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GrabCut algorithm for dental X-ray images based on full threshold segmentation

机译：基于完全阈值分割的牙科X射线图像GrabCut算法

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摘要

Teeth are difficult to be destroyed due to their corrosion resistance, high melting point and hardness. Dental biometrics can therefore provide assistance in human forensic identification, especially to the unknown corpses. One of the key issue in dental based human identification is the segmentation of Dental X-ray images. In this paper, a novel segmentation algorithm has been proposed for this purpose. The proposed algorithm is based on full threshold segmentation. We first obtain the outline image setIwholenand crown image setIcrownmof the complete target tooth. Morphological open operation is then applied to the difference images ofIwholenandIcrownm. Subsequently, the most complete target tooth image and its corresponding crown image are selected. Getting independent target tooth imageIcontourand its crown imageIcrownfrom these two images. Median filtering is applied to the synthetic image ofIcontourandIcrown, and the resulted image will be used as the Mask for GrabCut to obtain the target tooth image. Experimental results show our proposed algorithm can effectively overcome the problems of uneven grayscale distribution and adhesion of adjacent crowns in dental X-ray images. It can also achieve a high segmentation accuracy and outperform related methods to be compared.
机译：牙齿由于其耐腐蚀性，高熔点和硬度而难以破坏。因此，牙科生物特征识别可以为人类法医鉴定提供帮助，尤其是对未知尸体。基于牙科的人类识别的关键问题之一是牙科X射线图像的分割。为此，提出了一种新颖的分割算法。该算法基于全阈值分割。我们首先获得轮廓图像集 n 全部 n n和皇冠图像集 n Icrown n m n。然后将形态学打开操作应用于 n 整个 n nand Icrown n m n。随后，选择最完整的目标牙齿图像及其对应的牙冠图像。获取独立的目标牙齿图像 n 我 n 轮廓 n及其冠状图片 n 我 n 皇冠 n来自这两个图片。中值过滤应用于 n 我 n 轮廓 nand n 我 n 冠冕 n，结果图像将用作GrabCut的蒙版以获取目标牙齿图像。实验结果表明，本文提出的算法能够有效克服牙齿X射线图像灰度分布不均匀和相邻牙冠粘连的问题。它还可以实现较高的细分精度，并且优于相关方法进行比较。

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  • 来源
    《Image Processing, IET》 |2018年第12期|2330-2335|共6页
  • 作者

    Jiafa Mao; Kaihui Wang; Yahong Hu; Weiguo Sheng; Qixin Feng;

  • 作者单位

    College of Computer Science and Technology, Zhejiang University of Technology, People's Republic of China;

    College of Computer Science and Technology, Zhejiang University of Technology, People's Republic of China;

    College of Computer Science and Technology, Zhejiang University of Technology, People's Republic of China;

    Hangzhou Normal University, People's Republic of China;

    Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, People's Republic of China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    biometrics (access control); dentistry; feature extraction; image segmentation; medical image processing;

    机译：生物识别（访问控制）;牙科;特征提取;图像分割;医学图像处理;

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