CN115191949A - Dental disease diagnosis method and diagnosis instrument - Google Patents
Dental disease diagnosis method and diagnosis instrument Download PDFInfo
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Abstract
A dental disease diagnosis method belongs to the technical field of image processing. The method comprises the steps of 1, obtaining a tooth area image to be detected; step 2, carrying out noise reduction treatment on the collected tooth area image; 3, performing 3D modeling on the tooth region image subjected to noise reduction treatment in the step 3 to construct a tooth lesion three-dimensional model; step 4, constructing a deep convolutional neural network classifier by using the 3D model constructed in the step 3, putting the acquired dental disease image of the patient into the classifier for identification, and judging the symptom of the processed image corresponding to the dental disease; and 5, performing health assessment according to the diagnosis result, and performing medical advice matching on the disconnected images through the database to correspond to the symptoms of the dental diseases. By the diagnosis method, the user can carry out real-time tooth health diagnosis on the teeth of the user at any time in daily life, the effect of early treating and preventing the dental diseases is realized, and the probability of the dental diseases of the user is reduced.
Description
Technical Field
The invention relates to a dental disease diagnosis method, and belongs to the technical field of image processing.
Background
The people eat as the day, and the tooth health is a critical ring for human health.
The third national oral health survey data shows that more than 90% of people in China have the problems of irregular teeth, missing teeth, decayed teeth, looseness and the like. As for caries, the caries rates of people aged 5 years, 12 years, 35 to 44 years and 65 to 74 years in China are 66 percent, 28.9 percent, 88.1 percent and 98.4 percent respectively. It is seen that the dental diseases cause great harm to national health.
According to investigation, the treatment cost of the dental diseases is generally higher, the treatment process is generally more painful, and the harm of the dental diseases is further aggravated. The obvious symptoms of the dental diseases mostly appear in the middle or late stage of the dental diseases, so that most of people suffering from the dental diseases can detect the dental diseases and seek treatment in the middle or late stage of the dental diseases, and the optimal treatment period is delayed.
At present, the traditional tooth health detection has two main modes: the first is manual examination by dentist, the second is shooting X-ray, and the dentist carries out analysis and diagnosis. The two modes have very complicated operation process and higher diagnosis cost, and the X-ray causes certain damage to human bodies. The hospital equipment is expensive, the operation technology difficulty is high, the operation is complicated, and daily use cannot be realized. These factors greatly limit the development of the dental health career in our country.
In order to solve the above problems, (1) patent No. CN112213134A discloses an acoustic-based oral cleaning quality detection system and method for an electric toothbrush, which construct a wearable device using a low-cost throat microphone and a bluetooth headset based on an acoustic principle, and apply a tooth brushing behavior recognition model proposed by the method to a general electric toothbrush on the market by analyzing its audio characteristics, so as to detect the tooth brushing quality of an application in real time and establish a long-term oral health file.
(2) Patent No. CN212522064U discloses a household dental health detector, which uses a camera with a resolution of 1280x960 and a sensor pixel of more than 100 ten thousand to acquire an image, and feeds the image back to a user through an APP for remote health diagnosis.
(3) Patent No. CN112914484A discloses an intelligent dental endoscopic detection device, which can intelligently detect collected dental photographs by using computer vision and deep learning algorithm, identify dental caries and generate a dental health report. The user can screen the decayed tooth at any time and any place without the presence of a professional doctor, thereby reducing the difficulty and the cost of screening the decayed tooth.
However, these techniques suffer from the following disadvantages: (1) the image acquisition precision is not high; (2) The disease cannot be identified or is single, the health screening can be only carried out on the disease of decayed teeth, the type of the identified disease is single, the types of the Chinese people suffering from the dental diseases are more, the technical effect value is very limited, and the comprehensive popularization is not suitable; and (3) the visualization degree is low. Other technologies only present real-time images, so that a user cannot accurately judge the position of a disease, the visualization degree is low, and the user experience is poor.
