Tooth image damage identification processing system and method

Abstract

The invention discloses a tooth image damage identification processing system and method, and relates to the technical field of image identification processing. Comprising a damage identification module, a damage processing module and a user interface and output module, through analysis of the damaged tooth image, accurate evaluation of tooth damage is realized, and specific decision suggestions are provided according to an evaluation result. Through quantitative calculation and feature matching, a more objective recognition result is provided; a standardized analysis process can be provided through a preset standard damage image library and a gray feature value, and the consistency of an identification result is ensured; the system further comprises an image acquisition module and a server. The method comprises the following steps: acquiring an image of teeth by using a high-definition and high-resolution oral cavity endoscope, preprocessing image data, and sending the image data to a server for storage; through automatic image processing and analysis, tooth damage can be quickly and accurately identified, and the time and workload of manual identification are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition and processing, and in particular to a tooth image damage recognition and processing system and method. Background Art

[0002] Damage recognition and processing of dental images refers to analyzing dental images through image processing technology to detect and identify the type, location and severity of dental damage, which can help doctors identify the health status of teeth more efficiently and accurately and discover potential oral problems in a timely manner. Damage recognition and processing of dental images is of great significance in modern dental medicine, especially with the advancement of technology, automated and intelligent recognition plays an increasingly important role in oral health management.

[0003] Currently, when dentists work for long hours or face a large number of cases, they may make mistakes in judgment due to fatigue. When faced with complex tooth images or subtle injuries, dentists may overlook details, resulting in minor injuries going undetected. Traditional manual recognition often relies on the experience and intuition of dentists. The recognition results may be affected by individual differences, experience levels, and subjective factors of doctors, which may lead to misdiagnosis or missed diagnosis. Traditional tooth injury assessment usually relies on the doctor's experience for subjective judgment and lacks a quantitative and standardized assessment system, which may result in differences in the assessment results of the same injury between different doctors, or even by the same doctor at different times. Summary of the invention

[0004] The main purpose of the present invention is to provide a tooth image damage identification and processing system and method to overcome the problems mentioned in the above background technology.

[0005] To achieve the above object, according to one aspect of the present invention, a tooth image damage recognition and processing system is provided, the system comprising: a damage recognition module, a damage processing module and a user interface and output module;

[0006] The damage recognition module analyzes and processes tooth images to identify and classify tooth damage types;

[0007] The damage processing module analyzes the damaged tooth images to accurately assess caries, cracks and missing tooth damage, and provides specific decision-making recommendations based on the assessment results. The specific steps are as follows:

[0008] Assume that there is a sample library of caries damage assessment values, obtain the maximum value of caries damage assessment, and mark it as W 龋齿 ; Assume that there is a crack damage assessment sample library, obtain the maximum value of the crack damage assessment, and mark it as W 裂纹; Assume that there is a missing damage assessment sample library, obtain the maximum value of the missing damage assessment, and mark it as W 缺失 ; The target image's caries damage assessment value W1, crack damage assessment value W2, missing damage assessment value W3, caries damage assessment sample maximum value W 龋齿 , the maximum value of crack damage assessment sample W 裂纹 and the maximum value of the missing damage assessment sample W 缺失 Substitute the values ​​into the formula The standardized standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3' are calculated; the values ​​of the standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3 are substituted into the formula Ki=a7×W1'+a8×W2'+a9×W3' to calculate the damage degree value Ki; wherein a7, a8 and a9 are respectively preset weight correction factors;

[0009] A tooth damage degree threshold interval is set, and the damage degree value of the target image is compared and analyzed with the set tooth damage degree threshold interval; when the damage degree value of the target image is greater than the maximum value of the set tooth damage degree threshold interval, it indicates that the image is of a severe damage type, a severe damage image, and matches the corresponding preset severe decision scheme; when the damage degree value of the target image is within the set tooth damage degree threshold interval, it indicates that the image is of a slight damage type, a slight damage image, and matches the corresponding preset mild decision scheme; when the damage degree value of the target image is less than the minimum value of the set tooth damage degree threshold interval, it indicates that the image has no obvious damage and is a non-damaged tooth image;

[0010] The user interface and output module generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback functions.

