Information-based Feature Enhancement in Scientific Visualization

Abstract

Scientific visualization is a research area which gives insight into volumetric data acquired through measurement or simulation. The visualization allows a faster and more intuitive exploration of the data. Due to the rapid development in hardware for the measurement and simulation of scientific data, the size and complexity of data is constantly increasing. This has the benefit that it is possible to get a more accurate insight into the measured or simulated phenomena. A drawback of the increasing data size and complexity is the problem of generating an expressive representation of the data. Since only certain parts of the data are necessary to make a decision, it is possible to mask parts of the data along the visualization pipeline to enhance only those parts which are important in the visualization. For the masking various properties are extracted from the data which are used to classify a part as important or not. In general a transfer function is used for this classification process which has to be designed by the user. In this thesis three novel approaches are presented which use methods from information theory and statistics to enhance features from the data in the classification process that are important for a certain task. With the tools of information theory and statistics it is possible to extract properties from the data which are able to classify different materials or tissues in the data better than comparable other approaches. One approach adaptively extracts statistical properties, i.e. the mean value and the standard deviation, of the data values in the local neighborhood of each point in the data set. With these statistical properties it is possible to better distinguish between different materials in a data set even though the data is very noisy. The other two approaches in this thesis employ methods from information theory to extract features from multimodal data sets. Thus it is possible to enhance features of the data which are either very similar or very dissimilar in both modalities. Through information theory the variations in the value ranges of both modalities do not influence the classification of these features. All three approaches define novel transfer-function spaces which simplify the design process of a transfer function for the user. Different features of the data, such as different materials, can be clearly depicted in these spaces. Therefore, it is easier for a user to design a transfer function which enhances the features of importance for a certain task. For each of the new approaches results and comparisons to other existing techniques are shown to highlight the usefulness of the proposed methods. Through the described research it is shown that information theory and statistics are tools which are able to extract expressive properties from the data. In the introduction a broad overview over scientific visualization and the visualization pipeline is given. The classification process is described in more detail. Since information theory and statistics play an important role for all three approaches, a brief introduction to these concepts is given as well.