IEEE VIS Publication Dataset

Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.

We present a method for extracting boundary surfaces from segmented cross-section image data. We use a constrained Potts model to interpolate an arbitrary number of region boundaries between segmented images. This produces a segmented volume from which we extract a triangulated boundary surface using well-known marching tetrahedra methods. This surface contains staircase-like artifacts and an abundance of unnecessary triangles. We describe an approach that addresses these problems with a voxel-accurate simplification algorithm that reduces surface complexity by an order of magnitude. Our boundary interpolation and simplification methods are novel contributions to the study of surface extraction from segmented cross-sections. We have applied our method to construct polycrystal grain boundary surfaces from micrographs of a sample of the metal tantalum.

Multiple spatially-related videos are increasingly used in security, communication, and other applications. Since it can be difficult to understand the spatial relationships between multiple videos in complex environments (e.g. to predict a person's path through a building), some visualization techniques, such as video texture projection, have been used to aid spatial understanding. In this paper, we identify and begin to characterize an overall class of visualization techniques that combine video with 3D spatial context. This set of techniques, which we call contextualized videos, forms a design palette which must be well understood so that designers can select and use appropriate techniques that address the requirements of particular spatial video tasks. In this paper, we first identify user tasks in video surveillance that are likely to benefit from contextualized videos and discuss the video, model, and navigation related dimensions of the contextualized video design space. We then describe our contextualized video testbed which allows us to explore this design space and compose various video visualizations for evaluation. Finally, we describe the results of our process to identify promising design patterns through user selection of visualization features from the design space, followed by user interviews.

In nature and in flow experiments particles form patterns of swirling motion in certain locations. Existing approaches identify these structures by considering the behavior of stream lines. However, in unsteady flows particle motion is described by path lines which generally gives different swirling patterns than stream lines. We introduce a novel mathematical characterization of swirling motion cores in unsteady flows by generalizing the approach of Sujudi/Haimes to path lines. The cores of swirling particle motion are lines sweeping over time, i.e., surfaces in the space-time domain. They occur at locations where three derived 4D vectors become coplanar. To extract them, we show how to re-formulate the problem using the parallel vectors operator. We apply our method to a number of unsteady flow fields.

We present novel, comprehensive visualization techniques for the diagnosis of patients with coronary artery disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bull's eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bull's eye plot can be used for the assessment of transmurality. We link these visualizations to a 3D view that presents viability information in a detailed anatomical context. We combine multiple MRI scans (whole heart anatomical data, late enhancement data) and multiple segmentations (polygonal heart model, late enhancement contours, coronary artery tree). By selectively combining different rendering techniques we obtain comprehensive yet intuitive visualizations of the various data sources.

The recently introduced notion of Finite-Time Lyapunov Exponent to characterize Coherent Lagrangian Structures provides a powerful framework for the visualization and analysis of complex technical flows. Its definition is simple and intuitive, and it has a deep theoretical foundation. While the application of this approach seems straightforward in theory, the associated computational cost is essentially prohibitive. Due to the Lagrangian nature of this technique, a huge number of particle paths must be computed to fill the space-time flow domain. In this paper, we propose a novel scheme for the adaptive computation of FTLE fields in two and three dimensions that significantly reduces the number of required particle paths. Furthermore, for three-dimensional flows, we show on several examples that meaningful results can be obtained by restricting the analysis to a well-chosen plane intersecting the flow domain. Finally, we examine some of the visualization aspects of FTLE-based methods and introduce several new variations that help in the analysis of specific aspects of a flow.

The Morse-Smale complex is an efficient representation of the gradient behavior of a scalar function, and critical points paired by the complex identify topological features and their importance. We present an algorithm that constructs the Morse-Smale complex in a series of sweeps through the data, identifying various components of the complex in a consistent manner. All components of the complex, both geometric and topological, are computed, providing a complete decomposition of the domain. Efficiency is maintained by representing the geometry of the complex in terms of point sets.

