Computer Methods in Biomechanics and Biomedical Engineering

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Dental wear estimation using a digital intra-oral optical scanner and an automated 3D computer vision method Agnes Batista Meireles, Antonio Wilson Vieira, Livia Corpas, Bart Vandenberghe, Flavia Souza Bastos, Paul Lambrechts, Mario Montenegro Campos & Estevam Barbosa de Las Casas To cite this article: Agnes Batista Meireles, Antonio Wilson Vieira, Livia Corpas, Bart Vandenberghe, Flavia Souza Bastos, Paul Lambrechts, Mario Montenegro Campos & Estevam Barbosa de Las Casas (2015): Dental wear estimation using a digital intra-oral optical scanner and an automated 3D computer vision method, Computer Methods in Biomechanics and Biomedical Engineering, DOI: 10.1080/10255842.2015.1043627 To link to this article: http://dx.doi.org/10.1080/10255842.2015.1043627

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Date: 20 October 2015, At: 06:50

Computer Methods in Biomechanics and Biomedical Engineering, 2015 http://dx.doi.org/10.1080/10255842.2015.1043627

Dental wear estimation using a digital intra-oral optical scanner and an automated 3D computer vision method Agnes Batista Meirelesa1, Antonio Wilson Vieirab2, Livia Corpasc3, Bart Vandenberghec4, Flavia Souza Bastosd5, Paul Lambrechtsc6, Mario Montenegro Campose7 and Estevam Barbosa de Las Casasf* a

School of Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; bDepartment of Mathematics, Universidade Estadual de Montes Claros, Montes Claros, Brazil; cBIOMAT, Department of Oral Health Sciences, Katholieke Universiteit Leuven, Leuven, Belgium; dDepartment of Computational and Applied Mechanics, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil; e Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; fSchool of Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

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(Received 17 December 2014; accepted 19 April 2015) The objective of this work was to propose an automated and direct process to grade tooth wear intra-orally. Eight extracted teeth were etched with acid for different times to produce wear and scanned with an intra-oral optical scanner. Computer vision algorithms were used for alignment and comparison among models. Wear volume was estimated and visual scoring was achieved to determine reliability. Results demonstrated that it is possible to directly detect submillimeter differences in teeth surfaces with an automated method with results similar to those obtained by direct visual inspection. The investigated method proved to be reliable for comparison of measurements over time. Keywords: tooth wear; computer vision; dental models; imaging; intra-oral scanner

1.

Introduction

Wear of teeth is becoming increasingly relevant as life expectancy increases and teeth are retained longer. The major limitation of tooth wear studies is that they are frequently based on subjective information such as selfreports, questionnaires about tooth grinding (Carlsson et al. 1985; Lee et al. 2012), and scoring systems (AlOmiri et al. 2010). Most frequently used diagnosis method for clinicians are tooth wear indices such as the Smith and Knight (1984), which are unable in most cases (Al-Omiri et al. 2010) to identify wear progression. In recent years, the number of studies involving dental wear quantification increased (Haketa et al. 2004; Las Casas et al. 2008; Bastos et al. 2013) including procedures based on optical imaging as described in Haketa et al. (2004), Van’t Spijker et al. (2012) and Park et al. (2014). These studies investigated dental wear using optical imaging in methods that required physical models or manual marking. Recently, intra-oral optical devices have been developed and optimized for applications such as computer-assisted manufacturing of restorations in order to avoid precision loss when working with replicated physical models. Cuperus et al. (2012) determined the validity and reproducibility of measurements on stereolithographic (STL) and three-dimensional (3D) digital dental models of 10 dry human skulls, made with an intraoral optical scanner (LAVATM, C.O.S., 3M ESPE, Seefeld, Germany). Their results indicated that those

*Corresponding author. Email: [email protected] q 2015 Taylor & Francis

models provide valid and reproducible basis for measuring distances in dentition. Intra-oral optical imaging could be a useful tool for dental wear estimation, but no study could be found on this topic. In order to quantify tooth wear, a method for direct comparison of 3D models should be provided. Such comparison faces two important challenges in computer vision: alignment and representation. The alignment, or registering, is important to obtain a rigid transformation (rotation and translation) that best adjusts two given models to allow fair comparison despite maladjustments from different acquisition viewpoints. Several methods propose pointwise descriptors for recovering coarse alignment based on correspondence (Johnson and Hebert 1999; Tombari et al. 2010) followed by the interactive closest point (ICP) for fine adjustment (Besl and McKay 1992). Once provided the alignment, the models need a suitable representation to be compared. Such comparison may simply detect changes (Vieira et al. 2012), provide a distance measurement (Cignoni et al. 1998), or retrieve a quantified volumetric difference between models. In this work, implicit volumes and change detection strategy are employed to develop an assisted method using an intra-oral optical scanner and computer vision algorithms to provide an assessment of tooth wear that allows comparison of wear over time using digital dental models. An intra- and inter-examiner agreement is performed to determine reliability.

