CN111311653B - Method for registering dental plaque fluorescent image and tooth three-dimensional model - Google Patents
Method for registering dental plaque fluorescent image and tooth three-dimensional model Download PDFInfo
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Abstract
The invention discloses a method for registering dental plaque fluorescent images and a tooth three-dimensional model, which comprises the following steps: s11, acquiring three-dimensional model point cloud data P of tooth surfaces s Fluorescence image F of occlusal surface of tooth Img The method comprises the steps of carrying out a first treatment on the surface of the S12, fluorescence image F of occlusal surface of tooth Img Processing to obtain tooth region and contour information Edge 1 The method comprises the steps of carrying out a first treatment on the surface of the S13, carrying out point cloud data P on the three-dimensional model of the tooth surface s Posture correction is carried out to obtain a corrected tooth three-dimensional model P s The method comprises the steps of carrying out a first treatment on the surface of the S14, establishing a corrected tooth three-dimensional model P s Fluorescence image F of occlusal surface of tooth Img Is to be corrected into a three-dimensional tooth model P s Mapping of points on to fluorescence image F Img And (3) upper part. According to the invention, the mapping model is solved iteratively, so that the registration of the occlusal surface contour of the 2D fluorescent image and the 3D projection contour is realized, the corresponding relation between the dental plaque 2D fluorescent image and the dental occlusal surface 3D data is obtained, the three-dimensional spatial distribution of dental plaque on the dental surface is determined, and a foundation is provided for researching the three-dimensional distribution of dental plaque on the dental surface.
Description
Technical Field
The invention relates to the technical field of intelligent prevention of caries by computer assistance, in particular to a method for registering dental plaque fluorescent images and a tooth three-dimensional model.
Background
Caries is a dental hard tissue disease with chronic progressive destruction, which is highly developed worldwide, and the world health organization has juxtaposed cancer and cardiovascular diseases as non-infectious diseases for the three major important prevention and treatment of human beings, and if the treatment is delayed, the dental hard tissue disease can cause local pain, infection and tooth loss, and is closely related to systemic diseases such as cardiovascular diseases, diabetes mellitus and the like. Caries prevention guided by caries risk assessment (Caries Risk Assessment, CRA) is considered the basis of modern caries prevention and management, such systems being able to predict the likelihood of an individual developing new caries over a period of time. Caries-related factors encompassed by current CRA systems overlap to a large extent, such as caries experience, saliva, diet, systemic conditions, and fluoride exposure, and the like, with inadequate consideration of the effects of tooth morphology itself on caries occurrence risk.
At present, methods such as visual diagnosis, probes, X-rays and the like are commonly used for diagnosing caries in clinic. The visual inspection mainly utilizes the vision observation and experience judgment of an stomatologist, has strong subjectivity and can not diagnose early caries; the dental inspection is performed by scratching the enamel surface through the tip of the probe, judging whether the tip can be hooked, judging whether caries is formed or not, and also failing to diagnose caries in early stage. In order to shift caries from "destructive treatment" to "early prevention", non-destructive quantifiable optical detection methods are becoming a research hotspot for early diagnosis of caries. Based on the differences of the material composition and structural characteristics of tooth tissue and the interaction mechanism of light and tissue, the currently developed optical caries early detection methods comprise fluorescence technology, optical coherence tomography (Optical Coherence Tomography, OCT), raman spectrum analysis and the like. The fluorescence technology utilizes the difference of the self fluorescence spectrum generated by dental plaque formed by mixing oral cavity cariogenic bacteria and food residues under ultraviolet excitation, and can quantify the dental plaque content by collecting and analyzing the spectral distribution, and the dental plaque content is used as a characterization parameter of caries risk. The fluorescence image distribution of decayed teeth and healthy teeth is compared and analyzed by utilizing the 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, research on caries is more traditional, and it is this series of problems that promote exploration and application of optical technology, 3D digital technology and artificial intelligence technology in the field, and many intelligent medical information processing technologies have developed. However, in the field of treatment or prophylaxis, teeth are used as hosts for caries, but less research is conducted on the morphological structure of teeth, and research on the relationship between the tooth surface morphology and caries is of great importance, and the research is needed to achieve the establishment of the relationship between plaque distribution directly related to caries and the three-dimensional morphology of teeth. Therefore, the invention provides a method and a system for registering dental plaque fluorescent images with a tooth three-dimensional model.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for registering a dental plaque fluorescent image and a dental three-dimensional model, which realizes registration of an occlusal surface contour of a 2D fluorescent image and a 3D projection contour by iteratively solving a mapping model, obtains a corresponding relation between the dental plaque 2D fluorescent image and dental occlusion surface 3D data, determines three-dimensional space distribution of dental plaque on the dental surface, and provides a basis for researching the three-dimensional distribution of dental plaque on the dental surface.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method of registering a dental plaque fluoroscopic image with a three-dimensional model of a tooth, comprising:
s1, acquiring three-dimensional model point cloud data P of tooth surfaces s Fluorescence image F of occlusal surface of tooth Img ；
S2, fluorescence image F of occlusal surface of tooth Img Processing to obtain tooth region and contour information Edge 1 ；
S3, carrying out point cloud data P on the three-dimensional model of the tooth surface s Posture correction is carried out to obtain a corrected tooth three-dimensional model P s ；
S4, establishing a corrected tooth three-dimensional model P s Fluorescence image F of occlusal surface of tooth Img Is to be corrected into a three-dimensional tooth model P s Mapping of points on to fluorescence image F Img And (3) upper part.
