Human identification using dental radiographs is important in biometrics. Dental radiographs are mainly helpful for individual and mass disaster identification. In the 2004 tsunami, dental records were proven as the primary identifier of victims. So, this work aims to produce an automatic person identification system with shape extraction and matching techniques. For shape extraction, the available information is edge details, structural content, salient points derived from contours and surfaces, and statistical moments. Out of all these features, tooth contour information is a suitable choice here because it can provide better matching. This proposed method consists of four stages. The first step is preprocessing. The second one involves integral intensity projection for segmenting upper jaw, lower jaw, and individual tooth separately. Using connected component labeling, shape extraction was done in the third stage. The outputs obtained from the previous stage for some misaligned images are not satisfactory. So, it is improved by fast connected component labeling. The fourth stage is calculating Mahalanobis distance measure as a means of matching dental records. The matching distance observed for this method is comparatively better when it is compared with the semi-automatic contour extraction method which is our earlier work.
Dentistry can contribute for the identification of human remains after any disasters or crimes in assistance to other medical specialties. The algorithm can be developed by comparing post mortem and ante mortem dental radiographs. This work aims to introduce photographic images in addition to radiographs. In this research a contour and skeleton-based shape extraction as well as matching algorithm for dental images is proposed. An active contour model with selective binary and Gaussian filtering regularised level set method is used for contour extraction. Shape matching is done by both contour and skeleton-based approaches. The experimental results are obtained from a database of dental images include both radiographs and photographs. This algorithm provides better matching decision about the person than the existing algorithms since it includes skeleton measures also. The performance measures obtained and the hit-rate indicates that the better matching is observed with radiographic than the photographic images.
In the current scenario, identifying a person in mass disasters is a challenging problem if adequate biometric information is not available. It can be handled by using dental radiographs as a suitable biometric means in such a circumstance. Forensic Odontology is a branch under biometrics which uses dental radiographs for human identification. In this paper an attempt is made to develop an algorithm for teeth numbering and classification, which is a major section in Automated Dental Identification system. Individual identification using dental panoramic images is an issue addressed in the literature. Hence, this work makes use of panoramic images for emerging this algorithm. The dental radiographs are initially preprocessed and each tooth is isolated for further processing. This image is subjected to Support Vector Machine classifier to segregate the tooth as Molar or Premolar. Then template matching algorithm followed by Universal numbering system is used to number the teeth. Experimental results show that this algorithm has numbering accuracy of 93.3% for Molar and 92% for premolar.
Forensic dentistry involves the identification of people based on their dental records, mainly available as radiographic images. Human identification is done by matching the given post-mortem radiographs with the antemortem images. In this paper, a computer-aided dental identification system for matching dental records is presented. The tooth contour is used as a feature for matching here. The proposed algorithm consists of five stages. As an initial step, the image is preprocessed. Then it is isolated as individual tooth by radiograph segmentation. Shape extraction is made using connected component labeling. Finding similarity metric is the next step. Various distance measures are also applied to find better matching. Finally candidate matching is done by obtaining the percentage of match between the original and extracted shape using both the similarity and distance measures. Experimental results show that the finer matching distance is observed by the distance metric rather than similarity measures. The experimental results are obtained on a database of 100 dental images which includes both periapical and bitewing. The results show that a high hit rate is observed for the Euclidean distance measure and which is comparable with the other methods.
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