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2. Materials and methods 2.1 Sample preparation Eight teeth were extracted for orthodontic reasons and donated to Universidade Federal de Minas Gerais, Brazil (project approved by the university Ethics Committee, protocol number ETIC 300/03), cleaned and stored in a refrigerator. Before scanning, all tooth crowns were cut from their roots. Those crowns were fixed individually on a 1.5 mm width microscope plate. The plate was important for fixation of the samples and also served as a standard reference to recover the transformation from image to the world coordinate system values of the point cloud representing tooth. The teeth were divided into two groups. Each group had four teeth fixed on their plates spaced by 3 cm. The ‘tooth-plate’ set was then inserted into a layer of a condensation silicone (Optosil, Heraeus Kulzer, Hanau, Germany) to prevent mobility and to standardize the position of the image acquisition. This platform, made with the impression material, was used in all scanning phases. A thin titanium dioxide powder dusting was applied to prevent high surface reflectivity. The coating process was calibrated in order to optimize homogeneity and obtain the thinnest layer as described in Dehurtevent et al. (2015).

2.2 Scanning and wear Previously to the scanning phase, the scanner was calibrated with a specific device similar to a chess board. This calibration phase was repeated for all subsequent scannings. Once the dusting powder was applied, the first group of teeth was scanned with the intra-oral optical scanner (LAVATM, C.O.S., 3M ESPE, Seefeld, Germany). The scanner was chosen for its reported accuracy (Palaniappan et al. 2011; Vandenberghe et al. 2012) and precision (6 –11 mm) (Balakrishnama et al. 2009). All teeth were scanned using the same sequence and the scans were individually checked for completeness before their acceptance. All teeth were etched individually with 37% phosphoric acid gel and rubber dam isolation (Figure 1). The 1 min etching was followed by thorough rinsing with

(a)

(b)

Etching

water and gentle air drying. Samples were powdered and then were scanned again. The procedure was replicated for a total etching time of 5 and 10 min, respectively. Images from the peripheral margin were acquired using confocal microscopy (PLu 2300 Optical Profiler, Sensofar-Tech, SL, Barcelona, Spain) to verify wear.

2.3 Image processing The files were sent to the scanner manufacturer for STL acquisition with no correction step to prevent smoothing. Smoothing could affect final results masking the created wear. The computer processing was performed on a PC with a Core 2 Duo CPU running at 2.0 GHz and with 2 GB RAM. Algorithms were implemented in C/Cþ þ using Open GL. Acquisition: For each tooth M i (i from 1 to 8), the scanning process provided a reference STL model, M 0i ; acquired before acid exposure, a model M 1i after 1 min, a model M 5i after 5 min and a model M 10 i acquired after 10 min of exposure. Each STL mesh provided a 3D point cloud with normals tessellated in a triangle mesh. The point coordinates were acquired from slightly different view points, turning direct comparison unfeasible. Alignment: The second step involved the alignment between two given models. In order to recover alignment, the ICP algorithm was applied to find a rigid transformation to describe the models M 1i ; M 5i and M 10 i on the same reference frame from M 0i . The ICP algorithm iteratively selects pairs of nearest neighbors   for a given P ¼ ðp; qÞ [ M 0i £ M 1i ; kp 2 qk # d threshold d . 0 and uses the well-known least square method (Bretscher 2004) to find a rigid transformation R that minimizes the cost function X 2 kp 2 RðqÞk : ð1Þ ðp;qÞ[P

M 1i

Model is then updated using the matrix R and this process is repeated until R converges to an identity matrix, as the two models are well aligned. Algorithm 1 details this process.

(c)

Rinsing

(d)

Drying

Scanning

Figure 1. (a) Tooth etched partially with phosphoric acid preceded by rubber dam isolation followed by (b) thorough rinsing with water and (c) air drying. In (d) arrows indicate the detected etched area.

