Tooth segmentation using Gaussian mixture model and genetic algorithm

Joo Young Kim, Sun K. Yoo, W. S. Jang, Byung Eun Park, Wonse Park, Kee Deog Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background: The present study suggested an image segmentation method for dental cone beam computed tomography (CBCT) data with a proposed preprocessing step and genetic algorithm. Segmentation of dental CT images is often hampered by the proximity of teeth and alveolar bones that display similar brightness. The present study sought to overcome this difficulty by using a Gaussian mixture model (GMM) and contrastlimited adaptive histogram equalization (CLAHE) in the preprocessing step. First, the original dental image was processed by GMM to eliminate regions other than the teeth and alveolar bones. Then, we composed the preprocessed image by enhancing tooth contours through application of CLAHE. Finally, tooth and pulp regions were extracted via the evolutionary process of genetic algorithm. We confirmed that tooth segmentation using a genetic algorithm was effective in segmenting teeth that are adjacent and have similar shapes and brightness.

Original languageEnglish
Pages (from-to)1271-1276
Number of pages6
JournalJournal of Medical Imaging and Health Informatics
Issue number6
Publication statusPublished - 2017 Oct

Bibliographical note

Publisher Copyright:
© 2017 American Scientific Publishers All rights reserved.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Health Informatics


Dive into the research topics of 'Tooth segmentation using Gaussian mixture model and genetic algorithm'. Together they form a unique fingerprint.

Cite this