Visualization and Analysis of X-ray Computed Tomography Data

Information

• Publication Type: Habilitation Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: July 2021
• First Supervisor: Eduard Gröller
• Date (Start): 1. January 2010
• Date (End): 16. July 2021
• Open Access: yes

Abstract

Visualization and analysis of primary and secondary X-ray computed tomography (XCT) data has become highly attractive for boosting research endeavors in the materials science domain. On the one hand, XCT allows to generate detailed and cumulative data of the specimens under investigation in a non-destructive way. On the other hand, through the conception, the development, and the implementation of novel, tailored analysis and visualization techniques, in-depth investigations of complex material systems turned into reality, e.g., in the form of interactive visualization of spatial and quantitative data, uncertainty quantification and visualization, comparative visualization, ensemble analysis and visualization, visual parameter space analysis, and many others. Visual analysis of XCT data enables a detailed understanding of the internal structures and the characteristics of materials and thus facilitates studies on a multitude of phenomena, at multiple scales, in different dimensions, or even using different modalities. This was simply impossible before. This habilitation thesis presents contributions to computer science in terms of novel methodsand techniques as well as respective algorithms and data structures, which are advancing visual analysis and visualization for enabling insights into XCT data on material systems. The introduced methods and techniques focus on three distinct technical areas of visual analysis and visualization of XCT data. For each area, the problem statements, important research questions to be solved as well as the contributions of the habilitation candidate are discussed: 1. Interactive visualization of spatial and quantitative data: Visualization and analysis techniques are introduced in this thesis for exploring, encoding, connecting, abstracting elaborating, reconfiguring, filtering, and finally selecting in "rich" XCT data. To reveal insights into complex objects, MObjects (i.e., mean objects) is discussed as a novel aggregation and exploration technique, which computes average volumetric representations from selections of individual objects of interest. To analyze various of these mean objects and to compare them with regards to their individual characteristics, visual analysis techniques as presented in FiberScout facilitate a detailed exploration of primary spatial data together with derived quantitative data (i.e., secondary data). 2. Visual parameter space analysis (vPSA): The contributions towards vPSA focus on concepts for exploring and analyzing the space of possible parameter combinations of algorithms, models, and data processing pipelines as well as their effects on the ensemble of results. The presented methods and techniques visually guide users in finding adequate input parameter sets, leading to optimal output results. In particular, the vPSA of segmentation and reconstruction algorithms is investigated. Similarity Metrics are introduced for comparing features as well as their characteristics. 3. Comparative visualization and ensemble analysis: The comparison of larger sets of ensemble members as generated by vPSA is difficult, tedious, and error-prone, which is often exacerbated by subtle differences in the individual members. Here, techniques are presented to study the differences between multiple results regarding their visual representation as well as their characteristics. Dynamic Volume Lines is a novel technique for the visual analysis and comparison of large sets of 3D volumes using linearization methods combined with interactive data exploration. This technique is accompanied by a comparative visualization in the spatial domain to establish a link between the abstracted data and real world representations. Finally, in terms of visualization theory and modeling, this thesis abstracts the characteristics of visual parameter space analysis in a holistic conceptual framework. It also classifies and frames the novel area of visual computing in materials science, identifying research gaps within this domain.

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BibTeX

@habilthesis{Heinzl2021,
  title =      "Visualization and Analysis of X-ray Computed Tomography Data",
  author =     "Christoph Heinzl",
  year =       "2021",
  abstract =   "Visualization and analysis of primary and secondary X-ray
               computed tomography (XCT) data has become highly attractive
               for boosting research endeavors in the materials science
               domain. On the one hand, XCT allows to generate detailed and
               cumulative data of the specimens under investigation in a
               non-destructive way. On the other hand, through the
               conception, the development, and the implementation of
               novel, tailored analysis and visualization techniques,
               in-depth investigations of complex material systems turned
               into reality, e.g., in the form of interactive visualization
               of spatial and quantitative data, uncertainty quantification
               and visualization, comparative visualization, ensemble
               analysis and visualization, visual parameter space analysis,
               and many others. Visual analysis of XCT data enables a
               detailed understanding of the internal structures and the
               characteristics of materials and thus facilitates studies on
               a multitude of phenomena, at multiple scales, in different
               dimensions, or even using different modalities. This was
               simply impossible before. This habilitation thesis presents
               contributions to computer science in terms of novel
               methodsand techniques as well as respective algorithms and
               data structures, which are advancing visual analysis and
               visualization for enabling insights into XCT data on
               material systems. The introduced methods and techniques
               focus on three distinct technical areas of visual analysis
               and visualization of XCT data. For each area, the problem
               statements, important research questions to be solved as
               well as the contributions of the habilitation candidate are
               discussed: 1. Interactive visualization of spatial and
               quantitative data: Visualization and analysis techniques are
               introduced in this thesis for exploring, encoding,
               connecting, abstracting elaborating, reconfiguring,
               filtering, and finally selecting in "rich" XCT data. To
               reveal insights into complex objects, MObjects (i.e., mean
               objects) is discussed as a novel aggregation and exploration
               technique, which computes average volumetric representations
               from selections of individual objects of interest. To
               analyze various of these mean objects and to compare them
               with regards to their individual characteristics, visual
               analysis techniques as presented in FiberScout facilitate a
               detailed exploration of primary spatial data together with
               derived quantitative data (i.e., secondary data). 2. Visual
               parameter space analysis (vPSA): The contributions towards
               vPSA focus on concepts for exploring and analyzing the space
               of possible parameter combinations of algorithms, models,
               and data processing pipelines as well as their effects on
               the ensemble of results. The presented methods and
               techniques visually guide users in finding adequate input
               parameter sets, leading to optimal output results. In
               particular, the vPSA of segmentation and reconstruction
               algorithms is investigated. Similarity Metrics are
               introduced for comparing features as well as their
               characteristics. 3. Comparative visualization and ensemble
               analysis: The comparison of larger sets of ensemble members
               as generated by vPSA is difficult, tedious, and error-prone,
               which is often exacerbated by subtle differences in the
               individual members. Here, techniques are presented to study
               the differences between multiple results regarding their
               visual representation as well as their characteristics.
               Dynamic Volume Lines is a novel technique for the visual
               analysis and comparison of large sets of 3D volumes using
               linearization methods combined with interactive data
               exploration. This technique is accompanied by a comparative
               visualization in the spatial domain to establish a link
               between the abstracted data and real world representations.
               Finally, in terms of visualization theory and modeling, this
               thesis abstracts the characteristics of visual parameter
               space analysis in a holistic conceptual framework. It also
               classifies and frames the novel area of visual computing in
               materials science, identifying research gaps within this
               domain.",
  month =      jul,
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Heinzl2021/",
}