Computer Methods in Biomechanics and Biomedical Engineering   Algorithm 1 ICP-Alignment M 0i ; M 1i

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1: I ˆ Identity Matrix 2: repeat 3: P ˆ ðp; qÞ [ M 0i £ M 1i ; kp 2 qk # d 4: Find R solving: 5: P 1 R ˆ arg min kp 2 R* ðqÞk2  jPj * 6: M 1i ˆRR M 1i ðp;qÞ[P 7: until MaxIter Reached or kR 2 Ik # u

Since this convergence leads to a local optimum, as shown in Besl and McKay (1992), global optimum convergence depends on an initial coarse alignment which is already provided by the data. The same way, M 5i and 0 M 10 i are aligned to M i . In order to avoid influence of wear surface in model alignment, only points in the region without wear were used in our ICP algorithm. Figure 2 shows an alignment example achieved for teeth models. Comparison: The triangular meshes provided by the STL model for each tooth were used to compute the associated volume and retrieve a quantified volumetric difference from models M 1i ; M 5i and M 10 i with respect to the reference model M 0i . Considering a tessellated cutting plane at the same height for all teeth, each model of tooth i [ f1; . . . ; 8} in time j [ f0; 1; 5; 10} becomes a closed surface Sij enclosing a solid object Gij from which a volume VðGij Þ can be estimated by ððð VðGij Þ ¼ dV: ð2Þ Gij

Using the divergence theorem (Stewart 2012), we obtain a practical formulation for this volume in terms of vertices of the triangle mesh surface. The divergence

theorem states that ððð

~ divðFÞdV ¼

Gij Sij

3 ðð

~ FdS:

ð3Þ

Sij

Considering a piecewise linear surface parametrized by its triangle elements euvw with vertices u; v; w, each triangle area is given by kdSk, where ~ we dS ¼ ðv 2 uÞ £ ðw 2 vÞ=2. Concerning the flow F, ~ y; zÞ ¼ ðx=3; y=3; z=3Þ, such that we have choose Fðx; ~ ¼ 1. Using barycentric coordinates, each point divðFÞ ðx; y; zÞ along triangle euvw is written as ðx; y; zÞ ¼ au þ bv þ gw where a þ b þ g ¼ 1 and, hence, ~ ¼ ð1=3Þðau þ bv þ gwÞ. Then, in terms of finite F elements of the surface, Equations (2) and (3) lead to ððð ðð ~ VðGij Þ ¼ 1dV ¼ FdS Gij

¼

X1 Sij

3

Sij

ðau þ bv þ gwÞ

ðv 2 uÞ £ ðw 2 vÞ ; 2 ð4Þ

where ðÞ and ð£Þ are dot and cross product, respectively, whose properties lead to VðGij Þ ¼

1X auðv £ wÞ þ bvðw £ uÞ þ gwðu £ vÞ: 6 S ij

ð5Þ Since uðv £ wÞ ¼ vðw £ uÞ ¼ wðu £ vÞ, and that the signal depends on the surface orientation, we can write the volume as the absolute value     X  1  VðGij Þ ¼  ða þ b þ gÞ uðv £ wÞ 6   Sij      1 X ¼  uðv £ wÞ; 6 S 

ð6Þ

ij

Figure 2. Example for the alignment between models: in blue, a model without wear, and in red, a model after 10 min of acidic exposure. Black arrows indicate good model adjustment with the exception for the area indicated by the gray arrow. Difference between areas corresponds to wear zone.

where uðv £ wÞ is the vector triple product from the triangle elements of the closed mesh surface Sij . Wear W ij is then estimated as the difference between the volume of reference model M 0i and volume of model M ji acquired after an exposure to acid for j minutes as W ij ¼ VðGi0 Þ  VðGij Þ: Finally, to present error estimation for the computed volume, the regions without wear (not etched surfaces) were considered, since in these regions there was no exposure to acid and hence, no volume difference is expected. A volume variation even for not etched surfaces was observed. This variation calculated as its standard deviation (SD), considering a normal distributed error, is presented along with the results.

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2.4 Scoring The worn area was a cone-like area located at the tip of the disto-buccal cusp of all tested teeth (Figure 1). The examiners were two dentists with a doctoral degree and more than 15 years of clinical practice. They randomly evaluated 32 images for the four exposure times of the eight teeth. For calibration, images were presented individually on a computer screen (Figure 3). The established criteria for the visual scoring system based on 3D models were as follows: 0 – no visual change in enamel; 1 – surface change visible as a line around the distobuccal cusp corresponding to 1 min acid exposure; 2 – surface change visible as thicker line and change in surface smoothness around the same cusp corresponding to 5 min acid exposure; 3 – surface change visible as thickest line, more remarkable change in surface smoothness and perspective around the cusp corresponding to 10 min acid exposure. After calibration, a random test with the 32 images was carried out separately for the same examiners to calculate the inter- and intra-examiner agreement (1 week interval).

