CN111311653A - Method for registering dental plaque fluorescence image and tooth three-dimensional model - Google Patents
Method for registering dental plaque fluorescence image and tooth three-dimensional model Download PDFInfo
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Abstract
The invention discloses a method for registering a dental plaque fluorescence image and a tooth three-dimensional model, which comprises the following steps: s11, obtaining three-dimensional model point cloud data P of tooth surfacesAnd fluorescence image F of occlusal surface of toothImg(ii) a S12, carrying out fluorescence image F on occlusal surface of toothImgProcessing to obtain tooth area and contour information Edge1(ii) a S13, carrying out point cloud data P on the three-dimensional model of the tooth surfacesCarrying out posture correction to obtain a corrected tooth three-dimensional model Ps(ii) a S14, establishing a corrected tooth three-dimensional model PsFluorescence image F of occlusal surface of toothImgThe corrected three-dimensional tooth model PsPoint on to the fluorescence image FImgThe above. The method realizes registration of the occlusal surface contour and the 3D projection contour of the 2D fluorescence image by iteratively solving the mapping model to obtain the dental plaque 2D fluorescence image and the dental occlusion tableAnd determining the three-dimensional space distribution of dental plaque on the tooth surface by the corresponding relation between the surface 3D data, and providing a basis for researching the three-dimensional distribution of dental plaque on the tooth surface.
Description
Technical Field
The invention relates to the technical field of computer-aided intelligent caries prevention, in particular to a method for registering a dental plaque fluorescence image and a tooth three-dimensional model.
Background
Caries is a tooth hard tissue disease with chronic progressive destruction, is highly developed in the world, is classified as a non-infectious disease for human three major prevention and treatment by the world health organization together with cancer and cardiovascular diseases, and can cause local pain, infection and tooth loss if being treated by delay, and is closely related to systemic diseases such as cardiovascular diseases, diabetes and the like. Caries prevention, which is guided by Caries Risk Assessment (CRA), is considered the basis of modern Caries prevention and management, and such systems predict the likelihood of an individual developing new Caries over time. The caries-associated factors that are encompassed by the current CRA system overlap to a large extent, such as caries experience, saliva, diet, systemic conditions, and fluorine exposure, and the influence of tooth morphology on the risk of caries occurrence is under consideration.
At present, the caries is generally diagnosed by methods such as visual examination, probes, X-ray and the like in clinic. The visual examination mainly utilizes the visual observation and experience judgment of the stomatologist, has strong subjectivity and can not diagnose early caries loss; dental explorations can judge whether the tip can be hooked or not by scratching the surface of enamel by the probe tip, and can judge whether a carious cavity is formed or not, and can not diagnose carious lesions in early stage. In order to shift caries from "destructive treatment" to "early prevention", non-destructive quantifiable optical detection methods are the focus of research for early diagnosis of caries. Based on the difference of the substance composition and structural characteristics of tooth tissue and the interaction mechanism of light and tissue, the Optical caries early detection methods developed at present include fluorescence technology, Optical Coherence Tomography (OCT), raman spectroscopy, etc. The fluorescence technology utilizes the difference of autofluorescence spectrum generated by dental plaque formed by mixing oral cariogenic bacteria and food residues under the excitation of ultraviolet light, and can quantify the content of dental plaque by collecting and analyzing spectral distribution, which is used as a characterization parameter of caries risk. The fluorescence image distribution of decayed teeth and healthy teeth is contrastively analyzed by utilizing a quantitative light guide fluorescence QLF technology, and compared with the detection result of a polarization microscope, the effectiveness of the fluorescence method is verified.
