Retrieving dental radiographs for post-mortem identification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic detection and classification of teeth in CT data
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
| Hi-index | 0.00 |
Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). An equally important problem is the construction of a dental chart, which is a data structure that guides tooth-to-tooth matching. Dental charts are the key for avoiding illogical comparisons that inefficiently consume the limited computational resources and may mislead decision making. Labeling of the teeth is a challenging task which has received inadequate attention in the literature. We tackle this composite problem using a two-stage approach. The first stage utilizes low computational cost, appearance-based features for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and, hence, to assign each tooth a number corresponding to its location in the dental chart. Based on a large test dataset of 507 bitewing and periapical films that contain 2027 teeth, the proposed approach achieves classification accuracy of 87%. Experimental results indicate that the proposed approach works very fast, and achieves high performance compared to other approaches suggested in the literature.