Tooth development assessment method and device

Abstract

The invention discloses a tooth development assessment method and device. The method comprises the following steps: acquiring a tooth point cloud data set and a tooth normal point cloud data set; the tooth point cloud data set comprises tooth point cloud data; the tooth normal point cloud data set comprises normally developed tooth point cloud data collected in advance; preprocessing the tooth point cloud data set to obtain a preprocessed data set; and performing evaluation processing on the preprocessed data set and the tooth normal point cloud data set to obtain a tooth development evaluation value. According to the invention, based on the three-dimensional image data of the teeth, rapid and accurate evaluation of tooth development of the user is realized.

Description

Technical Field

[0001] The present invention relates to the field of medical information processing, and in particular to a tooth development assessment method and device. Background Art

[0002] At present, the evaluation of tooth development, especially that of adolescents, is mainly conducted through observation based on experience. This method has problems such as incomplete observation and easy error due to subjective influence.

[0003] In the past decade, oral imaging has developed rapidly, and digital imaging technology plays an important role in the diagnosis and treatment of periodontal disease and peri-implant diseases. With the help of computer imaging technology, such as X-rays, computed tomography (CT), and laser scanning, three-dimensional image data of teeth can be obtained. How to achieve a rapid and accurate assessment of the user's tooth development based on three-dimensional image data of teeth is an urgent problem to be solved. Summary of the invention

[0004] With regard to how to achieve rapid and accurate evaluation of a user's tooth development based on three-dimensional image data of teeth, the present invention discloses a tooth development evaluation method and device.

[0005] In a first aspect of the embodiments of the present application, a method for evaluating tooth development is disclosed, comprising:

[0006] S1, obtaining a tooth point cloud data set and a normal tooth point cloud data set; the tooth point cloud data set includes tooth point cloud data; the normal tooth point cloud data set includes pre-collected normally developed tooth point cloud data;

[0007] S2, preprocessing the tooth point cloud dataset to obtain a preprocessed dataset;

[0008] S3, evaluating and processing the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value.

[0009] The preprocessing of the tooth point cloud data set to obtain a preprocessed data set includes:

[0010] S21, performing data cleaning processing on the tooth point cloud data set to obtain a cleaned data set;

[0011] S22, performing anomaly detection processing on the cleaned data set to obtain a first data set;

[0012] S23, performing point cloud registration processing on the first data set to obtain a preprocessed data set.

[0013] The step of evaluating the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value includes:

[0014] S31, representing the preprocessed data set as a vector to be evaluated a; the elements of the vector to be evaluated are tooth scanning data;

[0015] S32, representing the normal tooth point cloud data set as a standard vector b; the elements of the standard vector are pre-collected normal tooth point cloud data; the dimension of the standard vector is the same as the dimension of the vector to be evaluated;

[0016] S33, performing evaluation processing on the standard vector and the vector to be evaluated to obtain a tooth development evaluation value.

[0017] The step of evaluating the standard vector and the vector to be evaluated to obtain a tooth development evaluation value includes:

[0018] The standard vector and the vector to be evaluated are processed to obtain the absolute value of the difference to obtain a difference vector c, c = |ab|;

[0019] Performing joint feature change processing on the difference vector c, the standard vector b and the vector to be evaluated a to obtain a feature signal set;

[0020] Performing weighted sum processing on the characteristic signal set to obtain a difference characteristic signal;

[0021] The difference characteristic signal is subjected to evaluation feature extraction processing to obtain a tooth development evaluation value.

