Application of texture filtering in clustering of x-ray computed tomography data of products from polymer composite materials

X-ray computed tomography (XCT) is one of the most informative methods of nondestructive testing of polymer composite materials (PCM) and products made of them. One of the important stages of XCT of PCM products is segmentation, the automation of which is of research interest. In the segmentation process it is important to identify isotexture zones containing local X-ray density variations. In this paper we investigated the possibilities of three-dimensional texture filtering (Gaussian filter, Gabor filters) in clustering of X-ray computed tomography data by simple linear iterative clustering (SLIC) algorithm and evaluated their efficiency in terms of parameters: the share of mismatches between the boundaries of clusters and the boundaries of segmented areas and sphericity of clusters, as well as the performance in terms of time to partition the dataset into the required number of clusters. The results of the study show that the application of three-dimensional texture filters improves the clustering accuracy and sphericity of isotexture clusters of PCM product XCT data without a significant increase in clustering time compared to the raw data. The maximum increase in clustering accuracy was observed when using a combination of Gaussian and Gabor filters, while clustering time increased.