We propose a novel, geometrically adaptive method for surface reconstruction from noisy and sparse point clouds, without orientation information. The method employs a fast convection algorithm to attract the evolving surface towards the data points. The force field in which the surface is convected is based on generalized Coulomb potentials evaluated on an adaptive grid (i.e., an octree) using a fast, hierarchical algorithm. Formulating reconstruction as a convection problem in a velocity field generated by Coulomb potentials offers a number of advantages. Unlike methods which compute the distance from the data set to the implicit surface, which are sensitive to noise due to the very reliance on the distance transform, our method is highly resilient to shot noise since global, generalized Coulomb potentials can be used to disregard the presence of outliers due to noise. Coulomb potentials represent long-range interactions that consider all data points at once, and thus they convey global information which is crucial in the fitting process. Both the spatial and temporal complexities of our spatially-adaptive method are proportional to the size of the reconstructed object, which makes our method compare favorably with respect to previous approaches in terms of speed and flexibility. Experiments with sparse as well as noisy data sets show that the method is capable of delivering crisp and detailed yet smooth surfaces.

This paper presents a method for filtered ridge extraction based on adaptive mesh refinement. It is applicable in situations where the underlying scalar field can be refined during ridge extraction. This requirement is met by the concept of Lagrangian coherent structures which is based on trajectories started at arbitrary sampling grids that are independent of the underlying vector field. The Lagrangian coherent structures are extracted as ridges in finite Lyapunov exponent fields computed from these grids of trajectories. The method is applied to several variants of finite Lyapunov exponents, one of which is newly introduced. High computation time due to the high number of required trajectories is a main drawback when computing Lyapunov exponents of 3-dimensional vector fields. The presented method allows a substantial speed-up by avoiding the seeding of trajectories in regions where no ridges are present or do not satisfy the prescribed filter criteria such as a minimum finite Lyapunov exponent.

Volumetric data commonly has high depth complexity which makes it difficult to judge spatial relationships accurately. There are many different ways to enhance depth perception, such as shading, contours, and shadows. Artists and illustrators frequently employ halos for this purpose. In this technique, regions surrounding the edges of certain structures are darkened or brightened which makes it easier to judge occlusion. Based on this concept, we present a flexible method for enhancing and highlighting structures of interest using GPU-based direct volume rendering. Our approach uses an interactively defined halo transfer function to classify structures of interest based on data value, direction, and position. A feature-preserving spreading algorithm is applied to distribute seed values to neighboring locations, generating a controllably smooth field of halo intensities. These halo intensities are then mapped to colors and opacities using a halo profile function. Our method can be used to annotate features at interactive frame rates.

We present a method to extract and visualize vortices that originate from bounding walls of three-dimensional time- dependent flows. These vortices can be detected using their footprint on the boundary, which consists of critical points in the wall shear stress vector field. In order to follow these critical points and detect their transformations, affected regions of the surface are parameterized. Thus, an existing singularity tracking algorithm devised for planar settings can be applied. The trajectories of the singularities are used as a basis for seeding particles. This leads to a new type of streak line visualization, in which particles are released from a moving source. These generalized streak lines visualize the particles that are ejected from the wall. We demonstrate the usefulness of our method on several transient fluid flow datasets from computational fluid dynamics simulations.

We present the results of two controlled studies comparing layered surface visualizations under various texture conditions. The task was to estimate surface normals, measured by accuracy of a hand-set surface normal probe. A single surface visualization was compared with the two-surfaces case under conditions of no texture and with projected grid textures. Variations in relative texture spacing on top and bottom surfaces were compared, as well as opacity of the top surface. Significant improvements are found for the textured cases over non-textured surfaces. Either larger or thinner top-surface textures, and lower top surface opacities are shown to give less bottom surface error. Top surface error appears to be highly resilient to changes in texture. Given the results we also present an example of how appropriate textures might be useful in volume visualization.

Surgical approaches tailored to an individual patient's anatomy and pathology have become standard in neurosurgery. Precise preoperative planning of these procedures, however, is necessary to achieve an optimal therapeutic effect. Therefore, multiple radiological imaging modalities are used prior to surgery to delineate the patient's anatomy, neurological function, and metabolic processes. Developing a three-dimensional perception of the surgical approach, however, is traditionally still done by mentally fusing multiple modalities. Concurrent 3D visualization of these datasets can, therefore, improve the planning process significantly. In this paper we introduce an application for planning of individual neurosurgical approaches with high-quality interactive multimodal volume rendering. The application consists of three main modules which allow to (1) plan the optimal skin incision and opening of the skull tailored to the underlying pathology; (2) visualize superficial brain anatomy, function and metabolism; and (3) plan the patient-specific approach for surgery of deep-seated lesions. The visualization is based on direct multi-volume raycasting on graphics hardware, where multiple volumes from different modalities can be displayed concurrently at interactive frame rates. Graphics memory limitations are avoided by performing raycasting on bricked volumes. For preprocessing tasks such as registration or segmentation, the visualization modules are integrated into a larger framework, thus supporting the entire workflow of preoperative planning.