[0022] The characteristic signal set includes first to tenth characteristic signals;

[0023] The calculation expression of the joint feature change processing is:

[0024] ac 22 (n)=Ga(a 2 (n)c 2 (n)), ac 26 (n)=Ga(a 2 (n)c 6 (n)), ac 12 (n) = Ga (a (n) c 2 (n)),

[0025] ac 13 (n) = Ga (a (n) c 3 (n)), ac 32 (n)=Ga(a 3 (n)c 2 (n)), bc 12 (n) = FFT(b 1 (n)c 2 (n)),

[0026] bc 16 (n) = FFT(b 1 (n)c 6 (n)), bc 22 (n) = FFT(b 2 (n)c 2 (n)), bc 35 (n) = FFT(b 3 (n)c 5 (n)),

[0027] bc 24 (n) = FFT(b 2 (n)c 4 (n)),

[0028] Wherein, a(n), b(n), c(n) represent the nth item of the vector to be evaluated a, the standard vector b and the difference vector c respectively, Ga() and FFT() represent Gabor change and FFT transformation respectively, ac 22 (n), ac 26 (n), ac 12 (n), ac 13 (n), ac 32 (n), bc 12 (n), bc 16 (n), bc 22 (n), bc 35 (n), bc 24 (n) represents the nth item of the first to tenth characteristic signals respectively;

[0029] The calculation expression of the weighted summation process is:

[0030]

[0031] Among them, p(n) represents the nth item of the difference characteristic signal, and θ1, θ2, θ3, θ4, and θ5 represent the preset first to fifth weighting coefficients respectively.

[0032] The calculation expression of the evaluation feature extraction process includes:

[0033]

[0034] Among them, g is the tooth development assessment value, f n is the nth item of the calculated intermediate vector, and L is the length of the difference characteristic signal.

[0035] In a second aspect of the embodiment of the present application, a tooth development assessment device is disclosed, the device comprising:

[0036] A memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the tooth development assessment method.

[0039] In a third aspect of the embodiments of the present application, a computer storable medium is disclosed, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the tooth development assessment method.

[0040] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the tooth development assessment method.

[0041] The beneficial effects of the present invention are:

[0042] The present invention obtains a difference vector c by performing absolute value processing on the difference between the standard vector and the vector to be evaluated, thereby realizing the construction of a difference value; in order to obtain a difference feature from the difference value, a feature information set is constructed using the difference value; by establishing a weighted summation processing process, the extraction of a difference feature signal in the feature information set that can reflect the tooth development evaluation process is realized, and further, the constructed evaluation feature extraction processing is used to obtain a tooth development evaluation value, thereby realizing a rapid and accurate evaluation of the user's tooth development.

[0043] The present invention realizes effective noise and interference suppression of tooth data through an effective preprocessing process of tooth three-dimensional image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0045] In order to better understand the content of the present invention, an embodiment is given here.

[0046] Figure 1 It is a flow chart for implementing the method of the present invention.

[0047] In a first aspect of the embodiments of the present application, a method for evaluating tooth development is disclosed, comprising:

[0048] S1, obtaining a tooth point cloud data set and a normal tooth point cloud data set; the tooth point cloud data set includes tooth point cloud data; the normal tooth point cloud data set includes pre-collected normally developed tooth point cloud data;

[0049] S2, preprocessing the tooth point cloud dataset to obtain a preprocessed dataset;

[0050] S3, evaluating and processing the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value;

[0051] The preprocessing of the tooth point cloud data set to obtain a preprocessed data set includes:

[0052] S21, performing data cleaning processing on the tooth point cloud data set to obtain a cleaned data set;

[0053] S22, performing anomaly detection processing on the cleaned data set to obtain a first data set;

[0054] S23, performing point cloud registration processing on the first data set to obtain a preprocessed data set;

[0055] The step of evaluating the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value includes:

[0056] S31, representing the preprocessed data set as a vector to be evaluated a; the elements of the vector to be evaluated are tooth scanning data;

[0057] S32, representing the normal tooth point cloud data set as a standard vector b; the elements of the standard vector are pre-collected normal tooth point cloud data; the dimension of the standard vector is the same as the dimension of the vector to be evaluated;

[0058] S33, evaluating the standard vector and the vector to be evaluated to obtain a tooth development evaluation value.

