Advanced Measurement and Quantification of Industrial CT Data

Fritz-Michael Gschwantner
Advanced Measurement and Quantification of Industrial CT Data
[Thesis]

Information

Abstract

Non–destructive testing (NDT) is a vital part in today’s industrial production and research processes. Such testing procedures often use Computed Tomography (CT) in order to get insights of the inner parts of an object. During the analysis of different objects, certain features can be automatically segmented and quantified in the CT dataset. However, due to various effects during the acquisition of the data, the original boundaries of two materials within the objects are not accurately represented in the dataset. This thesis describes a method to reconstruct these boundaries for automatically segmented features on a subvoxel level of the dataset. They are searched along the gradient of the data, using an edge–detection approach commonly used in image processing. The result is then represented as a distance field and further quantified through over–sampling and measuring. For a variety of datasets it is shown that these reconstructed boundaries are indeed providing a more accurate representation of the original segmented region. Further comparisons are made with a method that simply tries to improve the visual appearance through smoothing.

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BibTeX

@mastersthesis{Gschwantner_2011_AMQ,
  title =      "Advanced Measurement and Quantification of Industrial CT
               Data",
  author =     "Fritz-Michael Gschwantner",
  year =       "2011",
  abstract =   "Non–destructive testing (NDT) is a vital part in today’s
               industrial production and research processes. Such testing
               procedures often use Computed Tomography (CT) in order to
               get insights of the inner parts of an object. During the
               analysis of different objects, certain features can be
               automatically segmented and quantified in the CT dataset.
               However, due to various effects during the acquisition of
               the data, the original boundaries of two materials within
               the objects are not accurately represented in the dataset.
               This thesis describes a method to reconstruct these
               boundaries for automatically segmented features on a
               subvoxel level of the dataset. They are searched along the
               gradient of the data, using an edge–detection approach
               commonly used in image processing. The result is then
               represented as a distance field and further quantified
               through over–sampling and measuring. For a variety of
               datasets it is shown that these reconstructed boundaries are
               indeed providing a more accurate representation of the
               original segmented region. Further comparisons are made with
               a method that simply tries to improve the visual appearance
               through smoothing.",
  month =      oct,
  address =    "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/Gschwantner_2011_AMQ/",
}