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[A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting].

Shiwei Liu1, Yuanjun Wang2

  • 1School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
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This study introduces an improved algorithm for precise oral teeth segmentation, crucial for dental surgery. The method accurately segments complex tooth structures, including roots and molars, enhancing diagnostic capabilities.

Keywords:
cone beam computed tomographyedge detectionlocal Gaussian distribution fittingtooth segmentation

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate oral teeth segmentation is vital for orthodontic and implant surgeries.
  • Complex dental anatomy, including tooth roots and pulp chambers, presents segmentation challenges like over-segmentation and inner edge artifacts.

Purpose of the Study:

  • To develop an advanced segmentation algorithm for improved accuracy in dental imaging.
  • To address limitations of existing methods in segmenting complex tooth structures and surrounding tissues.

Main Methods:

  • A novel segmentation algorithm integrating local Gaussian distribution fitting and edge detection.
  • Incorporation of local pixel variance, mean values, and gradient information for enhanced robustness.

Main Results:

  • Precise segmentation of tooth roots in cone beam computed tomography (CBCT) images.
  • Successful differentiation between tooth roots and surrounding alveolar bone.
  • Accurate segmentation of split molars without artifacts around the pulp chamber.

Conclusions:

  • The proposed algorithm significantly improves oral teeth segmentation accuracy.
  • This method offers a robust solution for complex dental structures, beneficial for surgical planning.