講演抄録/キーワード |
講演名 |
2008-01-26 15:15
Automatic Teeth Recognition in Multi-Slice CT Images Maryam Momeni・○Reza A. Zoroofi(Univ. of Tehran)・Yoshinobu Sato(Osaka Univ.) MI2007-146 |
抄録 |
(和) |
teeth are unique in identification of deceased persons where other biometric features are not applicable. In many cases, e.g., fire victims, the conventional biometric features such as face, fingerprint, iris, etc. may not be available. In this research, we present a technique for automatic recognition of teeth in multi-slices CT images. Teeth classification is performed by proposing appropriate segmentation, feature extraction and classification techniques. We segment a tooth by a hybrid approach including anatomical based histogram thresholding, panaromic resampling, and Level-Set techniques. We perform feature extraction by employing the following techniques; (1) calculating the Eigen-Values of each tooth by a Dirichlet Laplacian technique; (2) providing the spectral features by Fourier and wavelet descriptors; and (3) determining statistical moments utilizing the teeth intensity range. Experimental results revealed that the technique was successful to automatically classify teeth in more than 93% of the cases in the lower and upper jaws. Our method is independent of the anatomical information such as the sequence and locality of the teeth in jaws. |
(英) |
teeth are unique in identification of deceased persons where other biometric features are not applicable. In many cases, e.g., fire victims, the conventional biometric features such as face, fingerprint, iris, etc. may not be available. In this research, we present a technique for automatic recognition of teeth in multi-slices CT images. Teeth classification is performed by proposing appropriate segmentation, feature extraction and classification techniques. We segment a tooth by a hybrid approach including anatomical based histogram thresholding, panaromic resampling, and Level-Set techniques. We perform feature extraction by employing the following techniques; (1) calculating the Eigen-Values of each tooth by a Dirichlet Laplacian technique; (2) providing the spectral features by Fourier and wavelet descriptors; and (3) determining statistical moments utilizing the teeth intensity range. Experimental results revealed that the technique was successful to automatically classify teeth in more than 93% of the cases in the lower and upper jaws. Our method is independent of the anatomical information such as the sequence and locality of the teeth in jaws. |
キーワード |
(和) |
Teeth recognition / Teeth segmentation / Level-Set / Dirichlet Laplacian / Spectral Descriptors / / / |
(英) |
Teeth recognition / Teeth segmentation / Level-Set / Dirichlet Laplacian / Spectral Descriptors / / / |
文献情報 |
信学技報, vol. 107, no. 461, MI2007-146, pp. 449-456, 2008年1月. |
資料番号 |
MI2007-146 |
発行日 |
2008-01-18 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します.(許諾番号:10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
PDFダウンロード |
MI2007-146 |
|