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Proceedings Paper

Method for nondestructive testing using multiple-energy CT and statistical pattern classification
Author(s): Murillo Rodrigo Petrucelli Homem; Nelson D. A. Mascarenhas; Paulo Estevao Cruvinel
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Paper Abstract

This paper reports on how multiple energy techniques in X and gamma-ray CT scanning are able to provide good results with the use of Statistical Pattern Classification theory. We obtained a set of four images with different energies (40, 60, 85 and 662 keV) containing aluminum, phosphorus, calcium, water and plexiglass, with a minitomograph scanner for soil science. We analyzed those images through both a supervised classifier based on the maximum-likelihood criterion under the multivariate Gaussian model and a supervised contextual classifier based on the ICM (iterated conditional modes) algorithm using an a priori Potts-Strauss model. A comparison between them was performed through the statistical kappa coefficient. A feature selection procedure using the Jeffries- Matusita (J-M) Distance was also performed. Both the classification and the feature selection procedures were found to be in agreement with the predicted discrimination given by the separation of the linear attenuation coefficient curves for different materials.

Paper Details

Date Published: 18 October 1999
PDF: 9 pages
Proc. SPIE 3808, Applications of Digital Image Processing XXII, (18 October 1999); doi: 10.1117/12.365879
Show Author Affiliations
Murillo Rodrigo Petrucelli Homem, Univ. Federal de Sao Carlos (Brazil)
Nelson D. A. Mascarenhas, Univ. Federal de Sao Carlos (Brazil)
Paulo Estevao Cruvinel, Brazilian Agricultural Research Corp. (Brazil)


Published in SPIE Proceedings Vol. 3808:
Applications of Digital Image Processing XXII
Andrew G. Tescher, Editor(s)

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