We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

The art of representing images with triangles is known as image triangulation, which purposefully uses abstraction and simplification to guide the viewer's attention. The manual creation of image triangulations is tedious and thus several tools have been developed in the past that assist in the placement of vertices by means of image feature detection and subsequent Delaunay triangulation. In this paper, we formulate the image triangulation process as an optimization problem. We provide an interactive system that optimizes the vertex locations of an image triangulation to reduce the root mean squared approximation error. Along the way, the triangulation is incrementally refined by splitting triangles until certain refinement criteria are met. Thereby, the calculation of the energy gradients is expensive and thus we propose an efficient rasterization‐based GPU implementation. To ensure that artists have control over details, the system offers a number of direct and indirect editing tools that split, collapse and re‐triangulate selected parts of the image. For final display, we provide a set of rendering styles, including constant colours, linear gradients, tonal art maps and textures. Finally, we demonstrate temporal coherence for animations and compare our method with existing image triangulation tools.

Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview visualizations guide physicians to suspicious regions to be analysed in detail, e.g. with slice‐based viewing. We give an overview of state of the art in multi‐modal medical data visualization techniques. Multi‐modal medical data consist of multiple scans of the same subject using various acquisition methods, often combining multiple complimentary types of information. Three‐dimensional visualization techniques for multi‐modal medical data can be used in diagnosis, treatment planning, doctor–patient communication as well as interdisciplinary communication. Over the years, multiple techniques have been developed in order to cope with the various associated challenges and present the relevant information from multiple sources in an insightful way. We present an overview of these techniques and analyse the specific challenges that arise in multi‐modal data visualization and how recent works aimed to solve these, often using smart visibility techniques. We provide a taxonomy of these multi‐modal visualization applications based on the modalities used and the visualization techniques employed. Additionally, we identify unsolved problems as potential future research directions.

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

Epidemiological population studies impose information about a set of subjects (a cohort) to characterize disease-specific risk factors. Cohort studies comprise heterogenous variables describing the medical condition as well as demographic and lifestyle factors and, more recently, medical image data. We propose an Interactive Visual Analysis (IVA) approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation. We incorporate image data, which involves shape-based object detection and the derivation of attributes describing the object shape. The concurrent investigation of image-based and non-image data is realized in a web-based multiple coordinated view system, comprising standard views from information visualization and epidemiological data representations such as pivot tables. The views are equipped with brushing facilities and augmented by 3D shape renderings of the segmented objects, e.g., each bar in a histogram is overlaid with a mean shape of the associated subgroup of the cohort. We integrate an overview visualization, clustering of variables and object shape for data-driven subgroup definition and statistical key figures for measuring the association between variables. We demonstrate the IVA approach by validating and generating hypotheses related to lower back pain as part of a qualitative evaluation.