Defect volume measurement in 3D CT image based on improved SIFCM and region growing
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TP391

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    Abstract:

    This paper proposed an internal defect volume automatic measurement algorithm based on the improved spatial intuitionistic fuzzy C-means clustering (NL-SIFCM) and 3D region growing for measuring the volume of holes and cavities in 3D CT images of workpieces. Firstly, the acquired 3D CT images are pre-processed. Subsequently, NL-SIFCM was used to segment on 3D CT image to obtain a binarized defect image set, while a fast algorithm was obtained for 3D CT image having spatial similarity between slices. Finally, the binary defect image set for 3D region growing to obtain the defect voxel number and spatial structure, and the defect spatial structure is displayed in the 3D visualization software to assist inspectors in analyzing defects. The experimental results show that the measured volume of the standard spherical volume used to simulate defects has a relative error of less than 1. 0%, indicating a high level of measurement accuracy. The applicability of the algorithm has been validated by actual workpiece inspection, demonstrating its effectiveness in meeting the demands of CT inspection.

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  • Online: September 22,2023
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