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从牙科CT图像中快速准确地半自动分割单个牙齿

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

作者信息

Kang Ho Chul, Choi Chankyu, Shin Juneseuk, Lee Jeongjin, Shin Yeong-Gil

机构信息

Palette Soft Inc., 599 Kwanak-ro, Kwanak-gu, Seoul 151-742, Republic of Korea.

Planet SK Planet Co., Ltd., Bundang-gu, 264 Pangyo-ro, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea.

出版信息

Comput Math Methods Med. 2015;2015:810796. doi: 10.1155/2015/810796. Epub 2015 Aug 27.

DOI:10.1155/2015/810796
PMID:26413143
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4564792/
Abstract

In 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 2.29 ± 0.56%, 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.

摘要

在本文中，我们提出了一种快速且准确的半自动方法，以有效区分牙科CT图像中牙齿与牙槽窝中的单个牙齿。基于参考图像中分离出的牙齿，阈值参数值和牙齿形状会传播到相邻切片。在牙齿阈值和形状传播之后，对当前切片的直方图进行分析。通过使用种子区域生长法自动分离并分割单个牙齿。然后，将新生成的分离信息迭代传播到相邻切片。我们的方法通过十组牙科CT扫描进行了验证，并将结果与手动分割结果和传统方法进行了比较。体积测量绝对值的平均误差为2.29±0.56%，比传统方法更准确。使用多核处理器提高速度后，显示比单核处理器快2.4倍。所提出的方法能够准确识别单个牙齿，表明它可以在牙科手术期间为牙医提供实质性帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/3b97756b474a/CMMM2015-810796.pseudo.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/87758b1e69a8/CMMM2015-810796.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/e7bda751a787/CMMM2015-810796.pseudo.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/3b97756b474a/CMMM2015-810796.pseudo.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/87758b1e69a8/CMMM2015-810796.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/ef2820751fae/CMMM2015-810796.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/60ddb0b8fdeb/CMMM2015-810796.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/456e1126873d/CMMM2015-810796.004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/6bcbda296336/CMMM2015-810796.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/6440eeccf22c/CMMM2015-810796.014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/e7bda751a787/CMMM2015-810796.pseudo.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1576/4564792/3b97756b474a/CMMM2015-810796.pseudo.002.jpg

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