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A CBCT series slice image segmentation method.

Jia Zheng1, Dinghua Zhang1, Kuidong Huang1

  • 1Key Lab of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an Shaanxi, China.

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|July 25, 2018
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Summary
This summary is machine-generated.

This study introduces an adaptive Phansalkar method to precisely segment industrial cone-beam computed tomography (CBCT) images, overcoming noise and artifacts for improved accuracy in defect detection.

Keywords:
Image segmentationindustrial CBCT systemnon-destructive inspection

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Area of Science:

  • Industrial imaging
  • Image processing
  • Computer vision

Background:

  • Industrial cone-beam computed tomography (CBCT) images often suffer from noise and beam hardening artifacts.
  • These image degradations present significant challenges for accurate segmentation of regions of interest.

Purpose of the Study:

  • To enhance the segmentation precision of industrial CBCT series slice images.
  • To develop a robust method capable of handling noise and artifacts in CBCT data.

Main Methods:

  • A novel adaptive accumulated Phansalkar method is proposed, collecting pixel classification from various local windows.
  • Adaptive local window size determination is achieved using bimodal histogram distribution.
  • Segmentation is refined by adaptively processing accumulated probabilities.

Main Results:

  • The new method effectively segments CBCT images with noise and beam hardening.
  • It achieved superior segmentation performance, evidenced by the highest BF (1) and AOM (0.9981) scores with minimal standard deviation (0.0013).
  • Outperformed existing methods like CMF, MS, DRLSE, and ARKFCM in segmentation accuracy.

Conclusions:

  • The developed method offers superior precision in segmenting noisy and artifact-laden CBCT images compared to existing techniques.
  • This approach holds significant potential for industrial CBCT image post-processing systems.