Dental biometrics: Human identification based on teeth and dental works in bitewing radiographs

Lin, Phen-Lan; Lai, Yan-Hao; Huang, Pengfei

doi:10.1016/j.patcog.2011.08.027

Cited by 57 publications

(24 citation statements)

References 17 publications

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“…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. [29] for use in human identification.…”

Section: Segmentation Methodsmentioning

confidence: 99%

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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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“…[13], [14], [15] Pushparaj et al [16] suggest using Butterworth high-pass filter together with homomorphic filtering, a frequency-space solution to the problem.…”

Section: ) Enhancementmentioning

confidence: 99%

“…This method has been used extensively by other authors [8], [11], [14], [20], [21], [22], [23], [24]. The iterative thresholding step bases the initial threshold value on the average of the edge pixels detected VOLUME Some have used only part of the method such as iterative thresholding [15], or adaptive thresholding [5], [10], after en-hancement. A version of this method is used by Zhou & Abdel-Mottaleb [10] for segmenting both the teeth and the bones between teeth.…”

Section: ) Enhancementmentioning

confidence: 99%

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Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

Ølberg

Goodwin

2016

Scandinavian Journal of Forensic Science

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Abstract:Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray images and uses the Hausdorff-distance measure for ranking persons. This combination of state-of-the-art approaches with a novel lowest cost path-based method for separating a dental X-ray image into individual teeth, is able to achieve comparable and better results than what is available in the literature. The proposed scheme is fully functional and is used to accurately identify people within a real dental database. The system is able to perfectly separate 88.7% of the teeth in the test set. Further, in the verification process, the system ranks the correct person in top in 86% of the cases, and among the top five in an astonishing 94% of the cases. The approach has compelling potential to significantly reduce the time spent on dental identification. Keywords:Path-finding, Human dental identification 44Copyright© Holstad Grafisk, Oslo -Print: prografia, Oslo -ISSN 1503-9552 FORENSIC SCIENCE Scandinavian journal of Nordisk rettsmedisinThe new separation method has the benefit of being able to recover from poor starting conditions, making it robust in cases where the teeth in the image are severely rotated or oddly shaped. A. Problem StatementThe ultimate goal of this work is to create an improved ADIS with new innovative methods as well as methods from related research. As part of the path towards this goal a few hypotheses will be tested. These can be listed as follows.1) It is possible to identify the persons in the data set using only tooth and dental work contour information extracted from the dental X-ray images.The particular combination of techniques and methods applied in this work has not been used to solve this problem before. This hypothesis helps find how the whole system performs with regards to ranking and retrieval accuracy.2) Pathfinding can be used effectively for separating teeth and jaws in dental X-ray images.The separation of dental X-ray images into smaller images each containing a single tooth is a significant problem during feature extraction from such images. B. OutlineFollowing the introduction, section II describes the previous work in the field. The proposed approach is presented in sec-tion III. Testing of both the new method and the methodology as a whole, with discussion, can be found in section IV. Subsequently, the conclusion is presented with a few thoughts on further work.

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

Osterloh

Viriri

2018

Computer Vision and Graphics

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Dental biometrics: Human identification based on teeth and dental works in bitewing radiographs

Cited by 57 publications

References 17 publications

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

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

Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

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

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