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.
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.
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