Politehnica University of Bucharest

Automatic recognition of teeth

ProfessorDr.Ing Nicu Goga Criv Ana-Maria, Software Engineering Master

ContentsAbstract31.Introduction5The objectives that will be followed62. Methods82.1. Step 1: Pre-processing and segmentation82.2. 1. Location of the dental work223. Results254.References27

Abstract

The finite element analysis represents an effective method to study the strength and predict the fracture risk of endodontically-treated teeth and also to predict and to diagnose different teeth pathologies. This paper presents a rapid method developed to generate a comprehensive tooth model using data retrieved from micro-computed tomography (CT). With this method, the inhomogeneity of material properties of teeth was included into the model without dividing the tooth model into different regions. The material properties of the teeth were assumed to be related to the mineral density. The micro-CT images of teeth were processed by a Matlab software programme and the CT numbers were retrieved. The teeth contours were obtained with thresholding segmentation. It was observed that the two degree grayscale differential method can significantly simplify the pattern recognition process for teeth. Compared to the popular recognition method like PCA and HDM, this method is a lot more simple, runs faster and the identification rate is better. The new approach will reduce the traditional reliance on CT image to get information and make dental decision. The new approach is tested and will be used in the dental decision making software to do preliminary dental advising for potential patients. An image of teeth are used as an example here; all types of teeth (molars, canines), individual or in pairs can be processed the same way.The common process consists of two major components. The subjective component mainly focuses on the patient's current health and relevant clinical history prior to the current condition, recording all significant information required for further analysis. The objective component refers to physical examination like CT scans or radiological scans. The assessment represents the differential diagnosis for the purpose of the medical visit, while the plan consists of the treatment that the patient is prescribed. Although the subjective and objective components take a significant amount of time, the most time-consuming steps are the assessment and plan, requiring thorough interpretation of the CT scans performed, and often being based on computerized 3D reconstruction which might be problematic to analyse on the computer screen under certain circumstances.This research is one which combines two different types of science: dentistry and computer science. University research, products and patents show that current solutions target only dental implants or orthodontic treatment forgetting some other important aspects. The distinctive features that will advance the knowledge in this domain are the followings: a) Integrated dental treatment planning for different types of dental problems, some which are not covered currently (carries, dental crown, etc.) and b) use of other medical information relevant to the treatments, not only CT (for example patient with diabetes cant undergo certain dental procedures. This project proposes new techniques for software dentistry, software treatment planning and dental problems identification.Marketing studies show a growth in dental market and software. According to IBIS World Industry Report the dental market performed well during the economic recession having an annual grow between 2008 and 2013 of 1.5%. Similar tendency is remarked for dental software. This favorable context assures also a good market for the product developed within this project proposal.The project aims at the improvement of the medical dentistry practice beneficial for both dentists and patients. From the dentists point of view , the time developing the product and dental planning will be reduced, and more time will be spent on management and precise dental treatment. From the patients point of view, the patient will benefit from accurate and faster healthcare treatment. University of Medicine and Pharmacy Carol Davila Bucharest (UMF): expertise in dental research and treatment OSF Global Services (OSF): expertise in software development Politehnica University of Bucharest (UPB): good expertise in medical informatics development and research

1.IntroductionProject topic This project proposal addresses the research topic Informatics systems for health and environment (e-health).The purpose of the project is to build an intelligent e-health system to help dentists and their patients to benefit from an improved dental health care system. The project has an interdisciplinary nature because it merges computer science and medical dentistry, being based on digital image processing.

Problem Nowadays, developing an initial assessment and devising a treatment plan in dentistry takes between 4 to 12 hours for each individual patient. The assessment involves a thorough radiological investigation along with its interpretation, followed by visual examination and, prior to devising a treatment plan, a lot of paperwork is involved. Our project aims to address this practical problem through an assistant system for treatment planning.Before presenting the algorithms and expected results end product, it is necessary to present some background theory in the dental field.

