An Effective Dental Shape Extraction Algorithm Using Contour Information and Matching by Mahalanobis Distance

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 ide...

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Bibliographic Details
Published in:Journal of digital imaging 2013-04, Vol.26 (2), p.259-268
Main Authors: Pushparaj, Vijayakumari, Gurunathan, Ulaganathan, Arumugam, Banumathi
Format: Article
Language:English
Subjects:
Algorithms
Biometry - methods
Dental Records
Extraction
Forensic Anthropology - methods
Forensic Dentistry - methods
Humans
Imaging
Marking
Matching
Mathematical analysis
Medicine
Medicine & Public Health
Phantoms, Imaging
Preprocessing
Radiographic Image Interpretation, Computer-Assisted
Radiographs
Radiography, Dental, Digital - methods
Radiology
Shape
Tooth - diagnostic imaging
Tooth Extraction
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Summary: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.
ISSN:0897-1889
1618-727X
DOI:10.1007/s10278-012-9492-4