Therefore, it is necessary to develop a portable dental diagnosis apparatus and a diagnosis method which can be used daily. The user can carry out real-time tooth health diagnosis on the teeth of the user at any time.
Disclosure of Invention
The present invention is directed to solving the problems that the existing dental disease diagnosis method diagnoses a single dental disease, only diagnoses one dental disease, and a user cannot accurately judge the location of a disease, resulting in failure of reference meaning of diagnosis, and a brief summary of the present invention is provided below to provide a basic understanding of some aspects of the present invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to determine the key or critical elements of the present invention, nor is it intended to limit the scope of the present invention.
The technical scheme of the invention is as follows:
a dental disease diagnostic method comprising the steps of:
step 2, carrying out noise reduction treatment on the collected tooth area image, wherein the specific method comprises the following steps: firstly, carrying out noise reduction processing on an image by using opencv, firstly, importing acquired image data of a tooth area, carrying out rapid noise reduction on the image by using a fastNlMenas denoisingColored function, secondly, expanding the data volume by using an image overturning method of opencv, removing useless parts (removing the characteristics irrelevant to tooth lesions in the image) according to an image slicing method, changing parameter values according to the difference of data to obtain an optimal part, and finally, storing the processed image data;
3, performing 3D modeling on the image subjected to noise reduction processing in the step 3 to construct a three-dimensional model of dental lesion;
step 31, transmitting the image data picture data set processed in the step 2 into RealityCapture software;
and step 32, solving a plurality of pairs of matching points through a sift algorithm or a surf algorithm, then obtaining an initial internal reference matrix K of the camera through calibration or from image information,
step 33, based on the internal reference matrix K and a plurality of pairs of matching points, solving a basic matrix F through a normalization eight-point method and a matching (Randac) mode for removing error points, and solving an essential matrix E (the essential matrix is a special case of the basic matrix and is the basic matrix under the normalized image coordinates) based on the basic matrix F and the internal reference matrix K;
step 34, carrying out triangulation on the matching pairs based on multiple visual angles and camera parameters to obtain three-dimensional point coordinates, selecting correct camera postures R and T by using the constraint of the three-dimensional point coordinates in front of a camera, wherein the R and the T are camera external parameters, the R is a 3 multiplied by 3 rotation matrix, the T is a 3 multiplied by 1 displacement vector, and in fact, the [ R | T ] × X (X is the homogeneous coordinate of a 3D point in a world coordinate system) is to transform the 3D point into a camera coordinate system;
step 35, carrying out triangulation based on the matching pairs and the camera parameters, and solving three-dimensional coordinates of all the matching pairs;
step 36, based on the three-dimensional coordinates and the camera parameters obtained in step 34 and the matching pair coordinates obtained in step 35, performing binding adjustment (nonlinear optimization) by using an LM (linear regression) algorithm to obtain more accurate three-dimensional point coordinates and camera internal and external parameters, realizing 3D model construction, rendering the model in Cinema 4D, and exporting the constructed 3D model;
<xnotran> 41. , , , , , 5 y, [1,0,0,0,0], [0,1,0,0,0], [0,0,1,0,0], [0,0,0,1,0], [0,0,0,0,1], X Y, , Y , [1,3*128*128] , X , X Y, X Y , , ; </xnotran>
Step 42, initialization: selecting an activation function Relu, using a He initial value as a weight initial value, and initializing grid parameters; relu performed better than tanh as tested, so we chose He initialization more suitable for Relu;
wherein n is l The number of the first layer neurons;
step 43, the input layer is convoluted and pooled twice, and the intermediate link part uses the RELU function
the size of a tf.nn.conv2d convolution kernel is 3 or 5, the step size is 1, padding = same, the size of an original image is maintained, the step size of tf.nn.max _ pool is 2, and the image characteristics are shrunk;
step 44, regularization, wherein the model often has an overfitting phenomenon, so after the last pooling layer is flattened, dropout is used for randomly removing some nodes (nerve units) to ensure that each element possibly disappears, so that the model does not excessively depend on a certain characteristic to prevent overfitting, and as dental diseases are small in a dental picture, 0.8-0.9 keep _ prob is selected to avoid too many nodes to remove key characteristics;
and step 45, finally, putting the full connection layer, and obtaining the probability through softmax transformation:
wherein p is i For the probability that the current picture belongs to the ith category, there are 5 categories, z i The ith input of the SoftMax layer;
step 46, integrating the model, using adam optimization to perform back propagation to minimize cost, and storing the session and parameters for subsequent data input;
and step 47, extracting frames of the video collected by the camera by opencv every 0.5s, cutting the video into images, and putting the images into a model to predict diseases.