At present, the researches on caries are partially traditional, and the exploration and application of optical technology, 3D digitization technology and artificial intelligence technology in the field are promoted by the series of problems, so that a plurality of intelligent medical information processing technologies are produced at the same time. However, in the field of treatment or prevention, the tooth as a host of caries is less researched on the morphological structure, and the research on the relationship between the tooth surface morphology and the caries is of great significance, and the research firstly needs to realize the establishment of the relationship between the plaque distribution directly related to the caries and the tooth three-dimensional morphology. Therefore, the invention provides a method and a system for registering a dental plaque fluorescence image and a tooth three-dimensional model.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a registration method of a dental plaque fluorescence image and a tooth three-dimensional model, which realizes registration of an occlusal surface contour and a 3D projection contour of a 2D fluorescence image by iteratively solving a mapping model, obtains a corresponding relation between the dental plaque 2D fluorescence image and tooth occlusal surface 3D data, determines three-dimensional space distribution of dental plaque on the tooth surface and provides a basis for researching the three-dimensional distribution of dental plaque on the tooth surface.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method of registration of a dental plaque fluorescence image with a three-dimensional model of a tooth, comprising:
s1, obtaining three-dimensional model point cloud data P of tooth surfacesAnd fluorescence image F of occlusal surface of toothImg；
S2, carrying out fluorescence image F on occlusal surface of toothImgProcessing to obtain tooth area and contour information Edge1；
S3, carrying out point cloud data P on the three-dimensional model of the tooth surfacesCarrying out posture correction to obtain a corrected tooth three-dimensional model Ps；
S4, establishing a corrected tooth three-dimensional model PsFluorescence image F of occlusal surface of toothImgThe corrected three-dimensional tooth model PsPoint on to the fluorescence image FImgThe above.
Further, the step S1 specifically includes:
s11, acquiring dentition three-dimensional data through an oral cavity three-dimensional scanner to obtain a three-dimensional model P of the tooth surfaces；
S12, obtaining a plaque fluorescence image F of the occlusal surface of the tooth through an oral fluorescence imagerImg。
Further, the step S2 specifically includes:
s21, enabling the tooth three-dimensional model PsCorresponding fluorescence data input into the fluorescence image FImgAnd a fluorescence image F of the occlusal surface of the toothImgCarrying out treatment;
s22, segmenting the tooth area from other areas by adopting an automatic threshold segmentation method to obtain the tooth area and contour information Edge1。
Further, the fluorescence image F of the occlusal surface of the tooth in the step S21ImgThe processing method for processing is mean filtering.
Further, the automatic threshold segmentation method adopted in step S22 is an OTSU global automatic threshold segmentation method.
Further, the step S3 is to perform a three-dimensional model point cloud data P on the teeth by a three-dimensional model space transformation formulasAnd (5) carrying out posture correction.
Further, the three-dimensional model point cloud data P is shown in step S3sThe posture correction is carried out by a PCA principal component analysis method.
Further, the step S4 specifically includes:
s41, calibrating the oral fluorescence imager for obtaining the fluorescence image of the occlusal surface of the tooth to obtain an internal reference matrix M of the oral fluorescence imager1；
S42, inputting the corrected tooth three-dimensional model PsFluorescence image of tooth FImgEdge of the contour information1Internal reference matrix M1And initializing the external parameter matrix M2Setting a matching distance threshold Ths；
S43, utilizing the external parameter matrix M2And an internal reference matrix M1Calculating an imaging image TPD of the tooth three-dimensional model under the virtual imaging systemimg；
S44, imaging image TPD under the virtual imaging systemimgPreprocessing is carried out, and an imaging image TPD under a virtual imaging system is calculatedimgObtaining the projection Edge of the tooth three-dimensional model2；
S45, calculating the projection Edge of the obtained tooth three-dimensional model2Contour information Edge from each two-dimensional coordinate point to tooth fluorescence image1Storing the corresponding relation and the distance information of the point with the nearest Euclidean distance in each two-dimensional coordinate point;
s46, obtaining projection Edge of tooth three-dimensional model2Two-dimensional point coordinates and corresponding three-dimensional model PsAnd calculating the average value T of the distances of all the corresponding pointsavg(ii) a Judging the average value TavgWhether the distance is less than a set matching distance threshold value ThsIf yes, outputting the current three-dimensional projection matrix M2As a registration matrix between the fluorescence image and the three-dimensional model data; if not, updating the external parameter matrix M by using a three-dimensional projection matrix equation2And proceeds to step S43;
s47, obtaining an external parameter matrix M according to the final result2And the reference matrix M1Using the three-dimensional projection matrix, the three-dimensional tooth model PsThe point of which is projected onto the fluorescence image.
Further, the step S44 includes acquiring a contour in the tooth three-dimensional model, performing an edge extraction operation on the fluorescence image to obtain a projection image contour point and a three-dimensional space point corresponding to the contour point, and storing all data.