Further, the step S1 specifically includes:
s11, acquiring three-dimensional data of dentition through an oral cavity three-dimensional scanner to obtain a three-dimensional model P of the tooth surface s ；
S12, acquiring a plaque fluorescence image F of the occlusal surface of the tooth through an oral cavity fluorescence imager Img 。
Further, the step S2 specifically includes:
s21, three-dimensional modeling of teeth P s Corresponding fluorescence data is input into the fluorescence image F Img And to the fluorescence image F of the occlusal surface of the tooth Img Processing;
s22, dividing the tooth area from other areas by adopting an automatic threshold segmentation method to obtain tooth area and contour information Edge 1 。
Further, in the step S21, a fluorescence image F of the occlusal surface of the tooth is obtained Img The processing method for processing is mean filtering.
Further, the automatic threshold segmentation method adopted in the step S22 is a global automatic threshold segmentation method of the OTSU.
Further, the step S3 is specifically to apply a three-dimensional model space transformation formula to the three-dimensional model point cloud data P of the tooth s Posture correction is performed.
Further, in the step S3, the three-dimensional model point cloud data P is shown s Posture correction is performed by a method of PCA principal component analysis.
Further, the step S4 specifically includes:
s41, calibrating an oral cavity fluorescence imager for acquiring fluorescence images of occlusal surfaces of teeth to obtain an internal reference matrix M of the oral cavity fluorescence imager 1 ；
S42, inputting the corrected tooth three-dimensional model P s Fluorescence image F of tooth Img Contour information Edge of (a) 1 Internal reference matrix M 1 And initialize the extrinsic matrix M 2 Setting a matching distance threshold T hs ；
S43, utilizing the external parameter matrix M 2 Internal reference matrix M 1 Computing an imaging image TPD of a three-dimensional model of a tooth under a virtual imaging system img ；
S44, performing imaging image TPD under the virtual imaging system img Preprocessing and calculating an imaging image TPD under a virtual imaging system img Obtain projected Edge of tooth three-dimensional model 2 ；
S45, calculating a projection Edge of the obtained tooth three-dimensional model 2 Contour information Edge from each two-dimensional coordinate point to tooth fluorescence image 1 The point with the nearest Euclidean distance in each two-dimensional coordinate point is stored with the corresponding relation and the distance information;
s46, acquiring a projection Edge of the tooth three-dimensional model 2 Two-dimensional point coordinates and corresponding three-dimensional modelP type s Corresponding points on the graph, and calculating the average value T of the distances of all the corresponding points avg The method comprises the steps of carrying out a first treatment on the surface of the Judging the average value T avg Whether or not it is smaller than a set matching distance threshold T hs If yes, outputting a current three-dimensional projection matrix M 2 As a registration matrix between the fluoroscopic image and the three-dimensional model data; if not, updating the external parameter matrix M by using a three-dimensional projection matrix equation 2 And continues to step S43;
s47, according to the finally obtained external parameter matrix M 2 And an internal reference matrix M 1 Using the three-dimensional projection matrix to model the three-dimensional teeth P s The above points are projected onto the fluoroscopic image.
