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)

Abstract

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
Volume7
Issue number6
DOIs
Publication statusPublished - 2017 Oct

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by the R&D Program of the Ministry of Trade, Industry and Energy/Korea Evaluation Institute of Industrial Technology (Grant No. MOTIE/KEIT 10048528, Development of ICT based Wireless Ultrasound Solution for Point-of-Care Applications).

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

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Health Informatics

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