10.14489/td.2026.04.pp.012-019

2026, 04 April

DOI: 10.14489/td.2026.04.pp.012-019

Shirshin A. V., Fedorov A. V.
METHOD AND ALGORITHM FOR PROCESSING X-RAY COMPUTED TOMOGRAPHY DATA FOR AUTOMATIC SEGMENTATION OF POLYMER COMPOSITE MATERIALS PRODUCTS
(pp.12-19)

Abstract. Nowadays, additive technologies have made it possible to manufacture polymer composite materials (PCM) products with a programmable internal structure that determines the functional features of the products being created. To control the morphological structure of such products, X-ray computed tomography (XCT) is used, which allows obtaining data on the entire volume of the object being studied. At the same time, an important stage of control is the segmentation of zones of the same structure, the morphological features of which directly affect the quality of the product. Manual allocation of such zones has low productivity, and existing automation tools have a high margin of error in the case of allocation of various isotextural areas. In this paper, we propose and evaluate an algorithm for automatic segmentation of isotextural zones of PCM products based on XCT data based on machine learning methods, texture filtering and volumetric textual (radiomic) analysis, as well as its software implementation. It is noted that when using the proposed algorithm, the reliability of XCT data markup increases in comparison with the automated segmentation algorithm from 75.4 to 91.4 % with texture taken into account and from 86.7 to 92.9 % without texture (with object-background separation), and the time spent on segmentation (based on 100,000 voxels of data), decreases from 377.4 s for manual segmentation to 13.4 s for automatic segmentation. The application of the developed algorithm can improve the segmentation accuracy of the isotextural zones of PCM products relative to existing automated algorithms without significantly increasing the segmentation time.

Keywords: рolymer composite materials, additive technologies, X-ray computed tomography, segmentation, machine learning, radiomics, simple linear iterative clustering.

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A.V. Shirshin, A.V. Fedorov (ITMO National Research University, Saint Petersburg, Russia) E-mail: Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра. , Данный адрес e-mail защищен от спам-ботов, Вам необходимо включить Javascript для его просмотра.

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