some appropriate suggestions are made in advance according to the treatment modes of related diseases, corresponding suggestions pop up after the diseases are detected, if no suggestions satisfactory to users exist, the hyperlink can be clicked, the url format of hundred-degree search is called, and the corresponding diseases are searched in a skipping mode.
A dental disease diagnostic apparatus comprises a scanner, a processor, a memory for storing a computer program capable of running on the processor, a probe shell, a bracket and a handheld handle, wherein the scanner is connected with the processor, the processor is used for executing the steps of a dental disease diagnostic method when running the computer program, the scanner is integrated in the probe shell, the probe shell is arranged on the bracket through a rotatable bearing, and the lower end of the bracket is connected with the handheld handle.
Preferably: the scanner has 500 ten thousand pixel lenses, a BCM2710A1 chip as a processor and 512MB of RAM as a memory.
The invention has the following beneficial effects:
1. by the diagnosis method, the user can perform real-time tooth health diagnosis on the user's own teeth at any time in daily life. After a user scans the teeth, the diagnostic apparatus can construct a 3D model of the teeth of the user in real time on a small program, comprehensively acquire tooth information of the user, perform intelligent diagnosis by combining a database, provide health condition information of the teeth of the user, types and medical suggestions of possible diseases, realize the effect of early treatment and prevention of the dental diseases and reduce the probability of the dental diseases of the user.
2. The invention carries out noise point removing processing on the image data set, improves the image definition, has higher accuracy of the dental disease matched after the noise reduction processing,
3. the invention can identify the diseases such as tooth cavities, plaque teeth, gingival bleeding, tooth deformity and tartar through the deep neural network, and has various identification types and wide range.
4. The method can set the weight, score the tooth health according to the disease pattern and the severity, automatically search and crawl a diagnosis case according to the disease for comprehensive judgment, and provide maintenance and medical advice for a user.
Drawings
FIG. 1 is a flow chart of a diagnostic method of the present invention;
FIG. 2 is a perspective view of the dental diagnostic apparatus of the present invention;
FIG. 3 is a diagram of a convolutional neural network model;
FIG. 4 is a schematic diagram of the regularization process of step 44, dropout function training with some neural units removed at random.