The present research project wants to implement an integrated decision support platform in the dental diagnosis processes by building upon state of the art digital image processing, image mining technologies and. Its aim is to help develop the current computer-aided detection techniques in dentistry with the goal of building and exploiting models that embed knowledge about the discriminative elements between normal medical imaging scans (CT) and scans of dental pathologies. So we can state that the project aims to analyse the existent visual elements present in the CT scans and to identify the certain types of pathologies that are usually associated with. These computerized models are discovered by going through large amounts of annotated medical imaging scans, and analysing the visual elements in images. Using the new patterns, we will be able to process the patient's medical imaging scans and suggest the most probable diagnoses and corresponding treatment plans greatly reducing the period of time and effort by up to 6 hours per patient.

The objectives that will be followed

1. Automating the process of creating a personalized treatment plan fully integrated in the SOAP protocol;1. In-depth analysis, selection and further development of image mining methods for discovery of discriminative models;1. Analysis, selection and further development of knowledge inference methods for processing new images and predicting, based on the mined models, the probability that the patients scans indicate the presence of an oral pathology;1. Development of semantic annotation techniques for associating patients scans with valuable semantic information which can be employed in the knowledge inference process along with visual content information;1. 3D model collection useful as teaching material for a 3D atlas of case studies and with direct application in prosthodontics, implantology and orthodontics;1. Generating a treatment plan that will include therapeutic indications in a logical order so that the medical practitioner will be able to save time and to take proper care of the patients needs while respecting medical decisions according to personal expertise of the doctor.1. Deploying an experimental pilot in a real clinical environment aimed at studying the effectiveness and applicability of the framework in order to create the opportunity of simulating the project in interaction with real users because they will be able to provide essential feedback for advancing the solution towards a user-friendly and highly accurate clinical decision support platform.

Dental biometrics is used in forensic dentistry to identify or verify persons based on their dental radiographs. This paper presents a method for identifying and numbering a humans teeth based on dental work information. The proposed method works with three main processing steps: segmentation (feature extraction), creation of a dental code, and matching. In the segmentation step, seed points of the dental works are detected by thresholding. The final segmentation is obtained with a snake (active contour) algorithm.

The dental code is defined from the position (upper or lower), the size of the dental works, and distance between neighboring dental works. The matching stage is performed with the Edit distance (Levenshtein distance). The costs for the insertion, deletion and substitution operations were adapted to make the matching algorithm more sensitive. The method was tested on a database including 50-60 dental radiographs and the results are pretty encouraging.Biometrics is the science and technology of identification, i.e. establishing the identity of an individual, by measuring the subjects physical or behavioral traits. The term is derived from the Greek words "bios" for life and "metron" for to measure . The method of dental biometrics is used in forensic medicine (forensic dentistry) to identify persons by matching post-mortem radiographs (acquired after a person is deceased), with ante-mortem radiographs (acquired before a person is deceased) in a database, but can also be used to match two ante-mortem or two post-mortem radio-graphs .In some cases (plane crashes, fire accidents, etc.) biometric features such as faces or fingerprints are destroyed and it is not possible to work with conventional identification methods like fingerprint or face recognition. In such cases, dental biometric is an appropriated method, because bones and teeth with their dental works (DWs), e.g. inlays, are very resistant to modest force effects and high temperatures (amalgam fillings up to 1000C, endodontic treatments up to 1100C ) and also has good biometric properties. Dental records have been used to identify the victims of disasters, such as the 9/11 bombing and the Asian tsunami or Fukushima nuclear incident from 2011.

The objective of this work is to develop and implement a dental biometric method for human identification based on dental work information. The algorithm performs dental work matching onto registered panoramic dental radiographs and is implemented in MatlabTM.

2. Methods

The proposed method for human identification consists of three main processing steps:0. Pre-processing of the dental scans and segmentation. 0. Creation of a dental code out of the information of the detected scans including position (jaw bone and mandible), size and distance between neighboring teeth.

