Visualization of Variation and Variability

As datasets grow in size and complexity, the importance of comparison as a tool for analysis is growing. We define comparison as the act of analyzing variation or variability based on two or more specific instances of the data. This thesis explores a number of cases spread across the range of comparisons, from variability within one entity through variability between two or more entities to variability within a population. For each of these we present an exploration tool, combining interaction with high-performance visualization rendering techniques, with the aim of providing more insight into a given dataset. We explore how different aspects of an application designed for interactive visual analysis can aid the user. This concerns both their initial exploration of a new dataset, as well as their ability to drill down into their discoveries and investigate the underlying details. For instance, multiple linked views can be used to combine highly abstract general-purpose views with highly problem-domain specific views in order to allow a user to translate abstract discoveries into the specific concepts used in their profession. Interactive composition can be applied to quickly focus on areas of interest, suppressing details which may not be relevant at the moment.Computer Graphics and VisualizationElectrical Engineering, Mathematics and Computer Scienc

Particle-based non-photorealistic volume visualization

Non-photorealistic techniques are usually applied to produce stylistic renderings. In visualization, these techniques are often able to simplify data, producing clearer images than traditional visualization methods. We investigate the use of particle systems for visualizing volume datasets using non-photorealistic techniques. In our VolumeFlies framework, user-selectable rules affect particles to produce a variety of illustrative styles in a unified way. The techniques presented do not require the generation of explicit intermediary surfaces

Visual comparison of 3D medical image Segmentation Algorithms Based on Statistical Shape Models

3D medical image segmentation is needed for diagnosis and treatment. As manual segmentation is very costly, automatic segmentation algorithms are needed. For finding best algorithms, several algorithms need to be evaluated on a set of organ instances. This is currently difficult due to dataset size and complexity. In this paper, we present a novel method for comparison and evaluation of several algorithms that automatically segment 3D medical images. It combines algorithmic data analysis with interactive data visualization. A clustering algorithm identifies regions of common quality across the segmented data set for each algorithm. The comparison identifies best algorithms per region. Interactive views show the algorithm quality. We applied our approach to a real-world cochlea dataset, which was segmented with several algorithms. Our approach allowed segmentation experts to compare algorithms on regional level and to identify best algorithms per region