of comparing shape trends in terms of a functional-based Riemannian metric.
Remarkably, this metric allows efficient, yet simple computations by virtue of a variational time discretization requiring only the solution of regression problems.
We validate our model on longitudinal data from the osteoarthritis initiative, including classification of disease progression.
The use of tangible interfaces for navigation of landscape scenery – for example, lost places re-created in 3D – has been pursued and articulated as a promising, impactful application of interactive visualization. In this demonstration, we present a modern, low-cost implementation of a previously-realized multimodal gallery installation. Our demonstration centers upon the versatile usage of a smartphone for sensing, navigating, and (optionally) displaying element on a physical surface in tandem with a larger, more immersive display.
The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.