0. Matching of a particular dental code with other existing dental codes in a database.

2.1. Step 1: Pre-processing and segmentation In order to obtain a clearer image of the patients scan we applied a texturizing algorithm that will later improve the percentage of teeth numbering and disease recognition. After trying a lot of algorithms, it was decided to apply a texturizing one. This kind of algorithm characterizes a certain region in an image because it is able to differentiate the texture context. The texturizing algorithm simply quantifies intuitively the qualities given by the pictures smooth, silky, or bumpy texture represented by the spatial variation in the pixels intensity.This kind of algorithm is usually used in a great variation of applications, including medical ones, especially image processing. Usually texture analysis can help separate texture boundaries known as texture segmentation. The algorithm is used to replace threshold algorithms in images that the later can not be applied.The algorithm includes some texture analysis functions which are applied to the image in order to filter it using static measurements. This type of measurements will define the texture of an image providing data about the local variability of the intensity value of pixels in a fragment of an image. For example, in areas with smooth texture, the range of values in the neighborhood around a pixel will be a small value; in areas of rough texture, the range will be larger. Similarly, calculating the standard deviation of pixels in a neighborhood can indicate the degree of variability of pixel values in that region. For the algorithm to be applied to the image a certain number of steps must be respected. First the image must be read, specifying it type:E=imread('32a00a333042.bmp');figure, imshow(I)figure,imshow(I);

Next step is represented by trying to establish the images gradient magnitude. For this to happen it is necessary to apply a filter. After using different filters like Canny it was established that the most appropriate filter to use was Sobel. Based on this one-dimensional analysis, the theory can be carried over to two-dimensions as long as there is an accurate approximation to calculate the derivative of a two-dimensional image. The Sobel operator performs a 2-D spatial gradient measurement on an image. Typically it is used to find the approximate absolute gradient magnitude at each point in an input grayscale image. The Sobel edge detector uses a pair of 3x3 convolution masks, one estimating the gradient in the x-direction (columns) and the other estimating the gradient in the y-direction (rows). A convolution mask is usually much smaller than the actual image. As a result, the mask is slid over the image, manipulating a square of pixels at a time. The actual Sobel masks are shown below:

The magnitude of the gradient is then calculated using the formula:

An approximate magnitude can be calculated using:|G| = |Gx| + |Gy|The code for the Sobel edge detector is shown below and uses the above gradient approximation.

hy = fspecial('sobel');hx = hy';Iy = imfilter(double(I), hy, 'replicate');Ix = imfilter(double(I), hx, 'replicate');gradmag = sqrt(Ix.^2 + Iy.^2); figureimshow(gradmag,[]), title('Gradient magnitude (gradmag)')On the next step the images texture is created using the entropyfil function. This functionreturns an array where each output pixel contains the entropy value of the 9-by-9 neighborhood around the corresponding pixel in the input imageI. Entropy is a statistical measure of randomness. Afterwards themat2gray function is applied with the purpose of rescaling the texture imageso that its values are in the default range for a double image.

hy = fspecial('sobel');hx = hy'; Iy = imfilter(double(I), hy, 'replicate'); Ix = imfilter(double(I), hx, 'replicate'); gradmag = sqrt(Ix.^2 + Iy.^2); figure imshow(gradmag,[]), title('Gradient magnitude (gradmag)')

Next we will try to create rough mask for one of the structures. This is done by thresholding the rescaled imageto segment the textures. A threshold value of 0.8 is selected because it is roughly the intensity value of pixels along the boundary between the textures. We then obtain a binary image with segmented objects that we can clearly see that they have the color white. If you compareBW1toI, you notice the top texture is overly segmented and the bottom texture is segmented almost in its entirety. You can extract the bottom texture usingthe function bwareaopen.

E=entropyfilt(gradmag); Eim=mat2gray(E); imshow(Eim);BW1= im2bw(Eim, .8);imshow(BW1); figure,imshow(I); BWao=bwareaopen(BW1,2000); imshow(BWao);

The last step is followed by mask creating. First we use the functionimcloseto smooth the edges and to close any open holes in the object inBWao. A 9-by-9 neighborhood is selected because this neighborhood was also used bythe entropyfilt function. Then we apply imfillto fill holes in the object incloseBWao.

nhood=true(9); closeBWa0=imclose(BWao,nhood); imshow(closeBWa0); roughMask=imfill(closeBWa0,'holes');Then we will apply the rough mask function to segment the texture. What this function does is comparing the binary imageroughMaskto the original imageI.

imshow(roughMask); figure,imshow(I); I2 = I; I2(roughMask) = 0; imshow(I2);

Then we use entropyfilt to calculate yje texture image and we will threshold it using graytresh function.

