3D segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology

Yu Bing Chang, James J. Xia, Peng Yuan, Tai Hong Kuo, Zixiang Xiong, Jaime Gateno, Xiaobo Zhou

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Recent advances in cone-beam computed tomography (CBCT) have rapidly enabled widepsread applications of dentomaxillofacial imaging and orthodontic practices in the past decades due to its low radiation dose, high spatial resolution, and accessibility. However, low contrast resolution in CBCT image has become its major limitation in building skull models. Intensive hand-segmentation is usually required to reconstruct the skull models. One of the regions affected by this limitation the most is the thin bone images. This paper presents a novel segmentation approach based on wavelet density model (WDM) for a particular interest in the outer surface of anterior wall of maxilla. Nineteen CBCT datasets are used to conduct two experiments. This mode-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 ± 0.2 mm of surface error from ground truth of bone surface.

Original languageEnglish (US)
Pages (from-to)251-282
Number of pages32
JournalJournal of X-Ray Science and Technology
Volume21
Issue number2
DOIs
StatePublished - 2013

Keywords

  • 3D segmentation
  • active shape model (ASM)
  • cone-beam computed tomography (CBCT)
  • craniomaxillofacial (CMF) surgeries
  • statistical shape model (SSM)

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Condensed Matter Physics
  • Radiation
  • Electrical and Electronic Engineering
  • Instrumentation

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