Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth

Nomir, Omaima; Abdel-Mottaleb, Mohamed

doi:10.1109/tifs.2007.897245

Cited by 95 publications

(34 citation statements)

References 22 publications

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“…It was concluded that this method was extremely fast and yielded positive results, however it was negatively impacted by noisy images and X-ray artifacts, thus requiring pre-processing to be done. If the images allow for this method it proves highly effective, as in a later collaboration Nomir et al [31] was able to use this segmentation method for human identification. Lin et al [32] also used an adaptation of the method presented in Ref.…”

Section: Segmentation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

Osterloh

Viriri

2016

Advances in Visual Computing

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Section: Segmentation Methodsmentioning

confidence: 99%

“…The number of teeth present ranges from 20 primary (milk) teeth in children to [28][29][30][31][32] permanent teeth in adults [19]. The variance is due to the mandibular third molars (wisdom teeth) which may not form in some of the population and in some cases where formation does occur, may not erupt from the gum line.…”

Section: Anatomy Of a Toothmentioning

confidence: 99%

Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

Osterloh

Viriri

2016

Advances in Visual Computing

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“…The partitioned image from the previous stage is subjected through median filter to remove some unwanted information even after preprocessing and then edge map is created using canny operator [6]. Then, it is convolved with the following proposed mask to trace the shape of the desired dental pattern.…”

Section: Connected Component Labelingmentioning

confidence: 99%

“…Paper [5] explains three different fusion approaches for the matching algorithms to get better matching between ante-and post-mortem images. Human identification using the shape and appearance of the tooth is explained in [6]. Multiresolution wavelet-Fourier descriptors are used to classify the teeth sequence in multislice computed tomography images as proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

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

Pushparaj¹,

Ulaganathan²,

Vijayakumari³

2012

J Digit Imaging

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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.

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“…Both techniques use teeth contour features (shape based); the automatic extraction of teeth contour is computationally intensive and the output itself may not be precise [8]. Nomir and AbdelMottaleb [9] describe a fast retrieval procedure based on teeth contour as one of their basic features. However, their technique can handle only bitewing films, which represents only ∼ 56% of the available database [10].…”

Section: Fig 1 Examples Of Dental Recordsmentioning

confidence: 99%

Retrieving dental radiographs for post-mortem identification

Abaza

Ross

Ammar

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Automating the process of postmortem identification of deceased individuals based on dental characteristics is receiving increased attention. With the large number of victims encountered in mass disasters (e.g., Asian tsunami), automating the identification process would enhance the scalability of this biometric. However, archiving and retrieving dental records from large databases is a challenging task and has received inadequate attention in the literature. This paper concerns itself with the task of efficient fast retrieving dental records from a database in order to assist the forensic expert in identifying deceased individuals in a rapid manner. The proposed method is an appearance-based technique that consolidates the evidence presented by individual teeth in a dental record, i.e., it 'moves' from tooth-to-tooth in order to render a recordto-record matching score. The proposed method is shown to reduce the searching time of record-to-record matching by a factor of hundred. Experimental results indicate that the proposed approach requires significantly less time compared to the other approaches suggested in the literature thereby underscoring its relevance in real-time applications.

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Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth

Cited by 95 publications

References 22 publications

Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

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

Retrieving dental radiographs for post-mortem identification

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