[0011] Furthermore, it also includes an image acquisition module and a server:

[0012] The image acquisition module uses a high-definition and high-resolution oral intraoral camera to collect images of teeth, pre-processes the image data, and sends the processed images to the server for storage.

[0013] Further, the specific analysis of the caries damage assessment value is as follows:

[0014] The edge of the caries area is monitored by the edge detection algorithm, and the caries area is located; the number of pixels in the caries area, that is, the number of pixels with an edge detection result of 1, is obtained, and the number of pixels in the caries area is summed up to obtain the area of ​​the caries area, which is marked as Ei; the total pixel area of ​​all teeth in the same position is averaged to obtain the average total pixel area of ​​each tooth in the same position, which is marked as E 总 ; Using the formula The regional intensity Di of caries is calculated; the numerical values ​​of the area Ei of the caries region and the regional intensity Di of caries are substituted into the set formula W1=a1×Ei+a2×Di to calculate the caries damage assessment value W1, wherein a1 and a2 are preset weight correction factors, and their sizes are customized by professional and technical personnel in this field.

[0015] Further, the specific analysis of the crack damage assessment value is:

[0016] The crack area is accurately located through the edge detection algorithm; the number of cracks in the crack area is obtained and marked as Ti; the grayscale value range of the crack area is obtained, and the maximum grayscale value and the minimum grayscale value of the crack are differenced to obtain the crack depth value, and it is marked as Bi; the numerical values ​​of the number of cracks Ti and the crack depth value Bi in the crack area are substituted into the set formula W2=a3×Ti+a4×Bi to calculate the damage assessment value W2 of the crack, where a3 and a4 are preset weight correction factors respectively.

[0017] Further, the specific analysis of the missing damage assessment value is as follows:

[0018] According to the hole area in the image, the contour features of the missing area are extracted and marked as the missing area; the area of ​​the missing area is determined by reverse edge detection, that is, the area with lower grayscale value, and the missing area is calculated by summing up the pixels in the area with lower grayscale value and marked as Ri; using the formula The missing strength Ci of the void is calculated; the area Ri of the missing region and the numerical value of the missing strength Ci are substituted into the formula W3=a5×Ri+a6×Ci to calculate the missing damage assessment value W3, where a5 and a6 are preset weight correction factors respectively.

[0019] Further, identify and classify the type of tooth damage. The specific steps are:

[0020] Obtain a preprocessed tooth image, divide it into several image blocks of the same size, and mark them as i, where i=1, 2, 3...I, I is a positive integer, I represents the total number of segmented images, and i represents the sequence number of any segmented image; pixelate each image block and convert it into a two-dimensional matrix composed of pixel values, and set the total number of pixels in the image block to M; use an edge detection algorithm (Canny algorithm) to find the edge features in the image, extract the damage features in the image, and mark them according to different damage types (such as caries, cracks and missing teeth); according to the samples in the damaged tooth image library, The grayscale values ​​of the caries images representing typical grayscale features are averaged to obtain the standard caries grayscale value, and its value is taken and marked as G'; the grayscale values ​​of the crack images representing typical grayscale features are averaged to obtain the standard crack grayscale value, and its value is taken and marked as L'; the grayscale values ​​of the missing images representing typical grayscale features are averaged to obtain the standard missing grayscale value, and its value is taken and marked as Q'; the tooth image i is marked as the target image, the grayscale value of the target image is obtained and marked as Pi, and the grayscale value Pi of the target image, the standard caries grayscale value G', the standard crack grayscale value L' and the standard missing grayscale value Q' are substituted into the formula group The caries similarity value Gi, crack similarity value Li and missing similarity value Qi of the target image are calculated; the caries similarity value Gi, crack similarity value Li and missing similarity value Qi are compared in size. When the caries similarity value Gi is the smallest, the target image damage type is determined to be caries; when the crack similarity value Li is the smallest, the target image damage type is determined to be crack; when the missing similarity value Qi is the smallest, the target image damage type is determined to be missing; a damage threshold is set, and the value with the smallest similarity value is compared and analyzed with the set damage threshold. When the similarity value of the target image is greater than the set threshold, it means that the target image has a low similarity with the standard damage type, and it is marked as a non-damaged tooth image; when the similarity value of the target image is less than the set threshold, it means that the target image has a high similarity with the standard damage type, and it is marked as a damaged tooth image and sent to the damage processing module.