Further, the parameter matrix M is updated by the least square method according to the obtained corresponding point information and the imaging model in step S462。
Compared with the prior art, the invention has the beneficial effects that:
1. the processing object of the invention is high-precision tooth three-dimensional point cloud data, and the processing speed is high, the precision is high, the realization is convenient, and the popularization is easy.
2. The invention provides a method for registering a dental plaque fluorescence image and a tooth three-dimensional model, and lays a foundation for researching the relation between the tooth three-dimensional form and dental caries.
3. The invention aims at the method for mapping the dental plaque fluorescence image and the dental three-dimensional model, adopts a simple projection matrix initialization mode according to the priori knowledge, reduces a large amount of calculation, and simultaneously makes the algorithm simpler and more efficient.
Drawings
FIG. 1 is a flowchart of a method for registration of a dental plaque fluorescence image with a three-dimensional model of a tooth according to an embodiment;
FIG. 2 is a schematic diagram of preferred dental point cloud data read in according to an embodiment;
FIG. 3 is a schematic diagram of preferred dental fluorescence image data read in according to one embodiment;
FIG. 4 is a graph showing the mean filtering result of a tooth fluorescence image according to an embodiment;
FIG. 5 is a diagram illustrating an automatic threshold segmentation result of a tooth fluorescence image according to an embodiment;
FIG. 6 is a diagram illustrating a tooth fluorescence image contour extraction result according to an embodiment;
FIG. 7 is a diagram illustrating dental point cloud data corrected according to an embodiment;
FIG. 8 is a diagram illustrating camera calibration results provided in accordance with an embodiment;
FIG. 9 is a diagram illustrating a projection result of dental point cloud data according to an embodiment;
FIG. 10 is a diagram illustrating the result of filling the gaps in the dental point cloud data projection according to an embodiment;
FIG. 11 is a schematic diagram of a point cloud projection contour acquisition result provided in the first embodiment;
FIG. 12 is an iteration M provided in the first embodiment2The outline matching result of the process is shown schematically;
FIG. 13 is a calculation M provided in the first embodiment2In-process contour mean distance result representationA drawing;
FIG. 14 is a graphical representation of the results provided by one embodiment of the final mapping of dental plaque fluorescence images onto a three-dimensional model.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to overcome the defects of the prior art and provides a method for registering a dental plaque fluorescence image with a tooth three-dimensional model.
Example one
The present embodiment provides a method for registering a dental plaque fluorescence image with a three-dimensional model of teeth, as shown in fig. 1, comprising:
s11, obtaining three-dimensional model point cloud data P of tooth surfacesAnd fluorescence image F of occlusal surface of toothImg；
S12, carrying out fluorescence image F on occlusal surface of toothImgProcessing to obtain tooth area and contour information Edge1；
S13, carrying out point cloud data P on the three-dimensional model of the tooth surfacesCarrying out posture correction to obtain a corrected tooth three-dimensional model Ps；
S14, establishing a corrected tooth three-dimensional model PsFluorescence image F of occlusal surface of toothImgThe corrected three-dimensional tooth model PsPoint on to the fluorescence image FImgThe above.
In step S11, three-dimensional model point cloud data P of the tooth surface is acquiredsAnd fluorescence image F of occlusal surface of toothImg。
In this embodimentFirstly, an intraoral three-dimensional scanner is used for obtaining point cloud data P of a three-dimensional model of the surface of a toothsThen, a fluorescence image F of the occlusal surface of the tooth is obtained by utilizing a plaque fluorescence imagerImg. The method specifically comprises the following steps:
s111, acquiring dentition three-dimensional data through an oral cavity three-dimensional scanner, cutting out a single molar to obtain a three-dimensional model P of the surface of the toothsAs shown in fig. 2;
s112, obtaining a plaque fluorescence image F of the occlusal surface of the tooth through an oral fluorescence imagerImgAs shown in fig. 3.
In step S12, fluorescence image F of occlusal surface of the tooth is obtainedImgProcessing to obtain tooth area and contour information Edge1。
In the present embodiment, the dental fluorescence image FImgAnd (3) processing: fluorescence image of tooth FImgAnd processing to obtain tooth area and contour information. The method specifically comprises the following steps:
s121, enabling the tooth three-dimensional model P to besCorresponding fluorescence data input into the fluorescence image FImgAnd a fluorescence image F of the occlusal surface of the toothImgCarrying out treatment;
the plaque fluorescence image is subjected to mean filtering by adopting a preprocessing method, and the dental plaque fluorescence image is acquired in a dark environment in the oral cavity, so that a lot of noise exists, the noise can be effectively filtered by adopting the mean filtering, the accuracy of extracting a tooth area from the whole image is improved, and the mean filtering result is shown in fig. 4.
