1,721,152 research outputs found
Message from the Paper Chairs and Guest Editors
No full textThis special issue includes papers that were presented at the IEEE Scientific Visualization Conference 2012 (SciVis 2012) and the IEEE Information Visualization Conference 2012 (InfoVis 2012), held together at IEEE VisWeek from 14-19 October 2012 in Seattle, WA
Kombinierte Visualisierung von EEG- und Diffusions-MRT-Nervenfaser-Daten
Full text linkZiel dieser Diplomarbeit ist die Entwicklung einer interaktiven Visualisierung von EEG-Daten und deren Quellen in Kombination mit Nervenfaserbündeldaten. Dazu soll als Softwaregrundlage das derzeit in Entwicklung befindliche OpenWalnut genutzt werden. Dabei handelt es sich um einen Softwarerahmen zur medizinischen Visualisierung mit Schwerpunkt auf die interaktive Darstellung von Gehirndaten. Die Darstellung und Selektion von Nervenfaserbündeln wurde darin bereits implementiert. Damit bietet es sich an, dieses System als Grundlage zu nehmen
Image Space Tensor Field Visualization Using a LIC-like Method
Full text linkTensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to
show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature.
We introduce a novel approach to compute a fabric-like texture pattern from tensor fields on arbitrary non-selfintersecting surfaces that is motivated by image space line integral convolution (LIC). Our main focus lies on regaining three-dimensionality of the data under user interaction, such as rotation and scaling. We employ a multi-pass rendering approach to estimate proper modification of the LIC noise input texture to support the three-dimensional perception during user interactions
Entfernen von Knoten in Graphen
Full text linkWerden in einem Graphen Knoten entfernt, so müssen auch alle Kanten entfernt werden, die diesen Knoten beinhalten. Dies kann dazu führen, dass Graphen nicht mehr zusammenhängend sind oder sich die Pfadlänge zwischen zwei Knoten verlängert. Um diesen Problemen entgegen zu wirken, müssen entsprechend der Graphenstruktur neue Kanten gezogen werden. Im Rahmen dieser Bachelorarbeit wurde ein Algorithmus entwickelt, der diese Kanten nach festen Regeln erstellt und somit die Struktur eines Graphen erhält, auch wenn Knoten aus diesem entfernt werden
Gaussian Processes for Uncertainty Visualization
Full text linkData is virtually always uncertain in one way or another. Yet, uncertainty information is not routinely included in visualizations and, outside of simple 1D diagrams, there is no established way to do it. One big issue is to find a method that shows the uncertainty without completely cluttering the display. A second important question that needs to be solved, is how uncertainty and interpolation interact. Interpolated values are inherently uncertain, because they are heuristically estimated values – not measurements. But how much more uncertain are they? How can this effect be modeled?
In this thesis, we introduce Gaussian processes, a statistical framework that allows for the smooth interpolation of data with heteroscedastic uncertainty through regression. Its theoretical background makes it a convincing method to analyze uncertain data and create a model of the underlying phenomenon and, most importantly, the uncertainty at and in-between the data points. For this reason, it is already popular in the GIS community where it is known as Kriging but has applications in machine learning too.
In contrast to traditional interpolation methods, Gaussian processes do not merely create a surface that runs through the data points, but respect the uncertainty in them. This way, noise, errors or outliers in the data do not disturb the model inappropriately. Most importantly, the model shows the variance in the interpolated values, which can be higher but also lower than that of its neighboring data points, providing us with a lot more insight into the quality of our data and how it influences our uncertainty! This enables us to use uncertainty information in algorithms that need to interpolate between data points, which includes almost all visualization algorithms
Hilbert transforms in Clifford analysis
Full text linkThe Hilbert transform on the real line has applications in many fields. In particular in one–dimensional signal processing, the Hilbert operator is used to extract global as well as instantaneous characteristics, such as frequency, amplitude and phase, from real signals. The multidimensional approach to the Hilbert transform usually is a tensorial one, considering the so-called Riesz transforms in each of the cartesian variables separately. In this paper we give an overview of generalized Hilbert transforms in Euclidean space, developed within the framework of Clifford analysis. Roughly speaking, this is a function theory of higher dimensional holomorphic functions, which is particularly suited for a treatment of multidimensional phenomena since all dimensions are encompassed at once as an intrinsic feature
Focus and Context Methods for Particle-Based Data
Full text linkParticle-based models play a central role in many simulation techniques used for example in thermodynamics, molecular biology, material sciences, or astrophysics. Such simulations are carried out by directly calculating interactions on a set of individual particles over many time steps. Clusters of particles form higher-order structures like drops or waves.
