Diploma and Master Theses (authored and supervised):

F. Gschwantner:
"Advanced Measurement and Quantification of Industrial CT Data";
Supervisor: E. Gröller, M. Hadwiger, L. Fritz; Institut für Computergraphik und Algorithmen, 2011.

English 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.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.