Purpose Teeth are one of the most important anatomical components in ergonomic appearance of the face and crucial in craniofacial surgeries and dentistry. Besides, teeth have specific roles in forensic to identify death persons. In this paper, we propose a hybrid technique to classify teeth in computed tomography (CT) dental images. Method Our method consists of three steps: segmentation, feature extraction and classification. We segment the teeth by a hybrid approach including anatomical-based histogram thresholding, panaromic resampling and level-set techniques. In the second step, for each segmented tooth, we calculate the 2D discrete wavelet transform. We then determine 1D inverse discrete wavelet transform by the same mother wavelet, calculate energy of decomposition and approximation coefficients. Next,we utilize these coefficients as feature vectors for classification in the third step. Teeth classification is performed by a conventional feed forward neural network. Results The proposed method is evaluated in the presence of 180 teeth. Experimental results reveal that the technique is effective to automatically classify teeth, on an average, in more than 95% of the cases in both jaws. Conclusion Our method is independent of anatomical information such as the sequence and locality of the teeth in jaws. The techniques are applicable to both forensic and dentistry investigations.