S122, segmenting the tooth area from other areas by adopting an automatic threshold segmentation method to obtain the tooth area and contour information Edge1。
The adopted automatic threshold segmentation method is a common image segmentation method, and has the advantages of simple realization principle, small calculated amount and stable performance, so the method is widely applied to the field of image segmentation. Assuming that the image f (x, y) is composed of a dark background and a lighter object, there is a significant difference in gray level between the two, therefore, a threshold T can be selected to separate the object from the background in the image as shown in the following formula, and the newly generated image is g (x, y)
After thresholding, the background part of the image is set to black, and the color of the target part keeps the self color. In the process of image thresholding, the selection of a gray threshold T is very important, a proper value T can effectively remove irrelevant information, and in a tooth fluorescence image, due to the complexity of an acquisition environment, the brightness of a plurality of fluorescence images has great difference, so that an algorithm capable of determining the threshold T according to the information of the image per se is required to be adopted to realize the accurate extraction of a tooth region.
The principle is that the image is divided into a background part and a foreground part according to the gray characteristic of the image, the larger the method between the background and the foreground is, the larger the difference between the two parts forming the image is, and the specific description of the algorithm is as follows;
let the total number of pixels of an image be N, and the gray scale range of the pixels be 0-L-1]The number of pixels having a gray value i is niThen, the probability of occurrence of the pixel with the gray value i is:
setting a division threshold value to T0Dividing the gray value of the whole image into C0And C1Two kinds, wherein C0Is distributed in [ C ]0～T0-1],C1Corresponding gray value is in [ T ]0～L-1]In between, then C0And C1Probability w of0And w1Respectively as follows:
C1and C1Mean value u of0，u1Comprises the following steps:
the mean grayscale value of the entire image is:
μ＝ω0μ0+ω1μ1
defining the variance between two gray scale partition classes as:
σ2＝ω0(μ0-μ)2+ω1(μ1-μ)2
in determining the segmentation threshold T0When it is, let T0Starting from 0, sequentially increasing to L-1 by step size 1, recording T each time0Corresponding between-class variance σ2，σ2Maximum T0I.e. the optimal segmentation threshold. The tooth fluorescence image is automatically segmented by the method, and the segmentation result is shown in fig. 5.
The extraction method of the tooth contour edge point comprises the steps of traversing one pixel, judging whether eight adjacent pixels simultaneously contain tooth region points and non-tooth region points, if so, determining the contour edge point, and if not, determining the contour edge point. Obtaining the tooth Edge corresponding to the fluorescence image1Wherein Edge1The data structure contained in is { { Ix1,Iy1},{Ix2,Iy2},...,{Ixi,Iyi} in which { I }xi,IyiAnd the corresponding image coordinates of the ith edge point of the fluorescence image are shown. The extracted contour structure is shown in fig. 6.
In step S13, point cloud data P of three-dimensional model of the tooth surfacesCarrying out posture correction to obtain a corrected tooth three-dimensional model Ps。
Inputting tooth three-dimensional point cloud data PsCalculating the gravity center and the main direction of the tooth point cloud data, and performing posture correction on the tooth three-dimensional point cloud data by using a three-dimensional model space transformation formula to ensure that the origin of a space coordinate system is at the gravity center position of the tooth point cloud data model, a occlusal plane is parallel to an XOY plane of the space coordinate system and is vertical to a Z axis of the space coordinate system, so as to obtain the corrected toothThree-dimensional model point set as new Ps(ii) a The corrected point cloud model is shown in fig. 7.
And (3) adopting a PCA principal component analysis method for posture correction of the tooth model Ps, calculating the principal direction of the point cloud, wherein the principal direction is the Z-axis direction, and converting the principal direction into the Z-axis direction by using a three-dimensional space coordinate conversion matrix. The three-dimensional space coordinate conversion formula is
Wherein the matrix is determined R, T using the method of PCA.
In step S14, a corrected tooth three-dimensional model P is createdsFluorescence image F of occlusal surface of toothImgThe corrected three-dimensional tooth model PsPoint on to the fluorescence image FImgThe above.
