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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 810796, 12 pages
Research Article

Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images

1Palette Soft Inc., 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea
2Planet SK Planet Co., Ltd., Bundang-gu, 264 Pangyo-ro, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea
3Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 440-746, Republic of Korea
4School of Computer Science & Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea
5School of Computer Science and Engineering, Seoul National University, 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea

Received 5 May 2015; Revised 3 August 2015; Accepted 5 August 2015

Academic Editor: Po-Hsiang Tsui

Copyright © 2015 Ho Chul Kang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


DIn this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was , which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery.