J Adv Prosthodont.  2017 Dec;9(6):409-415. 10.4047/jap.2017.9.6.409.

A standardization model based on image recognition for performance evaluation of an oral scanner

• Seo SW
• Lee WS
• Byun JY
• Lee KB

• KMID: 2398044
• DOI: http://doi.org/10.4047/jap.2017.9.6.409

Abstract

PURPOSE
Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed.
MATERIALS AND METHODS
The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best.
RESULTS
In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface.
CONCLUSION
Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

Keyword

3D scanner; Linear discriminant analysis (LDA); Standardization model; Image recognition

MeSH Terms

Dentistry
Tooth

Figure

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  Fig. 1 Comparison of data projection of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

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  Fig. 2 Schematic of dentiform and base models: (A) definitive model; (B) gypsum model with tooth number; (C) gypsum model with four variable factors.

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  Fig. 3 Dentiform specimen geometry of Cases 1 – 8.

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  Fig. 4 Scanning and recognition direction: ① occlusal; ② buccal; ③ lingual.

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  Fig. 5 Scanned image results of basic model: (A) DDS scanner AEGIS.PO; (B) 3Shape Trios.

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  Fig. 6 Scanned image results of specimen: (a) DDS scanner AEGIS.PO; (b) 3Shape Trios.

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  Fig. 7 Types of LDA recognition errors: (A) duplicate images, (B) position error in one axis, (C) position error on two axes.

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