Application of hyperspectral CT technology combined with machine learning in recognition of plastic components

Abstract

Abstract A new method based on a hyperspectral X-ray CT (HXCT) system is proposed in this paper to identify the material composition in a region of interest (ROI) during nondestructive testing (NDT). The HXCT system (based on the principle of photon counting) was used to scan the samples containing 10 kinds of plastic components, and the Algebraic Reconstruction Technique (ART) was applied to compute the tomography images. The X-ray absorption spectra (XAS) corresponding to the 360 pixel points in the tomography images were reconstructed. After wavelet denoising and principal component analysis (PCA) preprocessing, machine learning algorithms named Artificial Neural Network (ANN) and Support Vector Machine (SVM) were used to construct the model for classification. The highest ten-fold cross-validation accuracy of material identification in ROI reached 80.00%. It is demonstrated that NDT using hyperspectral CT technology based on the photon counting principle can not only perform structural analysis and defect detection, but also identify the components in the ROI, which has important research significance in radiology research.