E2=entropyfilt(I2);E2=mat2gray(E2);E2im=mat2gray(E2);imshow(E2im);BW2=im2bw(E2im,graythresh(E2im));imshow(BW2);figure, imshow(I);mask2=bwareaopen(BW2,1000); imshow(mask2);

Afterwards the segmentations results will be displayed by using mask 2 to extraxt the two structures.texture1=gradmag; texture1(~mask2)=0; texture2=I; texture2(mask2)=0; imshow(texture1); figure,imshow(texture2); boundary=bwperim(mask2); segmentResults=I; segmentResults(boundary)=255; imshow(segmentResults); S=stdfilt(I,nhood); imshow(mat2gray(S)); R=rangefilt(I,ones(5)); imshow(R);

In the future a mask will be created from scan number 43 and applied to all the other scans in order to obtain a clearer image. It is desirable to obtain clear scans that will make teeth recognition possible and also disease detection easier.

The dental scan is converted into a gray-scale image and median filtering is per-formed to reduce noise in the image. Because of different lightning conditions in the dental scans, the image is subdivided into two regions of interest (ROIs): left (ROI 1) and right (ROI 2).

The algorithm determines a gray value threshold in the left and the right region of interest of the dental scan. Typically the images has a certain feature that makes the highest intensities in the image and appear as a distinct, relatively small but pronounced, mode in the upper range of the gray-scale histogram. After smoothing the histogram with a moving average filter, the threshold is set to the gray-value at the location of the left valley at the rightmost mode, which indicates the dental work .

The threshold is used to binarize the gray-value image (see next Figure.). The results of the conversion are used as initial contours for the segmentation stage. Each region represents a possible dental problem.

A snake (active contour) algorithm is used to perform the final segmentation of the dental work. Snakes can be used to segment objects with fuzzy border contours where traditional edge-detection will fail. Snakes are curves that can move under the influence of internal forces (elasticity and bending forces) coming from within the curve itself and external forces (potential forces) computedfrom the image data. The internal and external forces are defined so that the final snake will con-form to an object boundary . The external force field is computed from the gradient image, as shown in . A snake needs to be initialized with an initial curve (e.g. circle) and is an itera-tive procedure which stops after a defined number of iterations. The better the initialization curve, the better the performance of the algorithm and the final segmentation results.

Figure showing threshold used to do Snake Segmentation by active contours

Figure showing the difference between normal teeth and dental workSnake algorithmI =double(imread('32a00a333043.bmp'));x=[163 166 207 248 210];y=[182 233 251 205 169];P=[x(:) y(:)];Options=struct;Options.Verbose=true;Options.Wedge=2;Options.Wline=0; Options.Wterm=0; Options.Sigma1=8; Options.Sigma2=8;Eext = ExternalForceImage2D(I,Options.Wline, Options.Wedge, Options.Wterm,Options.Sigma1);Fx=ImageDerivatives2D(Eext,Options.Sigma2,'x');Fy=ImageDerivatives2D(Eext,Options.Sigma2,'y');Fext(:,:,1)=-Fx*2*Options.Sigma2^2;Fext(:,:,2)=-Fy*2*Options.Sigma2^2;if(Options.Verbose) h=figure; set(h,'render','opengl') subplot(2,2,1), imshow(I,[]); hold on; plot(P(:,2),P(:,1),'b.'); hold on; title('The image with initial contour') subplot(2,2,2), imshow(Eext,[]); title('The external energy'); subplot(2,2,3), [x,y]=ndgrid(1:10:size(Fext,1),1:10:size(Fext,2)); imshow(I), hold on; quiver(y,x,Fext(1:10:end,1:10:end,2),Fext(1:10:end,1:10:end,1)); title('The external force field ') subplot(2,2,4), imshow(I), hold on; plot(P(:,2),P(:,1),'b.'); title('Snake movement ') drawnowendS=SnakeInternalForceMatrix2D(Options.nPoints,Options.Alpha,Options.Beta,Options.Gamma);h=[];for i=1:Options.Iterations P=SnakeMoveIteration2D(S,P,Fext,Options.Gamma,Options.Kappa,Options.Delta); % Show current contour if(Options.Verbose) if(ishandle(h)), delete(h), end h=plot(P(:,2),P(:,1),'r.'); c=i/Options.Iterations; plot([P(:,2);P(1,2)],[P(:,1);P(1,1)],'-','Color',[c 1-c 0]); drawnow endend if(nargout>1) J=DrawSegmentedArea2D(P,size(I));end

Results

Figure showing Initial curve of a snake computed out of the binary imag23

2. 2. Step 2: Creation of the dental code

Based on the dental work contour, a dental code is created. The dental incorporates information about the position (upper jaw and mandible), the size of the teeth and the distance between two neighboring teeth.

