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Scientists, engineers, and analysts are confronted with ever larger and more complex sets of data, whose analysis poses special challenges. In many situations it is necessary to compare two or more datasets. Hence there is a need for comparative visualization tools to help analyze differences or similarities among datasets. In this paper an approach for comparative visualization for sets of images is presented. Well-established techniques for comparing images frequently place them side-by-side. A major drawback of such approaches is that they do not scale well. Other image comparison methods encode differences in images by abstract parameters like color. In this case information about the underlying image data gets lost. This paper introduces a new method for visualizing differences and similarities in large sets of images which preserves contextual information, but also allows the detailed analysis of subtle variations. Our approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this process are then presented in an interactive web application which allows users to rapidly explore the space of differences and drill-down on particular features. We demonstrate the flexibility of our approach by applying it to multiple distinct domains.

Many application domains deal with multi-variate data that consist of both categorical and numerical information. Smallmultiple displays are a powerful concept for comparing such data by juxtaposition. For comparison by overlay or by explicit encoding of computed differences, however, a specification of references is necessary. In this paper, we present a formal model for defining semantically meaningful comparisons between many categories in a small-multiple display. Based on pivotized data that are hierarchically partitioned by the categories assigned to the x and y axis of the display, we propose two alternatives for structure-based comparison within this hierarchy. With an absolute reference specification, categories are compared to a fixed reference category. With a relative reference specification, in contrast, a semantic ordering of the categories is considered when comparing them either to the previous or subsequent category each. Both reference specifications can be defined at multiple levels of the hierarchy (including aggregated summaries), enabling a multitude of useful comparisons. We demonstrate the general applicability of our model in several application examples using different visualizations that compare data by overlay or explicit encoding of differences.

In volume rendering, it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Highly transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper, we present a new model for volume rendering, inspired by from illustration. It provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, sharing their easy and intuitive user control, but does not suffer from the drawback of missing context information.

This paper presents importance-driven feature enhancement as a technique for the automatic generation of cut-away and ghosted views out of volumetric data. The presented focus+context approach removes or suppresses less important parts of a scene to reveal more important underlying information. However, less important parts are fully visible in those regions, where important visual information is not lost, i.e., more relevant features are not occluded. Features within the volumetric data are first classified according to a new dimension, denoted as object importance. This property determines which structures should be readily discernible and which structures are less important. Next, for each feature, various representations (levels of sparseness) from a dense to a sparse depiction are defined. Levels of sparseness define a spectrum of optical properties or rendering styles. The resulting image is generated by ray-casting and combining the intersected features proportional to their importance (importance compositing). The paper includes an extended discussion on several possible schemes for levels of sparseness specification. Furthermore, different approaches to importance compositing are treated.

During the last decades, anatomy has become an interesting topic in education—even for laymen or schoolchildren. As medical imaging techniques become increasingly sophisticated, virtual anatomical education applications have emerged. Still, anatomical models are often preferred, as they facilitate 3D localization of anatomical structures. Recently, data physicalizations (i.e., physical visualizations) have proven to be effective and engaging—sometimes, even more than their virtual counterparts. So far, medical data physicalizations involve mainly 3D printing, which is still expensive and cumbersome. We investigate alternative forms of physicalizations, which use readily available technologies (home printers) and inexpensive materials (paper or semi‐transparent films) to generate crafts for anatomical edutainment. To the best of our knowledge, this is the first computer‐generated crafting approach within an anatomical edutainment context. Our approach follows a cost‐effective, simple, and easy‐to‐employ workflow, resulting in assemblable data sculptures (i.e., semi‐transparent sliceforms). It primarily supports volumetric data (such as CT or MRI), but mesh data can also be imported. An octree slices the imported volume and an optimization step simplifies the slice configuration, proposing the optimal order for easy assembly. A packing algorithm places the resulting slices with their labels, annotations, and assembly instructions on a paper or transparent film of user‐selected size, to be printed, assembled into a sliceform, and explored. We conducted two user studies to assess our approach, demonstrating that it is an initial positive step towards the successful creation of interactive and engaging anatomical physicalizations. [ABSTRACT FROM AUTHOR]

Exploded views are an illustration technique where an object is partitioned into several segments. These segments are displaced to reveal otherwise hidden detail. In this paper we apply the concept of exploded views to volumetric data in order to solve the general problem of occlusion. In many cases an object of interest is occluded by other structures. While transparency or cutaways can be used to reveal a focus object, these techniques remove parts of the context information. Exploded views, on the other hand, do not suffer from this drawback. Our approach employs a force-based model: the volume is divided into a part configuration controlled by a number of forces and constraints. The focus object exerts an explosion force causing the parts to arrange according to the given constraints. We show that this novel and flexible approach allows for a wide variety of explosion-based visualizations including view-dependent explosions. Furthermore, we present a high-quality GPU-based volume ray casting algorithm for exploded views which allows rendering and interaction at several frames per second. [ABSTRACT FROM AUTHOR]