Automatic segmentation of mandibular cortical bone on cone-beam CT images based on histogram thresholding and polynomial fitting

Rarasmaya Indraswari, Agus Zainal Arifin, Nanik Suciati, Eha Renwi Astuti, Takio Kurita

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Automatic segmentation of mandibular cortical bone is challenging due to the appearance of teeth that have similar intensity with the bone tissue and the variety of bone intensity. In this paper we propose a new method for automatic segmentation of mandibular cortical bone on cone-beam computed tomography (CBCT) images. The bone tissue is segmented by using Gaussian mixture model for histogram thresholding. The mandibular inferior cortical bone is obtained by incorporating several polynomial models to fit the structure of cortical bone on coronal slices. The buccal and lingual cortical plate is separated by using histogram thresholding for teeth elimination and polynomial fitting for shape extraction. After performing 3D reconstruction, the volumetric cortical bone is obtained. The proposed method gives average accuracy, sensitivity, and specificity value of 96.82%, 85.96%, 97.60%, respectively. This shows that the proposed method is promising for automatic and accurate segmentation of mandibular cortical bone on CBCT images.

Original languageEnglish
Pages (from-to)130-141
Number of pages12
JournalInternational Journal of Intelligent Engineering and Systems
Volume12
Issue number4
DOIs
Publication statusPublished - 2019

Keywords

  • Cone-beam computed tomography
  • Histogram thresholding
  • Mandibular cortical bone
  • Polynomial fitting
  • Segmentation

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