Detailed Description
In order that the objects, aspects and advantages of the invention will become more apparent, the invention will be described by way of example only, and in connection with the accompanying drawings. It is to be understood that such description is merely illustrative and not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The first specific implementation way is as follows: the present embodiment will be described with reference to fig. 1 to 3, and a dental disease diagnosis method of the present embodiment includes the steps of:
step 2, carrying out noise reduction treatment on the collected tooth area image, wherein the specific method comprises the following steps: firstly, carrying out noise reduction processing on an image by using opencv, firstly, importing acquired image data of a tooth area, carrying out fast noise reduction on the image by using a fastNlMeans DenoisingColored function, secondly, expanding the data volume by using an image overturning method of opencv, removing useless parts (removing characteristics irrelevant to tooth lesions in the image) according to an image slicing method, changing parameter values according to the difference of data to obtain an optimal part, and finally, storing the processed image data;
3, performing 3D modeling on the image subjected to noise reduction processing in the step 3 to construct a three-dimensional model of dental lesion;
step 31, transmitting the image data picture data set processed in the step 2 into RealityCapture software;
step 32, solving a plurality of pairs of matching points through a sift algorithm or a surf algorithm, then obtaining an initial internal reference matrix K of the camera through calibration or from image information,
step 33, based on the internal reference matrix K and a plurality of pairs of matching points, solving a basic matrix F through a normalization eight-point method and a matching (Randac) mode for removing error points, and solving an essential matrix E (the essential matrix is a special case of the basic matrix and is the basic matrix under the normalized image coordinates) based on the basic matrix F and the internal reference matrix K;
step 34, performing triangulation to obtain three-dimensional point coordinates based on matching pairs of multiple viewing angles and camera parameters, selecting correct camera postures R and T by using constraint of the three-dimensional point coordinates in front of a camera, wherein R and T are camera external parameters, R is a 3X 3 rotation matrix, T is a 3X 1 displacement vector, and in fact [ R | T ] × X (X is homogeneous coordinates of 3D points in a world coordinate system) is to transform the 3D points into a camera coordinate system;
step 35, carrying out triangulation based on the matching pairs and the camera parameters, and solving three-dimensional coordinates of all the matching pairs;
step 36, finally, based on the three-dimensional coordinates and the camera parameters obtained in the step 34 and the matching pair coordinates obtained in the step 35, performing binding adjustment (nonlinear optimization) by using an LM (linear matching) algorithm to obtain more accurate three-dimensional point coordinates and camera internal and external parameters, realizing 3D model construction, rendering the model in Cinema 4D, and exporting the model after the 3D model is constructed; the 3D model is constructed, so that an operator can visually observe the tooth lesion condition, and a user can drag the constructed 3D tooth model and rotate the 3D tooth model by a certain angle to observe the lesion position lesion state;
<xnotran> 41. , , , , , 5 y, [1,0,0,0,0], [0,1,0,0,0], [0,0,1,0,0], [0,0,0,1,0], [0,0,0,0,1], X Y, , Y , [1,3*128*128] , X , X Y, X Y , , ; </xnotran>
Step 42, initialization: selecting an activation function Relu, using a He initial value as a weight initial value, and initializing grid parameters; relu performed better than tanh tested, so we chose He initialization more suitable for Relu;
wherein n is l The number of layer I neurons;
step 43, the input layer is convoluted and pooled twice, and the intermediate link part uses the RELU function
the size of a tf.nn.conv2d convolution kernel is 3 or 5, the step size is 1, padding = same, the size of an original image is maintained, the step size of tf.nn.max _ pool is 2, and the image characteristics are shrunk;
step 44, regularization, wherein the model often has an overfitting phenomenon, so after the last pooling layer is flattened, dropout is used for randomly removing some nodes (nerve units) to ensure that each element possibly disappears, so that the model does not excessively depend on a certain characteristic to prevent overfitting, and as dental diseases are small in a tooth picture, 0.8-0.9 keep _ prob is selected to avoid too many nodes to remove key characteristics;
the method includes the steps of traversing nodes of each layer of the neural network, and then setting a keep _ prob (node retention probability) for the neural network of the layer, wherein the probability that the nodes of the layer have the keep _ prob is retained, and the value range of the keep _ prob is between 0 and 1. By setting the retention probability of the node of the layer of the neural network, the neural network is not biased to a certain node (because the node is possibly deleted), so that the weight of each node is not too large, and the overfitting of the neural network is reduced;
and step 45, finally, putting the full connection layer, and obtaining the probability through softmax transformation:
wherein p is i For the probability that the current picture belongs to the ith category, there are 5 categories, z i The ith input of the SoftMax layer;
step 46, integrating the model, using adam optimization to perform back propagation to minimize cost, and storing the session and parameters for subsequent data input;
and step 47, extracting frames of the video collected by the camera by opencv every 0.5s, cutting the video into images, and putting the images into a model to predict diseases.
some appropriate suggestions are made in advance according to the treatment modes of related diseases, corresponding suggestions pop up after the diseases are detected, if no suggestions satisfactory to users exist, the hyperlink can be clicked, the url format of hundred-degree search is called, and the corresponding diseases are searched in a skipping mode.
