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CT image crack segmentation method based on linear feature enhancement.

Zhi-Bin Zhang1,2, Yong-Ning Zou1,2, Ye-Ling Huang1,2

  • 1Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing, China.

Journal of X-Ray Science and Technology
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for industrial computed tomography (CT) crack segmentation. The method enhances linear features to improve accuracy, achieving high intersection-over-union (IOU) and F1 scores.

Keywords:
Image segmentationhessian matrixindustrial CT imagetotal variational model

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Area of Science:

  • Materials Science
  • Image Processing
  • Non-Destructive Testing

Background:

  • Industrial computed tomography (CT) crack segmentation is crucial for quality control.
  • Artifacts and noise in CT images significantly hinder accurate crack segmentation.
  • Existing methods often struggle with the complex features of cracks in industrial CT data.

Purpose of the Study:

  • To develop and validate a novel crack segmentation algorithm for industrial CT images.
  • To enhance the accuracy of crack detection by focusing on linear feature extraction.
  • To address the challenges posed by noise and artifacts in CT-based crack analysis.

Main Methods:

  • Denoising CT images using a total variational model.
  • Extracting linear structures with a Frangi multiscale filter for contrast enhancement.
  • Segmenting cracks using the Otsu algorithm based on enhanced linear features.

Main Results:

  • The proposed algorithm achieved an average intersection-over-union (IOU) of 86.10%.
  • An average F1 score of 92.44% was obtained, demonstrating high segmentation accuracy.
  • The algorithm proved effective in segmenting cracks in challenging industrial CT images.

Conclusions:

  • The developed algorithm significantly improves crack segmentation accuracy in industrial CT images.
  • Linear feature enhancement is an effective strategy for overcoming noise and artifacts.
  • The method shows strong potential for practical application in industrial non-destructive testing.