Visual Analytics for High-Dimensional Images via Dimensionality Reduction
A. Vieth (TU Delft - Computer Graphics and Visualisation)
A. Vilanova Bartroli – Promotor (TU Delft - Computer Graphics and Visualisation, Eindhoven University of Technology)
B.P.F. Lelieveldt – Promotor (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
E. Eisemann – Promotor (TU Delft - Computer Graphics and Visualisation)
T. Höllt – Copromotor (TU Delft - Computer Graphics and Visualisation)
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Abstract
Analyzing high-dimensional images is a complex task. Unlike regular color images, they are not straightforward to visualize. Their additional information content increases the complexity of interpretation, both in terms of computational processing and human comprehension. Visual analytics - the combination of visualization, interaction, and automated analysis methods - has proven useful to gain insights into such large, difficult-to-handle data. For example, non-linear dimensionality reduction is a commonly employed technique in visual analytics for exposing interesting patterns through lower-dimensional representations of high-dimensional data.
In this thesis, we investigate non-linear dimensionality-reduction methods for the exploration of high-dimensional images. Specifically, we address the problem that current dimensionality-reduction methods are image-agnostic: they treat spatially resolved data without considering their spatial layout. We present algorithmic solutions that yield image-informed embeddings and interactions techniques that connect images and embedding representations. To a large extend, we utilize hierarchical approaches to handle the image data. We show how these techniques enable more insightful exploration of high-dimensional images.
Further, we present an open-source visual analytics software framework for rapid prototyping and extensible workflow development for high-dimensional data analysis. All algorithms and techniques described in this thesis are made available in or fully implemented as plugins for this framework.
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