[0059] The step of evaluating the standard vector and the vector to be evaluated to obtain a tooth development evaluation value includes:

[0060] The standard vector and the vector to be evaluated are subjected to absolute value difference processing to obtain a difference vector c, c=|ab|;

[0061] Performing joint feature change processing on the difference vector c, the standard vector b and the vector to be evaluated a to obtain a feature signal set;

[0062] Performing weighted sum processing on the characteristic signal set to obtain a difference characteristic signal;

[0063] The difference characteristic signal is subjected to evaluation feature extraction processing to obtain a tooth development evaluation value.

[0064] The characteristic signal set includes first to tenth characteristic signals;

[0065] The calculation expression of the joint feature change processing is:

[0066] ac 22 (n)=Ga(a 2 (n)c 2 (n)), ac 26 (n)=Ga(a 2 (n)c 6 (n)), ac 12 (n) = Ga (a (n) c 2 (n)),

[0067] ac 13 (n) = Ga (a (n) c 3 (n)), ac 32 (n)=Ga(a 3 (n)c 2 (n)), bc 12 (n) = FFT(b 1 (n)c 2 (n)),

[0068] bc 16 (n) = FFT(b 1 (n)c 6 (n)), bc 22 (n) = FFT(b 2 (n)c 2 (n)), bc 35 (n) = FFT(b 3 (n)c 5 (n)),

[0069] bc 24 (n) = FFT(b 2 (n)c 4 (n)),

[0070] Wherein, a(n), b(n), c(n) represent the nth item of the vector to be evaluated a, the standard vector b and the difference vector c respectively, Ga() and FFT() represent Gabor change and FFT transformation respectively, ac 22 (n), ac 26 (n), ac 12 (n), ac 13 (n), ac 32 (n), bc 12 (n), bc 16 (n), bc 22 (n), bc 35 (n), bc 24 (n) represents the nth item of the first to tenth characteristic signals respectively;

[0071] The calculation expression of the weighted summation process is:

[0072]

[0073] Among them, p(n) represents the nth item of the difference characteristic signal, and θ1, θ2, θ3, θ4, and θ5 represent the preset first to fifth weighting coefficients respectively.

[0074] The step of performing evaluation feature extraction processing on the difference feature signal to obtain a tooth development evaluation value includes:

[0075]

[0076] Among them, g is the tooth development assessment value, f n is the nth item of the calculated intermediate vector, and L is the length of the difference characteristic signal.

[0077] The performing anomaly detection processing on the denoised data set to obtain a first data set includes:

[0078] S221, for each data attribute of the denoised data set, taking the data collection information of the data as the independent variable and the data value of the data as the dependent variable, a cluster analysis process is performed to obtain clustering result information of the data attribute of the class; the clustering result information includes the cluster category to which all data of the data attribute of the class belong;

[0079] S222, determining the number of data included in each cluster category; setting a data volume threshold; deleting the data included in the cluster category whose data number is less than the data volume threshold from the denoised data set;

[0080] S223, merging all data of the denoised data set after executing S221 and S222 to obtain a first data set;

[0081] The data collection information includes time information or space information corresponding to the data collection;

[0082] The point cloud registration process can be implemented by using the FastMAC algorithm or the RegFormer algorithm.

[0083] The preset first to fifth weighting coefficients have a value range of [0.01, 0.2].

[0084] The tooth development evaluation value is used to evaluate the normality of the user's tooth development. The higher the value, the more normal the development.

[0085] The data cleaning process includes noise reduction, filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0086] The noise reduction process can be implemented by using the DeNoise command in MATLAB.

[0087] In a second aspect of the embodiment of the present application, a tooth development assessment device is disclosed, the device comprising:

[0088] A memory storing executable program code;

[0089] a processor coupled to the memory;

[0090] The processor calls the executable program code stored in the memory to execute the tooth development assessment method.

[0091] In a third aspect of the embodiments of the present application, a computer storable medium is disclosed, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the tooth development assessment method.

[0092] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the tooth development assessment method.