The interactive visual inspection of particle datasets allows gaining in-depth insight, especially for initial exploration tasks. However, their visualization is challenging in many ways. Visualizations are required to convey structures and dynamics on multiple levels, such as per-particle or per-structure. Structures are typically dense and highly dynamic over time and are thus likely subject to heavy occlusion. Furthermore, since simulation systems become increasingly powerful, the number of particles per time step increases steadily, reaching data set sizes of trillions of particles. This enormous amount of data is challenging not only from a computational perspective but also concerning comprehensibility.
In this work, the idea of Focus+Context is applied to particle visualizations. Focus+Context is based on presenting a selection of the data – the focus – in high detail, while the remaining data – the context – is shown in reduced detail within the same image. This enables efficient and scalable visualizations that retain as much relevant information as possible while still being comprehensible for a human researcher. Based on the formulation of the most critical challenges, various novel methods for the visualization of static and dynamic 3D and nD particle data are introduced. A new approach that builds on global illumination and extended transparency allows to visualize otherwise occluded structures and
steer visual saliency towards selected elements. To address the time-dependent nature of particle data, Focus+Context is then extended to time. By using an illustration-inspired visualization, the researcher is supported in assessing the dynamics of higher-order particle structures. To understand correlations and high dimensional structures in higher dimensional data, a new method is presented, based on the idea of depth of field
Irreducible Orthogonal Decomposition of Tensors of any finite order in dimensions 2 and 3 in Deviatoric Tensors
Full text linkThe goal of this thesis is to understand the deviatoric decomposition of tensors of higher order in 2 and 3 dimensions. In the first chapter an introduction to tensor algebra will be given. Chapter 2 and 3 concentrate on establishing a recursive formula for the deviatoric decomposition in 2D and 3D, respectively. This recursive formula is the key to prove by induction the existense of a deviatoric decomposition for any tensor. Useful examples will also be given at the end of each chapter.:Introduction
1. Introduction to Tensor Algebra
2. Orthogonal Irreducible Decomposition for 2D Tensors
3. Orthogonal Irreducible Decomposition for 3D Tensors
4. Conclusion
Bibliography
5. Declaration of Originalit
Temporal Lossy In-Situ Compression for Computational Fluid Dynamics Simulations
Full text linkWährend CFD Simulationen für Metallschmelze im Rahmen des SFB920 fallen auf dem Taurus HPC Cluster in Dresden sehr große Datenmengen an, deren Handhabung den wissenschaftlichen Arbeitsablauf stark verlangsamen. Zum einen ist der Transfer in Visualisierungssysteme nur unter hohem Zeitaufwand möglich. Zum anderen ist interaktive Analyse von zeitlich abhängigen Prozessen auf Grund des Speicherflaschenhalses nahezu unmöglich. Aus diesen Gründen beschäftigt sich die vorliegende Dissertation mit der Entwicklung sog. Temporaler In-Situ Kompression für wissenschaftliche Daten direkt innerhalb von CFD Simulationen. Dabei werden mittels neuer Quantisierungsverfahren die Daten auf ~10% komprimiert, wobei dekomprimierte Daten einen Fehler von maximal 1% aufweisen. Im Gegensatz zu nicht-temporaler Kompression, wird bei temporaler Kompression der Unterschied zwischen Zeitschritten komprimiert, um den Kompressionsgrad zu erhöhen. Da die Datenmenge um ein Vielfaches kleiner ist, werden Kosten für die Speicherung und die Übertragung gesenkt. Da Kompression, Transfer und Dekompression bis zu 4 mal schneller ablaufen als der Transfer von unkomprimierten Daten, wird der wissenschaftliche Arbeitsablauf beschleunigt
Agent-based modeling of growing cell populations and the regenerating liver based on image processing
Full text linkIn the presented thesis we elaborated a general agent based model for multicellular populations. We used this model to shed light on the processes that determine the growth of avascular tumor spheroids and studied the key mechanisms of liver regeneration.
In order to make such analyses possible, we developed a comprehensive software tool that allowed us to effectively simulate, visualize and analyze the constructed computational model. We started with a minimal model for two-dimensional monolayers which are a common experimental technique for in vitro cell cultures.
We successively advanced our model in order to reflect an in vivo situation more closely for example by simulating complex three-dimensional tumor spheroids embedded in granular medium and host tissue.
We proposed a biomechanical form of contact inhibition that was able to explain the experimentally observed linear growth of the diameter in monolayer cultures [Bru et al., 1998] [Bru et al., 2003] and their specific proliferation pattern where cells mainly proliferate at the monolayer border. Furthermore, our model could mimic the growth dynamics of monolayer cultures very precisely.
