J Adv Prosthodont.  2014 Aug;6(4):259-265. 10.4047/jap.2014.6.4.259.

Measuring abutment convergence angles using stereovision dental image processing system

• Yang HS
• Park JM
• Han JS
• Lee JB
• Kim SH
• Yeo IS

• KMID: 1974844
• DOI: http://doi.org/10.4047/jap.2014.6.4.259

Abstract

PURPOSE
The purpose of this study was to develop a dental image processing system using a three-dimensional (3D) camera and stereovision technology. The reliability of the system for measuring axial wall convergence angles was evaluated.
MATERIALS AND METHODS
The new system predicted 3D coordinate points from 2D images and calculated distances and angles between points. Two examiners measured axial wall convergence angles for seven artificial abutments using a traditional tracing-based method (TBM) and the stereovision-based method (SVBM). Five wax abutment models of simplified abutment forms were made and axial wall convergence angles of wax models were measured by both methods. The data were statistically analyzed at the level of significance, 0.05.
RESULTS
Intraclass correlation coefficients showed excellent intra-examiner and inter-examiner reliabilities for both methods. Bland-Altman plots and paired t-tests showed significant differences between measurements and true values using TBM; differences were not significant with SVBM.
CONCLUSION
This study found that the SVBM reflected true angle values more accurately than a TMB and illustrated an example of 3D computer science applied to clinical dentistry.

Keyword

Three-dimensional; Tracing; abutment; Axial wall convergence angle; Stereovision dental image system

MeSH Terms

Dentistry

Figure

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  Fig. 1 (A) Image processing flow. Depth or 3D coordinates were calculated from disparity between images from right and left cameras. (B) Schematic diagram showing calculation of depth is calculated from disparities. If the image planes of two cameras are assumed to be on the same plane, and two optical axis are parallel, the focal lengths (f) of the camera lenses are the same (f = f_r = f_l). Point P in the real world can be detected as P_r and P_l in the right and left images. The disparity of two corresponding points P_r and P_l was the difference of the x-coordinate of the two points (disparity = XlP_l - XrP_r). The distance between cameras and targets was calculated as depth = (f × T)/disparity.

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  Fig. 2 Angles (α) between each vector and the XY plane were calculated. Four lines on captured two-dimensional images were converted into four vectors (black arrowheads).

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  Fig. 3 Wax abutment model. The buccal wall of the wax model was divided in half, giving two kinds of convergence angles (white arrowheads).

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  Fig. 4 Bland-Altman plots. Mesio-distal convergence angle (A) and bucco-palatal convergence angle (B). Grey lines, average difference of two measurements (stereovision-based - tracing-based). Grey dotted lines, average difference of two measurements ± 2 standard deviations. Especially, note the large difference in bucco-palatal convergence angle, which is considered to show the limitation of 2D analysis for a 3D object.

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