Further, the step S44 further includes obtaining a contour in the three-dimensional model of the tooth, and 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, in the step S46, the external parameter matrix M is updated by using a least square method through the obtained corresponding point information and the imaging model 2 。
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 has the advantages of high processing speed, high precision, convenient realization and easy popularization.
2. The invention provides a method for registering dental plaque fluorescent images and a tooth three-dimensional model, which lays a foundation for researching the relationship between the tooth three-dimensional form and caries.
3. The method aims at the tooth plaque fluorescent image and tooth three-dimensional model mapping method, adopts a simple mode of initializing a projection matrix according to priori knowledge, reduces a large amount of calculation amount, and simultaneously makes the algorithm simpler and more efficient.
Drawings
FIG. 1 is a flow chart of a method for registering a dental plaque fluoroscopic image with a three-dimensional model of a tooth according to one embodiment;
FIG. 2 is a schematic view of read-in preferred dental point cloud data provided in example one;
FIG. 3 is a schematic representation of read-in preferred dental fluorescence image data provided in example one;
FIG. 4 is a graph showing the results of the mean filtering of fluorescence images of teeth according to the first embodiment;
FIG. 5 is a schematic view of automatic thresholding of dental fluorescence images provided in accordance with an embodiment;
FIG. 6 is a schematic view of a tooth fluorescence image profile extraction result provided in the first embodiment;
FIG. 7 is a schematic diagram of the data of the corrected tooth point cloud data according to the first embodiment;
FIG. 8 is a schematic view of camera calibration results provided in accordance with an embodiment;
FIG. 9 is a schematic diagram of a projection result of tooth point cloud data according to the first embodiment;
FIG. 10 is a schematic diagram of the result of filling the gap with the projection of the tooth point cloud data according to the first embodiment;
FIG. 11 is a schematic view of a point cloud projection profile acquisition result provided in the first embodiment;
FIG. 12 is an iteration M provided by embodiment one 2 Schematic diagram of contour matching result in the process;
FIG. 13 is a calculation M provided in embodiment one 2 Schematic diagram of contour average distance result in the process;
FIG. 14 is a schematic representation of the results of the final mapping of dental plaque fluorescence images onto a three-dimensional model provided in example one.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The invention aims at overcoming the defects of the prior art and provides a method for registering a dental plaque fluorescent image and a dental three-dimensional model.
Example 1
The present embodiment provides a method for registering a dental plaque fluorescent image with a three-dimensional model of a tooth, as shown in fig. 1, including:
s11, acquiring three-dimensional model point cloud data P of tooth surfaces s Fluorescence image F of occlusal surface of tooth Img ；
S12, fluorescence image F of occlusal surface of tooth Img Processing to obtain tooth region and contour information Edge 1 ；
S13, carrying out point cloud data P on the three-dimensional model of the tooth surface s Posture correction is carried out to obtain a corrected tooth three-dimensional model P s ；
S14, establishing a corrected tooth three-dimensional model P s Fluorescence image F of occlusal surface of tooth Img Is to be corrected into a three-dimensional tooth model P s Mapping of points on to fluorescence image F Img And (3) upper part.
In step S11, three-dimensional model point cloud data P of the tooth surface is acquired s Fluorescence image F of occlusal surface of tooth Img 。
In this embodiment, first, three-dimensional model point cloud data P of the tooth surface is acquired by using an intraoral three-dimensional scanner s Then, a fluorescence image F of the occlusal surface of the tooth is obtained by using a plaque fluorescence imager Img . The method comprises the following steps:
s111, acquiring three-dimensional data of dentition through an oral cavity three-dimensional scanner, and then cutting out single molar to obtain a three-dimensional model P of the tooth surface s As shown in fig. 2;
s112, acquiring a plaque fluorescence image F of the occlusal surface of the tooth through an oral cavity fluorescence imager Img As shown in fig. 3.
In step S12, a fluorescence image F of the occlusal surface of the tooth Img Processing to obtain tooth region and contour information Edge 1 。
In this embodiment, dental fluorescence image F Img And (3) performing treatment: fluorescence image F of teeth Img Processing is carried out to obtain tooth area and contour information. The method comprises the following steps:
s121, three-dimensional model P of teeth s Corresponding fluorescence data is input into the fluorescence image F Img And to the fluorescence image F of the occlusal surface of the tooth Img Processing;
the plaque fluorescent image is subjected to mean value filtering by adopting a pretreatment method, and because the plaque fluorescent image is acquired in a dark environment in an oral cavity, a lot of noise exists in the plaque fluorescent image, the noise can be effectively filtered by adopting the mean value filtering, the accuracy of extracting a dental region from the whole image is improved, and the mean value filtering result is shown in fig. 4.