The second embodiment is as follows: referring to fig. 1 to 3, the dental diagnosis apparatus of the present embodiment includes a scanner 1, a processor, a memory for storing a computer program capable of running on the processor, a probe housing 3, a support 4, and a hand grip 5, wherein the scanner 1 is connected to the processor, the processor is used for executing the steps of the dental diagnosis method according to any one of claims 1 to 4 when running the computer program, the scanner 1 is integrated in the probe housing 3, the probe housing 3 is mounted on the support 4 through a rotatable bearing, and the hand grip 5 is connected to the lower end of the support 4.
The scanner 1 has 500 ten thousand pixel lenses, a BCM2710A1 chip as a processor and 512MB of RAM as a memory.
In this embodiment, the processor and the memory are integrated on a Raspberry Pi Zero 2W development board, on which a BCM2710A1 chip and 512MB RAM are integrated,
the scanner is composed of an "HBV-ZERO V3 camera", "OV5647 chip", "LED lamp", "switch button", "Raspberry Pi ZERO 2W development board", "electric quantity liquid crystal display screen", and "rechargeable battery", which are integrated in the hand-held handle 5.
When the dental disease diagnosis device is used, the handle switch is pressed, the camera of the raspberry group is called, the LED lamp is turned on to collect images, the dental disease diagnosis is realized through the processor and the memory after the collection, and a diagnosis result is output.
It should be noted that, in the above embodiments, as long as the technical solutions can be aligned and combined without contradiction, a person skilled in the art can exhaust all possibilities according to the mathematical knowledge of the alignment and combination, and therefore the invention does not describe the technical solutions after alignment and combination one by one, but it should be understood that the technical solutions after alignment and combination have been disclosed by the invention. Furthermore, it should be noted that "air" is merely an example, and other thermally stable gases are equally suitable.
This embodiment is only illustrative of the patent and does not limit the scope of protection thereof, and those skilled in the art can make modifications to its part without departing from the spirit of the patent.
Claims (6)
1. A method of diagnosing dental conditions, comprising the steps of:
step 1, acquiring a tooth area image to be detected;
step 2, carrying out noise reduction treatment on the acquired tooth area image;
3, performing 3D modeling on the tooth region image subjected to noise reduction treatment in the step 3 to construct a tooth lesion three-dimensional model;
step 4, constructing a deep convolutional neural network classifier, putting the acquired dental disease image of the patient into the classifier for recognition, and judging the symptom of the dental disease corresponding to the processed image;
step 5, performing health assessment according to the diagnosis result, and performing medical advice matching through a database;
making some appropriate suggestions aiming at the treatment modes of related diseases in advance, popping up corresponding suggestions after the diseases are detected, if no suggestions satisfactory to users exist, popping up and clicking hyperlink options to call a url format of hundred-degree search, and skipping and retrieving the corresponding diseases.
2. The dental disease diagnosis method according to claim 1, wherein: the specific method for performing noise reduction processing on the acquired tooth region image in the step 2 is as follows: firstly, carrying out noise reduction processing on an image by using opencv, firstly, importing acquired image data of a tooth area, carrying out rapid noise reduction on the image by using a fastNlMeans denoising color function, secondly, expanding the data volume by using an image overturning method of opencv, removing useless parts according to an image slicing method, changing parameter values according to the difference of data to obtain an optimal part, and finally, storing the processed image data.