2.2. 1. Location of the dental work

An algorithm was implemented to sort all dental work from left to right based on the center of mass point of each individual dental work (see next Figure).

Figure showing dental work mask with sorted dental works from left to right.

For the dental code it is also important to know whether the tooth belongs to the maxilla (upper jaw) or to the mandible (lower jaw). Therefore, a border between the maxillary and the mandibular teeth is detected. A stripe in the intensity image is cut with the width of the current region. Next, the intensity sum of all horizontal rows in the stripe is calculated. The highest intensity represents the area of the dental work. The algorithm detects the first valley on the left and on the right site of the highest intensity point. The valley with the lower intensity represents the border between the mandibular and maxillary teeth.

Figure showing areas

2.2. 2. Size of the dental restoration

The proposed method uses registrated dental records. Useful image information, including mandibular and maxillary teeth, is cut and resized to a size of 1000x300 pixels. According to this, the amount of pixels in a dental record is always the same, which means that the size of a dental work (amount of pixels) is a percentage of the total amount of pixels in the dental record.

2.2. 3. Distance between two dental works

To make the matching algorithm more sensitive, also the distance (amount of pixels) between two neighboring DWs is included into the DC. The distance is defined by the amount of pixels between the center of mass points of the two DWs. The distance of the leftmost DW (d1) is set to zero to make the algorithm more stable against small deviations in the manually registration of the DRs (see Figure 10). The value for the distance is given in percentage of the total width of the DR, which is always 1000 pixels. To have a better overview in the DC, the distance is multiplied by 102.

2.2. 4. Step 3: Matching

After the DC is created, it can be compared to other DCs in a database. These can be different codes of the same person or codes of different persons. Matching between radiographs of the same subject is called genuine matching and matching between radiographs belonging to different subjects is called impostor matching.

An algorithm was implemented which works with the Edit distance (The Edit distance is often used to compare gene sequences or strings. The Edit distance between two strings is given by the minimum number of operations needed to transform one string into the other, where an operation is an insertion, deletion, or substitution . Every operation is associated with certain costs. Because of the structure of the DC, it was necessary to restructure the algorithm of the Edit distance. Not only the letters U and L have to be compared, but also the size of the dental work and the distance between neighboring teeth. The costs of the operations insertion, deletion, or substitution were adapted to improve the results of the matching algorithm.

If the compared sizes differs more than 100%, the cost is set to 25. In the other case, the cost for comparing the size is set according to the percentage difference of the two compared dental works.If the compared distances differs more than 15%, the cost is set to 25. In the other case, the cost for comparing the distances is set according to the percentage difference of the two compared dental works.

3. ResultsOperationCost

insertion60

deletion60

Comparing the size:

0, difference between 0..10%

1, difference between 10..20%

10, diff. between 90..100%

substitution25, difference > 100%

Comparing the distance:

0, difference between 0..1%

1, difference between 1..2%

15, difference between 14..15%

25, difference > 15%

A database including approximately 60 dental records was used in the experiments to evaluate the proposed dental biometric method.The images were manually registrated to obtain comparable conditions. In cases of over- or under segmentation of the dental records, the segmentation result had to be corrected manually. Also, if they are not detected by thresholding, an area of interest has to be selected manually in the dental record to perform local thresholding.

Table : Costs for insertion, deletion and substitution in the Edit distance.

Because the amount of images in the database is low, it is not possible to make a clear statement about the performance and effectiveness of the proposed method. In future work, the proposed method will be assessed on a larger database. Also, in future work, the proposed method will be fused to other methods based on different tooth features, like crown and root shapes, and the seg-mentation method will be replaced by a method based on image-foresting transform, more effective for segmentation of bad quality images, with minimum human intervention.

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