Talks and Poster Presentations (with Proceedings-Entry):

Ch. Heinzl, J. Kastner, E. Gröller:
"Reproducible Surface Extraction for Variance Comparison in 3D Computed Tomography";
Talk: European Congress on Non-Destructive Testing (ECNDT), Berlin, Deutschland; 2006-09-25 - 2006-09-29; in: "Proceedings of 9th European Congress on Non-Destructive Testing (ECNDT 2006)", (2006).

English abstract:
This paper describes a novel method for creating surfaces models from
distorted volume datasets in 3D computed tomography (3D-CT). As all 3D-CT
datasets are prone to artefacts, especially geometry extraction may produce
erroneous surface models if a single global threshold is used. Depending on the
selected threshold either the area of material is thickened because ambient noise is
added (threshold too low) or it is thinned because outlying material regions are
classified as air (threshold too high).We propose a pipeline model which creates a
reproducible output using common 3D image processing filters: First of all we use
an edge preserving diffusion filter to reduce noise without blurring the edges of the
specimen. Furthermore, a watershed segmentation filter is applied to the gradient
image in order to extract a binary volume of the dataset. In the final step the surface
model is constructed using an advanced approach called elastic surface nets. The
major contribution of this paper is the development of the specific processing
pipeline for extracting surface models of homogeneous industrial components and to
handle large resolution data of industrial CT scanners. The pipeline is crucial for the
following visual inspection of deviations.

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