HIGH-PRECISION METHOD FOR PREDICTING LINEAR DIMENSIONS OF THREE-DIMENSIONAL OBJECTS OF ARBITRARY SHAPE BASED ON COMPUTED TOMOGRAPHY DATA

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Abstract
A high-precision method for predicting the linear dimensions of three-dimensional objects of arbitrary shape based on computed tomography data has been developed. The method is aimed at reducing measurement errors caused by partial-volume effects, shadow transitions, and arbitrary spatial orientation of the object. The features of voxel image formation for metallic fragments within a tomographic volume and the influence of non-uniform density distribution on the accuracy of boundary determination have been investigated. A segmentation algorithm has been proposed that combines threshold-based selection of high-density voxels, their clustering into connected three-dimensional sets using a wave-based method, and construction of the object boundary surface. The procedure for evaluating geometric parameters has been improved by introducing a three-layer density-gradient distribution model and using additional points within each voxel to predict coordinates in the shadow region. It has been established that accounting for the partial contribution of the object within shadow voxels makes it possible to reconstruct its actual contours more accurately than with standard segmentation. It has been found that the use of principal component analysis eliminates the influence of fragment orientation in space and enables correct determination of its principal axis. It has been shown that the combination of segmentation, boundary prediction, and alignment along the principal axis improves the accuracy of estimating the linear dimensions of metallic fragments from CT data and ensures the suitability of the method for automated analysis. It has been established that the average relative error in determining the length of the studied samples does not exceed 5 %, and the obtained results confirm the practical feasibility of the proposed approach. Prospects for further research have been identified, consisting in adapting the method for automated decision support systems and extending its application to other types of tomographic data.

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