Uncertainty-aware visual analytics: scope, opportunities, and challenges

In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided

Multi-modal Visualization of Stroke Lesion CT-Imaging

Stroke lesions are a result of a sudden cerebrovas-cular disease that cause a lack of blood supply to the brain. Clinicians aim to localize and quantify brain lesions by utilizing multi-modal CT (Computed Tomography) imaging in order to provide a suitable treatment. In clinical daily routine, neurologists review one modality at a time and a correlation between several modalities is hard to obtain. To better understand the effects of a stroke and identify the origin, we visualize the multi-modal CT data set of a patient by providing a multi-view visualization system. With this visualization we are able to provide a focus and overview of the multi-modal brain lesion imaging available of one patient that allows clinicians to correlate multi-modal stroke imaging. We show the applicability of the presented approach by applying it to real world patient data sets

Top Challenges in the Visualization of Engineering Tensor Fields

In this chapter we summarize the top research challenges in creating successful visualization tools for tensor fields in engineering. The analysis is based on our collective experiences and on discussions with both domain experts and visualization practitioners. We find that creating visualization tools for engineering tensors often involves solving multiple different technical problems at the same time, including visual intuitiveness, scalability, interactivity, providing both detail and context, integration with modeling and simulation, representing uncertainty and managing multi-fields; as well as overcoming terminology barriers and advancing research in the mathematical aspects of tensor field processing. We further note the need for tools and data repositories to encourage faster advances in the field. Our interest in creating and proposing this list is to initiate a discussion about important research issues within the visualization of engineering tensor fields

Detecting Critical Regions in Scalar Fields

Trivariate data is commonly visualized using isosurfaces or direct volume rendering. When exploring scalar fields by isosurface extraction it is often difficult to choose isovalues that convey “useful ” information. The significance of visualizations using direct volume rendering depends on the choice of good transfer functions. Understanding and using isosurface topology can help in identifying “interesting ” isovalues for visualization via isosurfaces and can be used to automatically generate transfer functions. Critical isovalues indicate changes in topology of an isosurface: the creation of new surface components, merging of surface components or the formation of holes in a surface component. Interesting isosurface behavior is likely to occur at and around critical isovalues. Current approaches to detect critical isovalues are usually limited to isolated critical points. Data sets often contain regions of constant value (i.e., mesh edges, mesh faces, or entire mesh cells). We present a method that detects critical points, critical regions and corresponding critical isovalues for a scalar field defined by piecewise trilinear interpolation over a uniform rectilinear grid. We describe how to use the resulting list of critical regions/points and associated values to examine trivariate data. 1

Detecting Critical Regions in Scalar Fields

Trivariate data is commonly visualized using isosurfaces or direct volume rendering. When exploring scalar fields by isosurface extraction it is often difficult to choose isovalues that convey "useful" information. The significance of visualizations using direct volume rendering depends on the choice of good transfer functions. Understanding and using isosurface topology can help in identifying "relevant" isovalues for visualization via isosurfaces and can be used to automatically generate transfer functions. Critical isovalues indicate..