In the present embodiment, a three-dimensional model P of the teeth is createdsFluorescence image of bacterial plaqueImgThe three-dimensional tooth model PsPoint on to plaque fluorescence image FImgThe above. The method specifically comprises the steps of calculating an imaging image of a tooth three-dimensional model according to a camera imaging model, comparing the imaging image outline with a fluorescence image outline, updating a projection matrix, realizing the same tooth imaging outline and fluorescence image outline, and realizing the three-dimensional tooth model PsThe points on are mapped onto the fluorescence image. The method specifically comprises the following steps:
s141, calibrating the oral cavity fluorescence imager for obtaining the tooth occlusal surface fluorescence image to obtain an internal reference matrix M of the oral cavity fluorescence imager1；
Calibrating the fluorescent imaging device in the oral cavity by adopting a Zhang Zhengyou calibration method to obtain a camera internal reference matrix M1The calibration plate image acquired by the camera calibration is shown in fig. 8. Its camera internal reference matrix M1Comprises the following steps:
and adopting a Zhangyingyou calibration method, shooting a plurality of chessboard calibration plate pictures by using an oral fluorescence imager, calibrating the imaging device, and obtaining a camera imaging internal reference matrix.
S142, inputting the corrected tooth three-dimensional model PsFluorescence image of tooth FImgEdge of the contour information1Internal reference matrix M1And initializing the external parameter matrix M2Setting a matching distance threshold Ths；
Inputting the corrected tooth three-dimensional model PsFluorescence image F of the corresponding toothImgOutline data Edge1Camera internal reference matrix M1(ii) a Initializing an extrinsic parameter matrix M2Setting a matching distance threshold Ths。
The initial projection matrix adopts empirical values, and because the acquisition angle is vertical to the occlusal surface of the tooth and is about 1-2 cm away from the surface of the tooth when the fluorescence image of the tooth is acquired, the external parameter matrix M can be initialized2Is composed of
S143, utilizing the external parameter matrix M2And an internal reference matrix M1Calculating an imaging image TPD of the tooth three-dimensional model under the virtual imaging systemimg；
The imaging relation model of the imaging system is utilized to obtain the imaging of the three-dimensional tooth model in the fluorescence imaging system, and the imaging model is
Wherein, ax＝c/dxScale factor on the u-axis, also known as equivalent (normalized) focal length on the u-axis; a isy＝c/dyScale factor on the v-axis, also known as equivalent (normalized) focal length on the v-axis; m is a 3 × 4 matrix called projection matrix, M1From camera parameters ax,ay,u0,v0Common decisions, called internal reference matrix, M2By cameras relative to the world coordinate systemThe extrinsic parameters are determined and called an extrinsic parameter matrix.
According to the perspective imaging matrix of the camera, the tooth point cloud data can be obtained in M1And M2In the projection matrix, since only the contour is used as the feature value, it is not necessary to calculate the distance value between each point and the coordinates of the viewpoint camera to generate a gray image, thereby reducing the speed of a large number of calculation and speeding up the algorithm, so another simple method is adopted to obtain a black and white image of the point cloud projection, when there is a corresponding three-dimensional projection point on the two-dimensional image point, the point is set to be white, and the other places are set to be black, and after the operation, a silhouette image of the three-dimensional model of the tooth is obtained, and the projection image is shown in fig. 9.
S144, imaging image TPD under the virtual imaging systemimgPreprocessing is carried out, and an imaging image TPD under a virtual imaging system is calculatedimgObtaining the projection Edge of the tooth three-dimensional model2；
To imaging image TPDimgPreprocessing and calculating imaging image TPDimgObtaining the projection Edge of the tooth three-dimensional model2. Wherein Edge2The data structure contained in the data structure is { { I { (I)x1,Iy1},{Xp1,Yp1,Zp1}},{{Ix2,Iy2},{Xp2,Yp2,Zp2}},...,{{Ixj,Iyj},{Xpj,Ypj,Zpj}, where { I } is equal toxj,IyjIs the image coordinate corresponding to the jth edge point, { Xpj,Ypj,ZpjAnd is the three-dimensional space point corresponding to the jth projection edge point.
