CT image crack segmentation method based on linear feature enhancement.

Industrial computed tomography (CT) crack segmentation is a key technology in industrial CT image processing. Unfortunately, the interference of artifact and noise in CT image often bring great trouble to the crack segmentation. In order to improve the segmentation accuracy of cracks in CT images, we propose to develop and test a new crack segmentation algorithm based on linear feature enhancement by analyzing the features of cracks in CT images. Firstly, the total variational model is used to denoise the input image. Next, a Frangi multiscale filter is used to extract linear structures in the image, and then the extracted linear structures are used to enhance the contrast of the image. Finally, the cracks in the image are detected and segmented by Otsu algorithm. By comparing with the manual segmentation results, the average intersection-over-union (IOU) reaches 86.10% and the average F1 score reaches 92.44%, which verifies the effectiveness and correctness of the algorithm developed in this study. Overall, experiments demonstrate that the new algorithm improves the accuracy of crack segmentation and it is effective applying to industry CT images.

[Chemical constituents of ethyl acetate fraction of Tetrastigma planicaule].

OBJECTIVE: To investigate the chemical constituents of ethyl acetate of Tetrastigma planicaule. METHODS: The ethyl acetate fraction was isolated and purified by repeated silica gel chromatography and recrystallization. The structures of compounds were identified by physical-chemical methods and spectral data such as MS, 1H-NMR, 13C-NMR, etc. RESULTS: Six compounds were identified as gheddic acid (I), tricosanol (II), beta-sitosterol (III), palmitic acid (IV), ethyl gallate (V) and vanillin (VI), respectively. CONCLUSION: Compounds I, II, V and VI are isolated from this plant for the first time.