S122, dividing the tooth area from other areas by adopting an automatic threshold segmentation method to obtain tooth area and contour information Edge 1 。
The automatic threshold segmentation method is a common image segmentation method, has simple implementation principle, small calculated amount and stable performance, and is widely applied to the field of image segmentation. Assuming that the image f (x, y) consists of a dark background and a brighter target object, the gray values between the two are significantly different, so a threshold T can be chosen, and the target object is separated from the background by the following formula, the newly generated image is g (x, y)
After thresholding, the background portion of the image is set to black, and the target portion retains its own color. In the image thresholding operation process, the selection of the gray threshold T is very important, the proper T value effectively removes irrelevant information, and in the dental fluorescent image, the brightness of a plurality of fluorescent images has great difference due to the complexity of the acquisition environment, so that an algorithm capable of determining the threshold T according to the information of the image is needed to be adopted to realize the accurate extraction of the dental region, and the embodiment adopts a global automatic threshold segmentation method based on OTSU.
The principle is that the image is divided into a background part and a foreground part according to the gray level characteristics 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;
setting the total pixel number of an image as N and the pixel gray level range as 0-L-1]The number of pixels with a gray value i is n i The probability of occurrence of a pixel having a gray value i is:
setting the segmentation threshold value as T 0 Dividing the gray value of the whole image into C 0 And C 1 Two classes, C 0 The gray value of (C) is distributed in [ C ] 0 ～T 0 -1],C 1 The corresponding gray value is [ T ] 0 ～L-1]Between C 0 And C 1 Probability w of (2) 0 And w 1 The method comprises the following steps of:
C 1 and C 1 Mean value u of (2) 0 ，u 1 The method comprises the following steps:
the gray average value of the whole image is as follows:
μ＝ω 0 μ 0 +ω 1 μ 1
defining two gray level partition inter-class variances as:
σ 2 ＝ω 0 (μ 0 -μ) 2 +ω 1 (μ 1 -μ) 2
at the determination of the segmentation threshold T 0 At the time, let T 0 Starting from 0, sequentially increasing to L-1 with step 1, recording each time T 0 Corresponding inter-class variance sigma 2 ，σ 2 Maximum T 0 I.e. the optimal segmentation threshold. By using the method, the tooth fluorescence image is automatically segmented, and the segmentation result is shown in fig. 5.
The extraction method of the tooth contour edge points comprises traversing one pixel, judging whether eight adjacent pixels simultaneously contain tooth region points and non-tooth region points, if so, judging the contour boundary points, and if not, judging the contour boundary points. Obtaining tooth Edge corresponding to the fluorescence image 1 Wherein Edge is 1 The data structure contained therein is { { { I x1 ,I y1 },{I x2 ,I y2 },...,{I xi ,I yi }, where { I } xi ,I yi And the image coordinates corresponding to the ith edge point of the fluorescence image. The extracted contour structure is shown in fig. 6.
In step S13, three-dimensional model point cloud data P for the tooth surface s Posture correction is carried out to obtain a corrected tooth three-dimensional model P s 。
Inputting tooth three-dimensional point cloud data P s Calculating the center of gravity and the main direction of tooth point cloud data, correcting the posture of the tooth three-dimensional point cloud data by utilizing a three-dimensional model space transformation formula, enabling the origin of a space coordinate system to be at the center of gravity of the tooth point cloud data model, enabling a tooth engagement surface to be parallel to an XOY plane of the space coordinate system and perpendicular to a Z axis of the space coordinate system, and obtaining a corrected tooth three-dimensional model point set as a new P s The method comprises the steps of carrying out a first treatment on the surface of the The corrected point cloud model is shown in fig. 7.
The posture correction of the tooth model Ps adopts a principal component analysis method of PCA, calculates the principal direction of the point cloud, namely the Z-axis direction, and converts 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 as follows
Wherein the R, T matrix is determined using PCA.
