Method for nondestructive testing using multiple-energy CT and statistical pattern classification

Murillo Rodrigo Petrucelli Homem; Nelson D. A. Mascarenhas; Paulo Estevao Cruvinel

doi:10.1117/12.365879

18 October 1999 Method for nondestructive testing using multiple-energy CT and statistical pattern classification

Murillo Rodrigo Petrucelli Homem, Nelson D. A. Mascarenhas, Paulo Estevao Cruvinel

Proceedings Volume 3808, Applications of Digital Image Processing XXII; (1999) https://doi.org/10.1117/12.365879
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

Citation Download Citation

Murillo Rodrigo Petrucelli Homem, Nelson D. A. Mascarenhas, and Paulo Estevao Cruvinel "Method for nondestructive testing using multiple-energy CT and statistical pattern classification", Proc. SPIE 3808, Applications of Digital Image Processing XXII, (18 October 1999); https://doi.org/10.1117/12.365879

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