2.5

Statistical analysis

The estimated scanning values for volume changes (in mm3) were compared using descriptive statistics. Nonparametric tests (Kruskal – Wallis and Mann – Whitney U test) were performed for the estimation of wear data and differences between groups. p-values were calculated for each group and compared. Intra- and inter-examiner

Figure 3.

agreement was accessed with kappa and Kendall indexes. Statistical analysis was performed using Minitab (v16 Minitab, Inc., State College, Pennsylvania, USA).

3.

Results

Figure 4 shows the wear estimation and its SD with results for each tooth (volume loss in mm3). Visually, it was possible to detect the tooth surface loss and wear-free areas using models such as those presented in Figure 3 and directly from the scanner screen as Figure 5(a) shows. The wear started very subtly and progressively increased with increase in acid exposure. The smoothest induced wear features (at 1 min etch) are represented in tooth model 1 in Figure 3 and in the scanner screen registered in Figure 5 (a). Figure 5(b) presents a confocal laser scanning image showing the border between wear and wear-free areas in a tooth after 10 min acid exposure. The volume loss between models was estimated after registration with the ICP algorithm. The difference was given by pixels and transformed into metric scale. This conversion was given by an object of known dimension, in this study, the transformation was supported by the 1.5 mm thickness microscope plate that fixed all teeth in the scanning process. The plate was measured with a calibrated caliper gauge. Boxplots in Figure 6 reveal a volume loss increase with acid exposure time. The data indicate that the variability for 1 min acid exposure changes slightly among the eight teeth. The Kruskal – Wallis test was used to verify the volume loss median for the three exposure times: 0.88 (1 min), 4.12 (5 min), and 9.19 mm3 (10 min). p-values of 0.000 show a significant difference for the exposure times. The Kruskal–

Scoring system for the visual analysis. The tooth wear area is consistent with etching exposure time.

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Computer Methods in Biomechanics and Biomedical Engineering

Figure 4.

5

Volume wear estimate through time of exposure.

(a)

(b)

One minute etched models

peripheral margin ditching

Figure 5. (a) Scanning after 1 min etch. Arrows indicate worn area directly visible from the intra-oral optical scanner screen through the generated models. (b) Confocal laser scanning image showing the border area of a tooth after 10 min exposure and indicating the loss of dental structure.

Figure 6.

Boxplot of wear: volume loss (mm3) versus exposure time. * indicates an outlier.

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Table 1.

Intra- and inter-examiner agreement.

Within appraisers Appraiser A Appraiser B Each appraiser vs standard Appraiser A Appraiser B Between appraisers All appraisers vs standard

Kappa

Kendall

1.00 0.83

1.00 0.98

1.00 0.92 0.92 0.96

1.00 0.96 0.98 0.98

a

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For a 95% confidence interval (CI).

Wallis test does not identify where the differences occur or how many differences actually occur. For this purpose, the Mann – Whitney Test was used to compare separately 1 and 5 min, 1 and 10 min, and 5 and 10 min. The p-value estimated were 0.0039, 0.0009, and 0.052, respectively. There is a significant difference of the median between 1 and 5 as well as between 1 and 10 min. An agreement analysis was performed and the kappa and Kendall values for the examiners’ visual evaluation are presented in Table 1. The values were considered significant ( p value ranged from 0.0000 to 0.0001) and acceptable for a 95% confidence interval. Kappa values for within examiners, for each examiner versus standard values, and between examiners ranged from 0.83 to 1, indicating a good agreement in the assessment of tooth wear progression using 3D geometrical models.

4.

Discussion

Although dental wear studies present high quality, standardized and calibrated methods, many of the used procedures are not suitable for direct clinical application or are not easy to handle and require indirect measurements with dental casts as well as subjective evaluation of wear by the examiner (Haketa et al. 2004). In recent years, relevant improvements have been made in digital dentistry (Solaberrieta et al. 2014) and could be useful for dental wear estimation by providing high quality images. Vant Spijker et al. (2012) employed an ordinary flatbed scanner and defined the intra- and inter-observer agreement, concluding that a 2D manual method of marking and tracing of occlusal visible wear facets in gypsum casts could assess occlusal tooth wear quantitatively. Haketa et al. (2004) also developed a 2D assessment using photographs of gypsum casts manually marked with pencil and ink. Most of these studies investigated dental wear using optical imaging on replicated physical models (Park et al. 2014). The proposed automated method is based in spatial models but differs from other studies (Haketa et al. 2004; Al-Omiri et al. 2010; Cuperus et al. 2012; Van’t Spijker et al. 2012) because it does not require