Because the point cloud of the tooth point is sparse and the resolution of the fluorescence image is large, the obtained tooth model silhouette image forms one point, the image needs to be operated in order to accurately extract the outline, the closed operation in the image morphology processing is selected, the image is expanded to fill small gaps, and then the original edge is restored to an original shape by using corrosion with the same size, so that a complete and gap-free tooth model white silhouette can be obtained, and the image is shown in fig. 10.
When the projection contour is obtained, the same edge operation with the fluorescence image is adopted to obtain the projection image contour point and the three-dimensional space point corresponding to the contour point, and all data are stored. The outline image is shown in fig. 11.
S145, calculating the projection Edge of the obtained tooth three-dimensional model2Contour information Edge from each two-dimensional coordinate point to tooth fluorescence image1Storing the corresponding relation and the distance information of the point with the nearest Euclidean distance in each two-dimensional coordinate point;
in this embodiment, Edge is calculated2In each two-dimensional coordinate point { I }xj,Iyj} to Edge1In each two-dimensional coordinate point { I }xi,IyiAnd storing the corresponding relation and the distance information at the point with the nearest Euclidean distance.
S146, obtaining projection Edge of tooth three-dimensional model2Two-dimensional point coordinates and corresponding three-dimensional model PsAnd calculating the average value T of the distances of all the corresponding pointsavg(ii) a Judging the average value TavgWhether the distance is less than a set matching distance threshold value ThsIf yes, outputting the current three-dimensional projection matrix M2As a registration matrix between the fluorescence image and the three-dimensional model data; if not, updating the external parameter matrix M by using a three-dimensional projection matrix equation2And continues to execute step S143;
deleting Edge according to 3 sigma principle for distance information2The Edge if the number of the remaining matching points is too large2A part of the points are randomly selected to form a paired set U of about 200 points, and the set U comprises two-dimensional point coordinates { I ] of the fluorescence imagexi,IyiAnd the corresponding three-dimensional PsCorresponding point of (C) { X)pj,Ypj,Zpj}. Calculating the average value T of the distances of all corresponding pointsavgIf T isavg<ThsThen the current projection matrix M is output2As a registration matrix between the fluorescence image 2D and the three-dimensional model 3D data. Otherwise, using three-dimensional projection matrix equationNew projection matrix M2Then, the process returns to step S143 to continue the iteration.
Updating the external parameter matrix M of the camera by using the obtained corresponding point information and the imaging model and adopting a least square method2. The contour updating process is shown in fig. 12. Final M2The matrix is
In an iterative process, the average of all point distances decreases as shown in fig. 13.
According to the finally obtained external parameter projection matrix M2And fluorescence camera internal reference matrix M1The three-dimensional projection matrix can be utilized to realize the three-dimensional tooth model PsA certain point above is projected onto the fluorescence image, and the mapping result is shown in fig. 14.
S147. according to the finally obtained external parameter matrix M2And the reference matrix M1Using the three-dimensional projection matrix, the three-dimensional tooth model PsThe point of which is projected onto the fluorescence image.
In the present embodiment, the projection matrix M is obtained according to the final result2And fluorescence camera internal reference matrix M1The three-dimensional projection matrix can be utilized to realize the three-dimensional tooth model PsA certain point on the image is projected onto the fluorescence image.
Compared with the prior art, the beneficial effect of this embodiment is:
1. the processing object of the embodiment is high-precision tooth three-dimensional point cloud data, and the processing speed is high, the precision is high, the implementation is convenient, and the popularization is easy.
2. The embodiment provides a method for registering a dental plaque fluorescence image and a tooth three-dimensional model, and lays a foundation for researching the relation between the tooth three-dimensional form and caries.
3. According to the method for mapping the dental plaque fluorescence image and the dental three-dimensional model, a simple projection matrix initialization mode is adopted according to priori knowledge, a large amount of calculated amount is reduced, and meanwhile, the algorithm is simpler and more efficient.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A method of registering a dental plaque fluorescence image with a three-dimensional model of a tooth, comprising:
s1, obtaining three-dimensional model point cloud data P of tooth surfacesAnd fluorescence image F of occlusal surface of toothImg；
S2, carrying out fluorescence image F on occlusal surface of toothImgProcessing to obtain tooth area and contour information Edge1；
S3, carrying out point cloud data P on the three-dimensional model of the tooth surfacesCarrying out posture correction to obtain a corrected tooth three-dimensional model Ps；
S4, establishing a corrected tooth three-dimensional model PsFluorescence image F of occlusal surface of toothImgThe corrected three-dimensional tooth model PsPoint on to the fluorescence image FImgThe above.