3. A dental disease diagnostic method according to claim 1, wherein: in step 3, the specific method for constructing the three-dimensional model of the dental lesion is as follows:
step 31, transmitting the image data picture data set processed in the step 2 into RealityCapture software;
step 32, solving a plurality of pairs of matching points through a sift algorithm, then calibrating or acquiring an initial internal reference matrix K of the camera from image information,
step 33, based on the internal reference matrix K and a plurality of pairs of matching points, solving a basic matrix F through a normalization eight-point method and a matching (Randac) mode for removing error points, and solving an essential matrix E based on the basic matrix F and the internal reference matrix K;
step 34, performing triangulation on the matching pairs based on multiple visual angles and camera parameters to obtain three-dimensional point coordinates, and selecting correct camera postures R and t by using the constraint of the three-dimensional point coordinates in front of a camera, wherein R and t are camera external parameters, R is a 3 x 3 rotation matrix, and t is a 3 x 1 displacement vector;
step 35, carrying out triangulation based on the matching pairs and the camera parameters, and solving three-dimensional coordinates of all the matching pairs;
and step 36, performing nonlinear optimization by using an LM (Linear modeling) algorithm based on the three-dimensional coordinates and the camera parameters obtained in the step 34 and the matching pair coordinates obtained in the step 35 to obtain more accurate three-dimensional point coordinates and camera internal and external parameters, so as to realize 3D model construction, and exporting after the 3D model is constructed.
4. The dental disease diagnosis method according to claim 1, wherein: in step 4, a deep convolutional neural network classifier is constructed, the acquired dental disease image of the patient is placed into the classifier for recognition, and the symptom of the dental disease corresponding to the processed image is judged, wherein the specific method comprises the following steps:
<xnotran> 41. , , , , , 5 y, [1,0,0,0,0], [0,1,0,0,0], [0,0,1,0,0], [0,0,0,1,0], [0,0,0,0,1], X Y, , Y , [1,3*128*128] , X , X Y, X Y , , ; </xnotran>
Step 42, initialization: selecting an activation function Relu, using a He initial value as a weight initial value, and initializing grid parameters;
wherein n is 1 The number of the first layer neurons;
step 43, the input layer is convoluted and pooled twice, and the intermediate link part uses the RELU function
the convolution kernel size of tf.nn.conv2d is 3 or 5, the step length is 1, padding = same, the size of the original image is maintained, the step length of tf.nn.max _ pool is 2, and the image characteristics are shrunk;
step 44, regularization is carried out, the model always has an overfitting phenomenon, after the last pooling layer is flattened, dropouts are used for randomly removing some nodes, each element is allowed to possibly disappear, the model does not depend on a certain characteristic excessively, overfitting is prevented, and as dental diseases are small in a tooth picture, 0.8-0.9 keep _ prob is selected, too many nodes cannot be eliminated, so that key characteristics are removed;
and step 45, finally, putting the full connection layer, and obtaining the probability through softmax transformation:
wherein p is i For the probability that the current picture belongs to the ith category, there are 5 categories, z i The ith input of the SoftMax layer;
step 46, integrating the model, using adam optimization to perform back propagation to minimize cost, and storing the session and parameters for subsequent data input;
and step 47, extracting frames of the video collected by the camera by opencv every 0.5s, cutting the video into images, and putting the images into a model to predict diseases.
5. A dental diagnostic apparatus comprising a scanner (1), a processor, a memory for storing a computer program operable on the processor, a probe housing (3), a support (4), and a handle (5), wherein the scanner (1) is connected to the processor, and the processor is configured to execute the steps of the dental diagnostic method according to any one of claims 1 to 4 when the computer program is executed, the scanner (1) is integrated in the probe housing (3), the probe housing (3) is mounted on the support (4) through a rotatable bearing, and the handle (5) is connected to a lower end of the support (4).
6. The dental diagnosis instrument according to claim 5, wherein: the scanner (1) has 500 ten thousand pixel lenses, a BCM2710A1 chip as a processor and 512MB of RAM as a memory.
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