In step S14, a corrected three-dimensional model of the tooth P is created s Fluorescence image F of occlusal surface of tooth Img Is to be corrected into a three-dimensional tooth model P s Mapping of points on to fluorescence image F Img And (3) upper part.
In the present embodiment, a three-dimensional model of teeth P is created s Fluorescence image F of plaque Img Mapping model of (2) three-dimensional tooth model P s Mapping of points on to plaque fluorescence image F Img And (3) upper part. The method specifically comprises calculating imaging image of three-dimensional tooth model according to camera imaging model, comparing imaging image contour with fluorescent image contour, updating projection matrix to realize identical tooth imaging contour and fluorescent image contour, and realizing three-dimensional tooth model P s The points on the map onto the fluorescence image. The method comprises the following steps:
s141, calibrating an oral cavity fluorescence imager for acquiring fluorescence images of occlusal surfaces of teeth to obtain an internal reference matrix M of the oral cavity fluorescence imager 1 ；
Calibrating the intraoral fluorescence imaging device by adopting a Zhang Zhengyou calibration method to obtain a camera internal reference matrix M 1 The calibration plate image acquired by camera calibration is shown in fig. 8. Its camera internal reference matrix M 1 The method comprises the following steps:
and (5) taking a plurality of chessboard calibration plate pictures by using an oral cavity fluorescent imager by adopting a Zhang Zhengyou calibration method, calibrating the imaging device, and obtaining a camera imaging internal reference matrix.
S142, inputting the corrected tooth three-dimensional model P s Fluorescence image F of tooth Img Contour information Edge of (a) 1 Internal reference matrix M 1 And initialize the extrinsic matrix M 2 Setting a matching distance threshold T hs ；
Inputting corrected tooth three-dimensional model P s Fluorescence image F of the corresponding tooth Img Contour data Edge 1 Camera internal reference matrix M 1 The method comprises the steps of carrying out a first treatment on the surface of the Initializing an extrinsic matrix M 2 Setting a matching distance threshold T hs 。
The initialization projection matrix adopts an empirical value, and the external parameter matrix M can be initialized because the acquisition angle of the projection matrix is vertical to the occlusal surface of the teeth and is about 1 cm to 2cm away from the surface of the teeth when the fluorescence image of the teeth is acquired 2 Is that
S143, utilizing the external parameter matrix M 2 Internal reference matrix M 1 Computing an imaging image TPD of a three-dimensional model of a tooth under a virtual imaging system img ；
Obtaining a three-dimensional tooth model by using an imaging relation model of an imaging system, and imaging the three-dimensional tooth model in a fluorescence imaging system, wherein the imaging model is as follows
Wherein a is x ＝c/d x Is the scale factor on the u-axis, also known as the equivalent (normalized) focal length on the u-axis; a, a y ＝c/d y Is the scale factor on the v-axis, also known as the equivalent (normalized) focal length on the v-axis; m is a 3×4 matrix, called projection matrix, M 1 By camera parameter a x ,a y ,u 0 ,v 0 Co-determination, called reference matrix, M 2 Is determined by the camera's external parameters relative to the world coordinate system, called the external parameter matrix.
According to the camera perspective imaging matrix, the tooth point cloud data in M can be obtained 1 And M 2 In the projection matrix, only the outline is used as the characteristic value, so that gray images can be generated without calculating the distance value between each point and the coordinates of the viewpoint camera, thereby reducing the speed of a large amount of calculation and accelerating the algorithm, and therefore, another simple method is adopted to acquire a black-and-white image projected by the point cloud, the point is set to be white when the corresponding projected three-dimensional point exists on the two-dimensional image point, the other points are set to be black, and after the operation, a silhouette image of the tooth three-dimensional model is obtainedThe projected image of which is shown in fig. 9.
S144, performing imaging image TPD under the virtual imaging system img Preprocessing and calculating an imaging image TPD under a virtual imaging system img Obtain projected Edge of tooth three-dimensional model 2 ；
For imaging image TPD img Preprocessing and calculating an imaging image TPD img Obtain projected Edge of tooth three-dimensional model 2 . Wherein Edge is 2 The data structure contained in the data structure is { { { { I x1 ,I y1 },{X p1 ,Y p1 ,Z p1 }},{{I x2 ,I y2 },{X p2 ,Y p2 ,Z p2 }},...,{{I xj ,I yj },{X pj ,Y pj ,Z pj } }, wherein { I } xj ,I yj The j-th edge point corresponds to the image coordinate, { X }, and pj ,Y pj ,Z pj and the j-th projection edge point corresponds to the three-dimensional space point.