2. The method for registration of a dental plaque fluorescence image with a three-dimensional model of a tooth according to claim 1, wherein said step S1 specifically comprises:
s11, acquiring dentition three-dimensional data through an oral cavity three-dimensional scanner to obtain a three-dimensional model P of the tooth surfaces；
S12, obtaining a plaque fluorescence image F of the occlusal surface of the tooth through an oral fluorescence imagerImg。
3. The method for registration of a dental plaque fluorescence image with a three-dimensional model of a tooth according to claim 1, wherein said step S2 specifically comprises:
s21, enabling the tooth three-dimensional model PsCorresponding fluorescence data input into the fluorescence image FImgAnd a fluorescence image F of the occlusal surface of the toothImgCarrying out treatment;
s22, segmenting the tooth area from other areas by adopting an automatic threshold segmentation method to obtain the tooth area and contour information Edge1。
4. The method for registering a dental plaque fluorescence image with a three-dimensional model of a tooth as claimed in claim 3, wherein the fluorescence image F of the occlusal surface of the tooth in step S21ImgThe processing method for processing is mean filtering.
5. The method for registration of a dental plaque fluorescence image with a three-dimensional model of a tooth according to claim 3, wherein the automatic threshold segmentation method employed in step S22 is a global automatic threshold segmentation method of OTSU.
6. The method for registering a dental plaque fluorescence image with a tooth three-dimensional model according to claim 1, wherein the step S3 is to perform point cloud data P on the tooth three-dimensional model by a three-dimensional model space transformation formulasAnd (5) carrying out posture correction.
7. The method for registration of dental plaque fluorescence image with tooth three-dimensional model according to claim 6, wherein the step S3 is performed on the point cloud data P of the three-dimensional modelsThe posture correction is carried out by a PCA principal component analysis method.
8. The method for registration of a dental plaque fluorescence image with a three-dimensional model of a tooth according to claim 3, wherein said step S4 specifically comprises:
s41, calibrating the oral fluorescence imager for obtaining the fluorescence image of the occlusal surface of the tooth to obtain an internal reference matrix M of the oral fluorescence imager1；
S42, inputting the corrected teethThree-dimensional model PsFluorescence image of tooth FImgEdge of the contour information1Internal reference matrix M1And initializing the external parameter matrix M2Setting a matching distance threshold Ths；
S43, utilizing the external parameter matrix M2And an internal reference matrix M1Calculating an imaging image TPD of the tooth three-dimensional model under the virtual imaging systemimg；
S44, imaging image TPD under the virtual imaging systemimgPreprocessing is carried out, and an imaging image TPD under a virtual imaging system is calculatedimgObtaining the projection Edge of the tooth three-dimensional model2；
S45, calculating the projection Edge of the obtained tooth three-dimensional model2Contour information Edge from each two-dimensional coordinate point to tooth fluorescence image1Storing the corresponding relation and the distance information of the point with the nearest Euclidean distance in each two-dimensional coordinate point;
s46, obtaining projection Edge of tooth three-dimensional model2Two-dimensional point coordinates and corresponding three-dimensional model PsAnd calculating the average value T of the distances of all the corresponding pointsavg(ii) a Judging the average value TavgWhether the distance is less than a set matching distance threshold value ThsIf yes, outputting the current three-dimensional projection matrix M2As a registration matrix between the fluorescence image and the three-dimensional model data; if not, updating the external parameter matrix M by using a three-dimensional projection matrix equation2And proceeds to step S43;
s47, obtaining an external parameter matrix M according to the final result2And the reference matrix M1Using the three-dimensional projection matrix, the three-dimensional tooth model PsThe point of which is projected onto the fluorescence image.
9. The method according to claim 8, wherein the step S44 further comprises obtaining a contour in the tooth three-dimensional model, performing an edge extraction operation on the fluorescence image, obtaining a projection image contour point and a three-dimensional space point corresponding to the contour point, and storing all data.
10. The method for registering a dental plaque fluorescence image with a three-dimensional dental model according to claim 8, wherein the step S46 is performed by updating the external parameter matrix M by using a least square method according to the obtained corresponding point information and the imaging model2。
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