Subsequently, we considered three-dimensional cell aggregates by studying substrate detachment whereby normally two-dimensional monolayers due to the failure of certain control mechanisms expand perpendicular to the monolayer plane. Failure of growth control mechanisms is known to play an important role in the development of
cancer [Hanahan & Weinberg, 2000]. By additionally introducing nutrient diffusion and consumption, we established a further extended model for three-dimensional tumor spheroids which are a common experimental model in therapeutically oriented cancer research. Surprisingly, we found that the proposed biomechanical form of contact inhibition also explains the growth of these tumor spheroids. Thereby, our model suggests in agreement with experimental data [Freyer & Sutherland, 1985]
[Freyer & Sutherland, 1986] that the nutrient concentration in the environment of a growing tumor, which is widely believed to control its growth, only determines the size of its necrotic core. Moreover, also in this three-dimensional situation our model precisely mimicked the growth dynamics and proliferation pattern of tumor spheroids in vitro where the necrotic core is enclosed by an intermediate layer of
quiescent cells and an outer layer of proliferating cells [Kunz-Schughart, 1999].
We further advanced our model for the growth of three dimensional cell populations even closer towards in vivo tumors by including aspects from the surrounding tissue.
We showed that the biomechanical properties of an embedding tissue have a major impact on the growth dynamics and morphology of growing cell populations by systematically varying the biophysical properties of the embedding tissue. Our model predicts Saffman-Taylor-like instabilities leading to fractal interfaces and an
increased ability of cells to invade harsh environments if the motility of the embedding cells is small. We additionally observed large wavelength instabilities as a consequence of decreased density, increased elasticity, strong adhesion or 5. Summary 160 increased cell size of the embedding tissue or granular medium. Interestingly, we found a nearly complete inhibition of tumor growth for specific properties of the embedding tissue which, if experimentally validated, could have direct therapeutical
implications.
Furthermore, we achieved a remarkable agreement with experimental data on tumor growth dynamics by [Helmlinger et al., 1997] and [Galle et al., 2006]. However, the large variety of complex influences predicted by our model strongly indicates that the widespread experimental technique of embedding growing tumor spheroids in agarose gels [Helmlinger et al., 1997] [Galle et al., 2006] [Cheng et al., 2009] may
not be sufficient to realistically capture all the biomechanical effects of an
embedding tissue. Effects due to the granularity of the surrounding tissue, for example, are missing in experiments like those performed in [Helmlinger et al., 1997].
In contrast to chapter three where we mainly compared our model to published in vitro data, in chapter four we investigated a particular in vivo situation and studied the fascinating process of liver regeneration after intoxication with CCl4, a prototypical substance for drugs inducing pericentral liver damage.
We established a procedure to use three-dimensional confocal laser scans to reconstruct in vivo tissues by image processing and image analysis. We then combined this very detailed and quantitative information with a further advanced version of our repeatedly experimentally validated model. We started with a minimal
two-dimensional model for the regenerating liver lobule that nevertheless led to first impressions of the specific impact of the various factors that influence liver regeneration. On that basis we extended our model and created the first threedimensional agent-based model of the regenerating liver lobule.
By capturing a 16 day regeneration process, our model underlined the importance of the complex columnar microarchitecture within the liver lobules, which is formed by hepatocytes and sinusoids. This microarchitecture ensures optimal exchange of metabolites between blood and hepatocytes. The model unambiguously predicted a
so far unrecognized mechanism, the alignment of daughter hepatocytes along the
orientation of the closest sinusoid, which we named hepatocyte-sinusoid alignment (HSA), as essential for liver regeneration. Only if HSA was included into the model the simulated tissue architecture was in agreement with the experimentally obtained data and no other likely mechanism could replace it. In order to experimentally validate the model prediction of HSA, we analyzed the orientation of daughter
hepatocytes in relation to the sinusoids in three-dimensions. The results of this analysis clearly confirmed the model prediction and thus verified HSA as a yet unknown key mechanism of liver regeneration.
During this analysis we introduced novel techniques that made currently
experimentally not accessible information available by image processing and analysis of volumetric datasets obtained by confocal laser scanning microscopy. In addition to the three-dimensional analysis of HSA, we used a similar approach to obtain further currently not experimentally available information on the average 5. Summary 161 contact area between hepatocytes and sinusoids. Surprisingly, we found this
parameter to allow for an automatic differentiation between normal liver tissue and hepatocellular carcinoma. The further pursuit of this finding will be interesting.
In summary, in this thesis we present an interdisciplinary approach to combine microscopic imaging, image processing and analysis and computational modeling - all in three dimensions. The integration of methods and results from different scientific fields like cell biology, physics and computer science enabled us to obtain new insights in cancer research and hepatology.
We therefore consider the presented interdisciplinary approach and the
corresponding procedures exemplary and widely applicable in the systems biology of tissues in general
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