Special Section on Quality Control by Artificial Vision

Automatic classification of three-dimensional segmented computed tomography data using data fusion and support vector machine

[+] Author Affiliations
Ahmad Osman

Fraunhofer Development Center X-ray Technologies EZRT, Dr-Mack-Str. 81, 90762 Fuerth, Germany

Valérie Kaftandjian

University of Lyon, National Institute of Applied Sciences INSA-Lyon, Non-Destructive Testing Using Ionising Radiations Laboratory CNDRI, Bat. St. Exupery, F-69621 Villeurbanne, France

Ulf Hassler

Fraunhofer Development Center X-ray Technologies EZRT, Dr-Mack-Str. 81, 90762 Fuerth, Germany

J. Electron. Imaging. 21(2), 021111 (May 07, 2012). doi:10.1117/1.JEI.21.2.021111
History: Received July 20, 2011; Revised November 30, 2011; Accepted January 25, 2012
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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.

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Citation

Ahmad Osman ; Valérie Kaftandjian and Ulf Hassler
"Automatic classification of three-dimensional segmented computed tomography data using data fusion and support vector machine", J. Electron. Imaging. 21(2), 021111 (May 07, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.2.021111


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