Visual Analysis of Multi-Field Data

This thesis investigates methods for the visualization of multi-field medical data. In the medical field, data complexity has been growing consistently over the past years. Not only the size of the data grows, but also the need to visualize beyond traditional boundaries. We present a number of novel facets that encompass a general approach to the exploration of multi-field data. We strongly believe that human-in-the-loop visual data analysis on large and complex datasets is best aided by multiple linked different representations. The presented techniques demonstrate how complex data from multiple modalities can be visualized and interactively explored. We explore the use of linked selections to aid in reducing the complexity of the visualizations. Using multiple-linked views, we can integrate multiple orthogonal representations of the data simultaneously. We have applied aforementioned techniques in the design and implementation of a number of prototype frameworks, with applications ranging from brain imaging for neurosurgical planning to the study of the behavior of marine animals through the use of sensor data. We also present a conceptual framework for studying complex longitudinal data, by means of aggregation and multi-level visualization. We successfully adapted techniques from information visualization in order to use them on datasets that are orders of magnitude larger than they are originally used for.Computer Science, Computer Graphics groupElectrical Engineering, Mathematics and Computer Scienc

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis’04 contest), the ionization front instability data set (Vis’08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Interactive visualization of fused fMRI and DTI for planning brain tumor resections

The surgical removal of brain tumors can lead to functional impairment. Therefore it is crucial to minimize the damage to important functional areas during surgery. These areas can be mapped before surgery by using functional MRI. However, functional impairment is not only caused by damage to these areas themselves. It is also caused by damage to the fiber bundles that connect these areas with the rest of the brain. Diffusion Tensor Images (DTI) can add information about these connecting fiber bundles. In this paper we present interactive visualization techniques that combine DTI, fMRI and structural MRI to assist the planning of brain tumor surgery. Using a fusion of these datasets, we can extract the fiber bundles that pass through an offset region around the tumor, as can be seen in Figure 1. These bundles can then be explored by filtering on distance to the tumor, or by selecting a specific functional area. This approach enables the surgeon to combine all this information in a highly interactive environment in order to explore the pre-operative situation.Data Visualization GroupElectrical Engineering, Mathematics and Computer Scienc

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

A note on medieval microfabrication: The visualization of a prayer nut by synchrotron-based computer X-ray tomography

One of the most fascinating objects in the Rijksmuseum (Amsterdam, The Netherlands) is an early 16th century prayer nut. This spherical wooden object measures 4 cm in diameter and consists of two hemispheres connected with a small hinge so that it can be opened. The interior of the nut holds wood carvings with scenes from the life of Christ. These miniature reliefs show an incredible degree of finish with carving details well beyond the millimetre scale. In the present paper it is shown how synchrotron-based computer X-ray tomography revealed the structure and fabrication method of the bead. The central part of the relief was cut from a single piece of wood, rather than assembled from multiple components, underlining the extraordinary manual dexterity of its maker. In addition, a piece of fibrous material contained in the inner structure of the bead is revealed. This may have served as a carrier for an odorous compound, which would be in line with the religious function of the prayer nut.Materials Science and EngineeringMechanical, Maritime and Materials Engineerin