Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.

Visual comparison for information visualization

Visualization and visual analysis play important roles in exploring, analyzing, and presenting scientific data. In many disciplines, data and model scenarios are becoming multifaceted: data are often spatiotemporal and multivariate; they stem from different data sources (multimodal data), from multiple simulation runs (multirun/ensemble data), or from multiphysics simulations of interacting phenomena (multimodel data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multifaceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.

/pdf/visualization-and-visual-analysis-of-multifaceted-scientific-1fqhkx70ew.pdf

Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey

Visualization in Medicine is the first book on visualization and its application to problems in medical diagnosis, education, and treatment. The book describes the algorithms, the applications and their validation (how reliable are the results?), and the clinical evaluation of the applications (are the techniques useful?). It discusses visualization techniques from research literature as well as the compromises required to solve practical clinical problems. 

The book covers image acquisition, image analysis, and interaction techniques designed to explore and analyze the data. The final chapter shows how visualization is used for planning liver surgery, one of the most demanding surgical disciplines. The book is based on several years of the authors' teaching and research experience. Both authors have initiated and lead a variety of interdisciplinary projects involving computer scientists and medical doctors, primarily radiologists and surgeons.

* A core field of visualization and graphics missing a dedicated book until now
* Written by pioneers in the field and illustrated in full color
* Covers theory as well as practice

Visualization in Medicine: Theory, Algorithms, and Applications

Computed tomographic (CT) angiography has been improved significantly with the introduction of four- to 64-section spiral CT scanners, which offer rapid acquisition of isotropic data sets. A variety of techniques have been proposed for postprocessing of the resulting images. The most widely used techniques are multiplanar reformation (MPR), thin-slab maximum intensity projection, and volume rendering. Sophisticated segmentation algorithms, vessel analysis tools based on a centerline approach, and automatic lumen boundary definition are emerging techniques; bone removal with thresholding or subtraction algorithms has been introduced. These techniques increasingly provide a quality of vessel analysis comparable to that achieved with intraarterial three-dimensional rotational angiography. Neurovascular applications for these various image postprocessing methods include steno-occlusive disease, dural sinus thrombosis, vascular malformations, and cerebral aneurysms. However, one should keep in mind the potenti...

https://pubs.rsna.org/doi/pdf/10.1148/rg.26si065508

New techniques in CT angiography.

In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multivariate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.

/pdf/visualization-of-multi-variate-scientific-data-3azavi8012.pdf

Visualization of Multi‐Variate Scientific Data

Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data

We present an approach to visualizing correlations in 3D multifield scalar data. The core of our approach is the computation of correlation fields, which are scalar fields containing the local correlations of subsets of the multiple fields. While the visualization of the correlation fields can be done using standard 3D volume visualization techniques, their huge number makes selection and handling a challenge. We introduce the multifield-graph to give an overview of which multiple fields correlate and to show the strength of their correlation. This information guides the selection of informative correlation fields for visualization. We use our approach to visually analyze a number of real and synthetic multifield datasets

Diffusion tensor imaging is a magnetic resonance imaging method which has gained increasing importance in neuroscience and especially in neurosurgery. It acquires diffusion properties represented by a symmetric 2nd order tensor for each voxel in the gathered dataset. From the medical point of view, the data is of special interest due lo different diffusion characteristics of varying brain tissue allowing conclusions about the underlying structures such as while matter tracts. An obvious way to visualize this data is to focus on the anisotropic areas using the major eigenvector for tractography and rendering lines for visualization of the simulation results. Our approach extends this technique to avoid line representation since lines lead 10 very complex illustrations and furthermore are mistakable. Instead, we generate surfaces wrapping bundles of lines. Thereby, a more intuitive representation of different tracts is achieved.

Visualization of White Matter Tracts with Wrapped Streamlines

/pdf/visualization-of-white-matter-tracts-with-wrapped-2iw8o8s439.pdf

Visualization of white matter tracts with wrapped streamlines

Direct volume visualization has recently gained importance as a tool for the analysis of intracranial aneurysms focused on therapy planning. CT-angiography (CTA) intensities are mapped to color and opacity values by so called one-dimensional transfer functions. In this work, we introduce 3D-visualization of intracranial aneurysms making use of transfer functions based on measured values and gradient magnitudes extracted from the CTA data. Furthermore, we present a tool for the creation of 2D transfer functions in the clinical environment. The visualization application runs on standard PCs equipped with modern 3D graphics cards. Evaluations were carried out on 17 clinical cases from our archive. Clear improvements with respect to standard volume visualization were observed especially in the area of the skull base, where the arteries are difficult to separate from the bone. Effective separation of skull and arteries was achieved even for cases where the critical vascular structures were embedded in osseous tissue.

/pdf/enhanced-3d-visualization-of-intracranial-aneurysms-wpnwgsn92t.pdf

Enhanced 3D-Visualization of Intracranial Aneurysms Involving the Skull Base

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Papers

Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data

Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data

Visualization of White Matter Tracts with Wrapped Streamlines

Visualization of white matter tracts with wrapped streamlines

Enhanced 3D-Visualization of Intracranial Aneurysms Involving the Skull Base