Much of the visualization research has focused on improving the rendering quality and speed, and enhancing the perceptibility of features in the data. Recently, significant emphasis has been placed on focus+context (F+C) techniques (e.g., fisheye views and magnification lens) for data exploration in addition to viewing transformation and hierarchical navigation. However, most of the existing data exploration techniques rely on the manipulation of viewing attributes of the rendering system or optical attributes of the data objects, with users being passive viewers. In this paper, we propose a more active approach to data exploration, which attempts to mimic how we would explore data if we were able to hold it and interact with it in our hands. This involves allowing the users to physically or actively manipulate the geometry of a data object. While this approach has been traditionally used in applications, such as surgical simulation, where the original geometry of the data objects is well understood by the users, there are several challenges when this approach is generalized for applications, such as flow and information visualization, where there is no common perception as to the normal or natural geometry of a data object. We introduce a taxonomy and a set of transformations especially for illustrative deformation of general data exploration. We present combined geometric or optical illustration operators for focus+context visualization, and examine the best means for preventing the deformed context from being misperceived. We demonstrated the feasibility of this generalization with examples of flow, information and video visualization.

We describe a system for interactively rendering isosurfaces of tetrahedral finite-element scalar fields using coherent ray tracing techniques on the CPU. By employing state-of-the art methods in polygonal ray tracing, namely aggressive packet/frustum traversal of a bounding volume hierarchy, we can accommodate large and time-varying unstructured data. In conjunction with this efficiency structure, we introduce a novel technique for intersecting ray packets with tetrahedral primitives. Ray tracing is flexible, allowing for dynamic changes in isovalue and time step, visualization of multiple isosurfaces, shadows, and depth-peeling transparency effects. The resulting system offers the intuitive simplicity of isosurfacing, guaranteed-correct visual results, and ultimately a scalable, dynamic and consistently interactive solution for visualizing unstructured volumes.

We present a new approach for real-time sound rendering in complex, virtual scenes with dynamic sources and objects. Our approach combines the efficiency of interactive ray tracing with the accuracy of tracing a volumetric representation. We use a four-sided convex frustum and perform clipping and intersection tests using ray packet tracing. A simple and efficient formulation is used to compute secondary frusta and perform hierarchical traversal. We demonstrate the performance of our algorithm in an interactive system for complex environments and architectural models with tens or hundreds of thousands of triangles. Our algorithm can perform real-time simulation and rendering on a high-end PC.

Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimension reduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.

In this paper we present a method to compute and visualize volumetric white matter connectivity in diffusion tensor magnetic resonance imaging (DT-MRI) using a Hamilton-Jacobi (H-J) solver on the GPU (graphics processing unit). Paths through the volume are assigned costs that are lower if they are consistent with the preferred diffusion directions. The proposed method finds a set of voxels in the DTI volume that contain paths between two regions whose costs are within a threshold of the optimal path. The result is a volumetric optimal path analysis, which is driven by clinical and scientific questions relating to the connectivity between various known anatomical regions of the brain. To solve the minimal path problem quickly, we introduce a novel numerical algorithm for solving H-J equations, which we call the fast iterative method (FIM). This algorithm is well-adapted to parallel architectures, and we present a GPU-based implementation, which runs roughly 50-100 times faster than traditional CPU-based solvers for anisotropic H-J equations. The proposed system allows users to freely change the endpoints of interesting pathways and to visualize the optimal volumetric path between them at an interactive rate. We demonstrate the proposed method on some synthetic and real DT-MRI datasets and compare the performance with existing methods.

Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.

We describe a novel volumetric global illumination framework based on the face-centered cubic (FCC) lattice. An FCC lattice has important advantages over a Cartesian lattice. It has higher packing density in the frequency domain, which translates to better sampling efficiency. Furthermore, it has the maximal possible kissing number (equivalent to the number of nearest neighbors of each site), which provides optimal 3D angular discretization among all lattices. We employ a new two-pass (illumination and rendering) global illumination scheme on an FCC lattice. This scheme exploits the angular discretization to greatly simplify the computation in multiple scattering and to minimize illumination information storage. The GPU has been utilized to further accelerate the rendering stage. We demonstrate our new framework with participating media and volume rendering with multiple scattering, where both are significantly faster than traditional techniques with comparable quality.