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基于小波-傅里叶描述子的多层 CT 图像中牙齿的分类与编号。

Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor.

机构信息

Faculty of Engineering, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran.

出版信息

Int J Comput Assist Radiol Surg. 2010 May;5(3):237-49. doi: 10.1007/s11548-009-0389-8. Epub 2009 Jul 29.

Abstract

PURPOSE

Teeth arrangement is essential in face ergonomics and healthiness. In addition, they play key roles in forensic medicine. Various computer-assisted procedures for medical application in quantitative dentistry require automatic classification and numbering of teeth in dental images.

METHOD

In this paper, we propose a multi-stage technique to classify teeth in multi-slice CT (MSCT) images. The proposed algorithm consists of the following three stages: segmentation, feature extraction and classification. We segment the teeth by employing several techniques including Otsu thresholding, morphological operations, panoramic re-sampling and variational level set. In the feature extraction stage, we follow a multi-resolution approach utilizing wavelet-Fourier descriptor (WFD) together with a centroid distance signature. We compute the feature vector of each tooth by employing the slice associated with largest tooth tissues. The feature vectors are employed for classification in the third stage. We perform teeth classification by a conventional supervised classifier. We employ a feed- forward neural network classifier to discriminate different teeth from each other.

RESULTS

The performance of the proposed method was evaluated in the presence of 30 different MSCT data sets including 804 teeth. We compare classification results of the WFD technique with Fourier descriptor (FD) and wavelet descriptor (WD) techniques. We also investigate the invariance properties of the WFD technique. Experimental results reveal the effectiveness of the proposed method.

CONCLUSION

We provided an integrated solution for teeth classification in multi-slice CT datasets. In this regard, suggested segmentation technique was successful to separate teeth from each other. The employed WFD approach was successful to discriminate and numbering of the teeth in the presence of missing teeth. The solution is independent of anatomical information such as knowing the sequence of teeth and the location of each tooth in the jaw.

摘要

目的

牙齿排列对于面部工程学和健康至关重要。此外,它们在法医学中也起着关键作用。各种用于定量牙科学的计算机辅助医学程序都需要对牙部图像中的牙齿进行自动分类和编号。

方法

在本文中,我们提出了一种用于多层 CT(MSCT)图像中牙齿分类的多阶段技术。所提出的算法包括以下三个阶段:分割、特征提取和分类。我们通过使用包括 Otsu 阈值、形态操作、全景重采样和变分水平集在内的几种技术来分割牙齿。在特征提取阶段,我们采用多分辨率方法,结合小波-傅里叶描述符(WFD)和质心距离特征。我们通过使用与最大牙齿组织相关联的切片来计算每个牙齿的特征向量。在第三阶段,我们使用特征向量进行分类。我们采用传统的监督分类器进行牙齿分类。我们使用前馈神经网络分类器来区分不同的牙齿。

结果

我们在 30 个不同的 MSCT 数据集(包括 804 颗牙齿)中评估了该方法的性能。我们将 WFD 技术的分类结果与傅里叶描述符(FD)和小波描述符(WD)技术进行了比较。我们还研究了 WFD 技术的不变性特性。实验结果表明了该方法的有效性。

结论

我们提供了一种用于多层 CT 数据集的牙齿分类的集成解决方案。在这方面,所提出的分割技术成功地将牙齿彼此分开。所采用的 WFD 方法成功地在缺失牙齿的情况下对牙齿进行了区分和编号。该解决方案不依赖于解剖学信息,例如了解牙齿的顺序和每个牙齿在颌骨中的位置。

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