manual marking, avoiding subjectivity. Another important characteristic of the used method is the possibility to obtain a submillimeter range of tooth wear in a direct measurement method, avoiding impression procedures as described in Park et al. (2014). Those are the major contributions of the proposed method. Spatial density or implicit volumes are high-level representations suitable for objects described by point clouds in applications that require comparison between different objects. While traditional approaches present complex metrics, including statistical information associated with the underlying point distribution, Vieira et al. (2012) presented a method, based on density calculation and 3D Gaussian smoothing, that transforms raw data into a continuous 3D density field, upon which Boolean operations are used to express the concept of change detection between the two clouds. By using simple Boolean operations on the smoothed density space, this method achieves quite a complex operation in a simple way, leading to both robustness and computational speed. Furthermore, implicit volume representation is robust to noise and outliers typical of real data by using a kernel strategy to smooth the spatial occupancy. While smoothing reduces noise, the density level clustering is able to remove outliers. Results show that enamel volume loss increases with time, as previously observed by other authors (Silverstone et al. 1975; Hermsen and Vrijhoef 1993; Palaniappan et al. 2011). Comparing the mean values of the pairs M 0i and M 1i , 1 5 1 10 5 M 0i and M 5i , M 0i and M 10 i , M i and M i , M i and M i , and M i 10 and M i , the results show a significant difference for M 0i 1 5 1 and M 1i , M 0i and M 5i , M 0i and M 10 i , M i and M i , and M i and 10 M i for volume loss at 95% CI. Despite the fact that there was an increase in enamel loss for all etched teeth, for the pair M 5i and M 10 i mean enamel loss values were statistically similar, indicating a small increase. Some authors reported a relationship between time and loss of enamel for etching with 37% phosphoric acid (Hermsen and Vrijhoef 1993) with enamel loss also increasing at a slower rate after 1 min. They attributed their results to the smear layer-like film created by the etching, thus covering the underlying enamel. The underlying enamel is also cited by Silverstone et al. (1975), which stated that after 1 min etching there is mostly removal of an aprismatic enamel layer. The inter- and intra-examiner agreement analysis in visual scoring confirms the numerical findings and supports the proposed method by revealing the reliability of the method, including for a 1 min subtle wear. The possibility of having an automated quantification method available for the professional increases the reliability of diagnosis and treatment planning of patients. As studied by Al-Omiri et al. (2010), with the drop of scanner’s prices and the increasing number of scanning services, a growing

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Computer Methods in Biomechanics and Biomedical Engineering number of clinical studies will possibly use this technology in the future. Subjective methods such as wear index scoring cover a macroscopic scale and require well-calibrated professionals. Although the estimated error of the system is of the same order as the initial wear after 1 min, visual inspection and statistical evidence show that the scanner is capable of detecting volume loss from etching wear in all exposure times tested. Future studies should apply the same methodology for in vivo trials to achieve the assessment directly in patients on regular basis with data analysis provided automatically, increasing the range of applications and broadening the potential user public. For clinical purpose, the microscope plate can be replaced by fiducial points on oral structures. Restorative treatments that remain intact with time, orthodontic brackets or anatomical features such as cementoenamel junction can be useful for this purpose or it could be solved by providing acid-resistant markers for the studied teeth in a method similar to that described in Schlueter et al. (2005).

5.

Conclusions

A method to assess dental wear, based on data obtained from a digital intra-oral optical scanner and an automated 3D computer vision method, was applied and tested. Values of enamel wear volume were estimated for different time periods of acidic exposition. Results demonstrated that it is possible to directly detect differences in teeth surfaces at the submillimeter level without impressions and casts marking, which reduces subjectivity and enables using an in vitro approach to access the problem in a relatively short time span. It could be implemented also in clinics with proper scanners that allow to capture the geometry of teeth directly from the patient. Visual scoring supports the results and shows good agreement in the assessment of tooth wear including the submillimeter situation. Once a methodology for allowing the comparison of measures over time is established and tested, the intra-oral optical scanner is a potential resource to allow reliable comparison of measurements over time.

Conflict of interest disclosure statement No potential conflict of interest was reported by the authors.

Funding The authors acknowledge the support from the Brazilian research funding agencies FAPEMIG, CAPES [grant number 0697-11-7] and CNPq [grant number EDT-506/07] and 3M for the use of equipment.

7

Notes 1. 2. 3. 4. 5. 6. 7.

Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]

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Dental wear estimation using a digital intra-oral optical scanner and an automated 3D computer vision method.

The objective of this work was to propose an automated and direct process to grade tooth wear intra-orally. Eight extracted teeth were etched with aci...
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