[0093] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating tooth development, characterized in that: include: S1, obtaining a tooth point cloud dataset and a normal tooth point cloud dataset; The tooth point cloud data set includes tooth point cloud data; The normal tooth point cloud data set includes pre-collected normally developed tooth point cloud data; S2, preprocessing the tooth point cloud dataset to obtain a preprocessed dataset; S3, evaluating and processing the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value.

2. The tooth development assessment method according to claim 1, characterized in that: The preprocessing of the tooth point cloud data set to obtain a preprocessed data set includes: S21, performing data cleaning processing on the tooth point cloud data set to obtain a cleaned data set; S22, performing anomaly detection processing on the cleaned data set to obtain a first data set; S23, performing point cloud registration processing on the first data set to obtain a preprocessed data set.

3. The tooth development assessment method according to claim 1, characterized in that: The step of evaluating the preprocessed data set and the normal tooth point cloud data set to obtain a tooth development evaluation value includes: S31, representing the preprocessed data set as a vector to be evaluated a; the elements of the vector to be evaluated are tooth scanning data; S32, representing the normal tooth point cloud data set as a standard vector b; the elements of the standard vector are pre-collected normal tooth point cloud data; the dimension of the standard vector is the same as the dimension of the vector to be evaluated; S33, evaluating the standard vector and the vector to be evaluated to obtain a tooth development evaluation value.

4. The tooth development assessment method according to claim 3, characterized in that: The step of evaluating the standard vector and the vector to be evaluated to obtain a tooth development evaluation value includes: The standard vector and the vector to be evaluated are subjected to absolute value difference processing to obtain a difference vector c, c=|ab|; Performing joint feature change processing on the difference vector c, the standard vector b and the vector to be evaluated a to obtain a feature signal set; Performing weighted sum processing on the characteristic signal set to obtain a difference characteristic signal; The difference characteristic signal is subjected to evaluation feature extraction processing to obtain a tooth development evaluation value.

5. The tooth development assessment method according to claim 4, characterized in that: The characteristic signal set includes first to tenth characteristic signals; The calculation expression of the joint feature change processing is: and 22 (n)=Ga(a 2 (n)c 2 (n)), and 26 (n)=Ga(a 2 (n)c 6 (n)), and 12 (n)=Ga(a(n)c 2 (n)), ac 13 (n)=Ga(a(n)c 3 (n)),ac 32 (n)=Ga(a 3 (n)c 2 (n)),bc 12 (n)=FFT(b 1 (n)c 2 (n)), bc 16 (n)=FFT(b 1 (n)c 6 (n)),bc 22 (n)=FFT(b 2 (n)c 2 (n)),bc 35 (n)=FFT(b 3 (n)c 5 (n)), bc 24 (n)=FFT(b 2 (n)c 4 (n)),, Wherein, a(n), b(n), c(n) represent the nth item of the vector to be evaluated a, the standard vector b and the difference vector c respectively, Ga() and FFT() represent Gabor change and FFT transformation respectively, ac 22 (n), ac 26 (n), ac 12 (n), ac 13 (n), ac 32 (n), bc 12 (n), bc 16 (n), bc 22 (n), bc 35 (n), bc 24 (n) represents the nth item of the first to tenth characteristic signals respectively.

6. The tooth development assessment method according to claim 1, characterized in that: The calculation expression of the weighted summation process is: Among them, p(n) represents the nth item of the difference characteristic signal, and θ1, θ2, θ3, θ4, and θ5 represent the preset first to fifth weighting coefficients respectively.

7. The tooth development assessment method according to claim 1, characterized in that: The calculation expression of the evaluation feature extraction process includes: Among them, g is the tooth development assessment value, f n is the nth item of the calculated intermediate vector, and L is the length of the difference characteristic signal.

8. A tooth development assessment device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the tooth development assessment method according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the tooth development assessment method according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the tooth development assessment method according to any one of claims 1 to 7.

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