Because the cloud point cloud of the tooth points is sparse and the resolution ratio of the fluorescent image is large, the obtained dental model silhouette image forms one point, the image needs to be operated for accurately extracting the outline, the closed operation in the image morphological processing is selected, the image is expanded to fill small gaps, then the original edge is restored to the original shape by using corrosion with the same size, and therefore, the complete and void-free dental model white silhouette can be obtained, and the image is shown in fig. 10.
When the projection contour is obtained, the same operation of taking edges from the fluorescent image is adopted, so that the projection image contour point and the three-dimensional space point corresponding to the contour point are obtained, and all data are stored. The outline image is shown in fig. 11.
S145, calculating a projection Edge of the obtained tooth three-dimensional model 2 Contour information Edge from each two-dimensional coordinate point to tooth fluorescence image 1 The point with the nearest Euclidean distance in each two-dimensional coordinate point is stored with the corresponding relation and the distance information;
in the present embodiment, edge is calculated 2 Each two-dimensional coordinate point { I } xj ,I yj } to Edge 1 Each two-dimensional coordinate point { I } xi ,I yi And storing the corresponding relation and the distance information of the point with the nearest Euclidean distance in the step.
S146, obtaining a projection Edge of the tooth three-dimensional model 2 Two-dimensional point coordinates and corresponding three-dimensional model P s Corresponding points on the graph, and calculating the average value T of the distances of all the corresponding points avg The method comprises the steps of carrying out a first treatment on the surface of the Judging the average value T avg Whether or not it is smaller than a set matching distance threshold T hs If yes, outputting a current three-dimensional projection matrix M 2 As a registration matrix between the fluoroscopic image and the three-dimensional model data; if not, updating the external parameter matrix M by using a three-dimensional projection matrix equation 2 And proceeds to step S143;
deleting Edge of distance information according to 3 sigma principle 2 The wild points in the list are selected from Edge if the remaining paired points are too many 2 A part of points are randomly selected to form a pairing set U of about 200 points, wherein the set U comprises two-dimensional point coordinates { I ] of a fluorescent image xi ,I yi ' and corresponding three-dimensional P s Corresponding point { X over pj ,Y pj ,Z pj }. Calculating the average value T of all the corresponding point distances avg If T avg <T hs Then the current projection matrix M is output 2 As a registration matrix between the fluoroscopic image 2D and the three-dimensional model 3D data. Otherwise, updating the projection matrix M by using a three-dimensional projection matrix equation 2 Then, the process returns to step S143 to continue the iteration.
Updating the camera external parameter matrix M by using the obtained corresponding point information and an imaging model and adopting a least square method 2 . The contour updating process is shown in fig. 12. Final M 2 The matrix is
In the iterative process, the average value reduction process of all the point distances is shown in fig. 13.
According to the final resultThe obtained external parameter projection matrix M 2 And a fluorescence camera reference matrix M 1 The three-dimensional projection matrix can be utilized to realize the three-dimensional tooth model P s A certain point on the map is projected onto the fluorescent image, and the mapping result is shown in fig. 14.
S147, according to the finally obtained external parameter matrix M 2 And an internal reference matrix M 1 Using the three-dimensional projection matrix to model the three-dimensional teeth P s The above points are projected onto the fluoroscopic image.
In the present embodiment, the projection matrix M is obtained based on the final result 2 And a fluorescence camera reference matrix M 1 The three-dimensional projection matrix can be utilized to realize the three-dimensional tooth model P s A point on the image is projected onto the fluoroscopic image.
Compared with the prior art, the beneficial effects of the embodiment are as follows:
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 fluorescent image and a dental three-dimensional model, which lays a foundation for researching the relationship between the dental three-dimensional form and caries.
