Model-Based Visualization for Intervention Planning

Computer support for intervention planning is often a two-stage process: In a first stage, the relevant segmentation target structures are identified and delineated. In a second stage, image analysis results are employed for the actual planning process. In the first stage, model-based segmentation techniques are often used to reduce the interaction effort and increase the reproducibility. There is a similar argument to employ model-based techniques for the visualization as well. With increasingly more visualization options, users have many parameters to adjust in order to generate expressive visualizations. Surface models may be smoothed with a variety of techniques and parameters. Surface visualization and illustrative rendering techniques are controlled by a large set of additional parameters. Although interactive 3d visualizations should be flexible and support individual planning tasks, appropriate selection of visualization techniques and presets for their parameters is needed. In this chapter, we discuss this kind of visualization support. We refer to model-based visualization to denote the selection and parameterization of visualization techniques based on \u27a priori knowledge concerning visual perception, shapes of anatomical objects and intervention planning tasks

Feature Lines for Illustrating Medical Surface Models: Mathematical Background and Survey

This paper provides a tutorial and survey for a specific kind of illustrative visualization technique: feature lines. We examine different feature line methods. For this, we provide the differential geometry behind these concepts and adapt this mathematical field to the discrete differential geometry. All discrete differential geometry terms are explained for triangulated surface meshes. These utilities serve as basis for the feature line methods. We provide the reader with all knowledge to re-implement every feature line method. Furthermore, we summarize the methods and suggest a guideline for which kind of surface which feature line algorithm is best suited. Our work is motivated by, but not restricted to, medical and biological surface models.Comment: 33 page

Interactive Visual Analysis of Heterogeneous Cohort Study Data

Cohort studies in medicine are conducted to enable the study of medical hypotheses in large samples. Often, a large amount of heterogeneous data is acquired from many subjects. The analysis is usually hypothesis-driven, i.e., a specific subset of such data is studied to confirm or reject specific hypotheses. In this paper, we demonstrate how we enable the interactive visual exploration and analysis of such data, helping with the generation of new hypotheses and contributing to the process of validating them. We propose a data-cube based model which handles partially overlapping data subsets during the interactive visualization. This model enables seamless integration of the heterogeneous data, as well as linking spatial and non-spatial views on these data. We implemented this model in an application prototype, and used it to analyze data acquired in the context of a cohort study on cognitive aging. We present case-study analyses of selected aspects of brain connectivity by using the prototype implementation of the presented model, to demonstrate its potential and flexibility

A survey of visual analytics for public health

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.Projekt DEAL 201

A Survey of Visual Analytics for Public Health

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

Hybrid Sample-based Surface Rendering

The performance of rasterization-based rendering on current GPUs strongly depends on the abilities to avoid overdraw and to prevent rendering triangles smaller than the pixel size. Otherwise, the rates at which highresolution polygon models can be displayed are affected significantly. Instead of trying to build these abilities into the rasterization-based rendering pipeline, we propose an alternative rendering pipeline implementation that uses rasterization and ray-casting in every frame simultaneously to determine eye-ray intersections. To make ray-casting competitive with rasterization, we introduce a memory-efficient sample-based data structure which gives rise to an efficient ray traversal procedure. In combination with a regular model subdivision, the most optimal rendering technique can be selected at run-time for each part. For very large triangle meshes our method can outperform pure rasterization and requires a considerably smaller memory budget on the GPU. Since the proposed data structure can be constructed from any renderable surface representation, it can also be used to efficiently render isosurfaces in scalar volume fields. The compactness of the data structure allows rendering from GPU memory when alternative techniques already require exhaustive paging