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Journal of Electronic Imaging

Automatic classification of three-dimensional segmented computed tomography data using data fusion and support vector machine
Author(s): Ahmad Osman; Ulf Hassler; Valerie Kaftandjian
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Paper Abstract

The three dimensional (3D) X-ray computed tomography (3D-CT) has proven its successful application as an inspection method in nondestructive testing. The generated 3D volume uses high efficiency reconstruction algorithms containing all required information on the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions that need to be classified as defective or false alarms. This paper deals with the classification step using data fusion theory, which was successfully applied on 2D X-ray data in previous work along with a support vector machine (SVM). For this study we chose a 3D-CT dataset of aluminium castings that needs to be fully inspected via X-ray CT to ensure their quality. We achieved a true classification rate of 97% on a validation dataset, which proves the effectiveness of the data fusion theory as a method to build a better classifier. Comparison with SVMs shows the importance of selecting the most pertinent features to improve the classifier performance and attaining 98% of true classification rate.

Paper Details

Date Published: 7 May 2012
PDF: 9 pages
J. Electron. Imaging. 21(2) 021111 doi: 10.1117/1.JEI.21.2.021111
Published in: Journal of Electronic Imaging Volume 21, Issue 2
Show Author Affiliations
Ahmad Osman, Fraunhofer-Institut für Integrierte Schaltungen (Germany)
Ulf Hassler, Fraunhofer-Institut für Integrierte Schaltungen (Germany)
Valerie Kaftandjian, Institut National des Sciences Appliquées (France)

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