3. According to the method for mapping the dental plaque fluorescent image and the tooth three-dimensional model, a simple mode of initializing a projection matrix is adopted according to priori knowledge, so that a large amount of calculation quantity is reduced, and meanwhile, the algorithm is simpler and more efficient.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (8)
1. A method of registering a dental plaque fluoroscopic image with a three-dimensional model of a tooth, comprising:
s1, acquiring three-dimensional model point cloud data of a tooth surfaceP s Fluorescent image of occlusal surface of toothF Img ；
S2, performing fluorescence image on the occlusal surface of the toothF Img Processing to obtain tooth region and contour informationEdge 1 ；
S3, carrying out point cloud data on the three-dimensional model of the tooth surfaceP s Posture correction is carried out to obtain a corrected tooth three-dimensional modelP s ；
S4, establishing a corrected tooth three-dimensional modelP s Fluorescent image of occlusal surface of toothF Img Mapping model of (2) to model the three-dimensional teeth after correctionP s Mapping of points on to fluorescence imagesF Img Applying;
the step S2 specifically comprises the following steps:
s21, three-dimensional modeling of teethP s Corresponding fluorescence data is input into the fluorescence imageF Img And to the fluorescence image of the occlusal surface of the toothF Img Processing;
s22, dividing the tooth area from other areas by adopting an automatic threshold segmentation method to obtain tooth area and contour informationEdge 1 ；
The step S4 specifically comprises the following steps:
s41, calibrating an oral cavity fluorescence imager for acquiring fluorescence images of occlusal surfaces of teeth to obtain an internal reference matrix of the oral cavity fluorescence imagerM 1 ；
S42, inputting the corrected tooth three-dimensional modelP s Fluorescence image of teethF Img Profile information of (a)Edge 1 Internal reference matrixM 1 And initializing the extrinsic matrixM 2 Setting a matching distance thresholdT hs ；
S43, utilizing the external parameter matrixM 2 Internal reference matrixM 1 Computing the formation of a three-dimensional model of a tooth under a virtual imaging systemImageTPD img ；
S44, imaging images under the virtual imaging systemTPD img Preprocessing and calculating an imaging image under a virtual imaging systemTPD img Obtain projected edge of tooth three-dimensional modelEdge 2 ；
S45, calculating projection edges of the obtained tooth three-dimensional modelEdge 2 Contour information from each two-dimensional coordinate point to tooth fluorescence imageEdge 1 The point with the nearest Euclidean distance in each two-dimensional coordinate point is stored with the corresponding relation and the distance information;
s46, obtaining projection edges of tooth three-dimensional modelsEdge 2 Two-dimensional point coordinates and corresponding three-dimensional modelP s Corresponding points on the graph, and calculating the average value of the distances of all the corresponding pointsT avg The method comprises the steps of carrying out a first treatment on the surface of the Determining the average valueT avg Whether or not it is smaller than a set matching distance thresholdT hs If yes, outputting the current three-dimensional projection matrixM 2 As a registration matrix between the fluoroscopic image and the three-dimensional model data; if not, updating the external parameter matrix by using a three-dimensional projection matrix equationM 2 And continues to step S43;
s47, according to the finally obtained external parameter matrixM 2 And an internal reference matrixM 1 Modeling three-dimensional teeth using a three-dimensional projection matrixP s The above points are projected onto the fluoroscopic image.
2. The method of registration of dental plaque fluoroscopic images with a three-dimensional model of teeth according to claim 1, wherein step S1 comprises:
s11, acquiring three-dimensional data of dentition through an oral cavity three-dimensional scanner to obtain a three-dimensional model of the tooth surfaceP s ；
S12, acquiring plaque fluorescence images of occlusal surfaces of teeth through an oral cavity fluorescence imagerF Img 。
3. The method of registration of a dental plaque fluoroscopic image with a three-dimensional model of a tooth according to claim 1, wherein the fluoroscopic image of the occlusal surface of the tooth in step S21F Img The processing method for processing is mean filtering.
4. The method of claim 1, wherein the automatic thresholding method used in step S22 is the global automatic thresholding method of the OTSU.
5. The method according to claim 1, wherein step S3 is specifically performed on three-dimensional model point cloud data of teeth through a three-dimensional model space transformation formulaP s Posture correction is performed.
6. The method of registration of dental plaque fluoroscopic images with a three-dimensional model of teeth according to claim 5, wherein in step S3, the three-dimensional model point cloud data is acquiredP s Posture correction is performed by a method of PCA principal component analysis.
7. The method according to claim 1, wherein step S44 further comprises obtaining a contour in the three-dimensional model of the tooth, and 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.
8. The method according to claim 1, wherein the step S46 is performed by updating the extrinsic matrix M by using least square method based on the obtained corresponding point information and the imaging model 2 。
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