[0021] Furthermore, an identification report is generated based on the damage identification and decision information of the target image. The specific steps are as follows:

[0022] Obtain injury information from the injury identification module and the injury treatment module, generate the final identification report, support different formats of output, and can be exported and printed for reference by patients and dentists; all generated identification reports should be automatically archived in the system database and classified according to the patient's ID, decision time, etc.; dentists can query the patient's historical identification report through the system to understand the patient's decision-making process;

[0023] The generated identification report lists the types of tooth damage and attaches relevant images or annotations to help dentists understand the nature of the damage; based on the image analysis results, the location of the damaged area is automatically marked and displayed in the report; based on the damage assessment value, the report automatically assesses the severity of the damage and provides a detailed rating; based on the damage assessment results, the system automatically recommends the corresponding decision plan; based on the type and severity of the damage, the report will list the expected decision steps and effects;

[0024] Dentists can interact with the system and click on a damaged area to view detailed information or adjust assessment parameters. The system also enhances image interaction and supports different types of annotation and highlighting so that dentists can quickly understand the details of the injury when viewing the image. It provides a toolbar so that dentists can annotate, modify or edit the image, optimize the recognition results of the damaged area, instantly display the modified results, and automatically update the recognition and decision-making recommendations. If there is new image data or assessment results during the decision-making process, it will be automatically updated and fed back to the interface in real time to ensure the accuracy and timeliness of the information.

[0025] In order to achieve the above object, according to another aspect of the present invention, a method for identifying and processing tooth image damage is provided, the method comprising the following steps:

[0026] S1: Use a high-definition and high-resolution oral intraoral camera to collect images of teeth, pre-process the image data, and send the processed images to the server for storage;

[0027] S2: Identify and classify the types of tooth damage by analyzing and processing tooth images;

[0028] S3: Through the analysis of damaged tooth images, accurate assessment of caries, cracks and missing tooth damage is achieved, and specific decision-making recommendations are provided based on the assessment results; the specific steps are as follows:

[0029] Assume that there is a sample library of caries damage assessment values, obtain the maximum value of caries damage assessment, and mark it as W 龋齿 ; Assume that there is a crack damage assessment sample library, obtain the maximum value of the crack damage assessment, and mark it as W 裂纹 ; Assume that there is a missing damage assessment sample library, obtain the maximum value of the missing damage assessment, and mark it as W 缺失 ; The target image's caries damage assessment value W1, crack damage assessment value W2, missing damage assessment value W3, caries damage assessment sample maximum value W 龋齿 , the maximum value of crack damage assessment sample W 裂纹 and the maximum value of the missing damage assessment sample W 缺失 Substitute the values ​​into the formula The standardized standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3' are calculated; the values ​​of the standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3 are substituted into the formula Ki=a7×W1'+a8×W2'+a9×W3' to calculate the damage degree value Ki; wherein a7, a8 and a9 are respectively preset weight correction factors;

[0030] A tooth damage degree threshold interval is set, and the damage degree value of the target image is compared and analyzed with the set tooth damage degree threshold interval; when the damage degree value of the target image is greater than the maximum value of the set tooth damage degree threshold interval, it indicates that the image is of a severe damage type, a severe damage image, and matches the corresponding preset severe decision scheme; when the damage degree value of the target image is within the set tooth damage degree threshold interval, it indicates that the image is of a slight damage type, a slight damage image, and matches the corresponding preset mild decision scheme; when the damage degree value of the target image is less than the minimum value of the set tooth damage degree threshold interval, it indicates that the image has no obvious damage and is a non-damaged tooth image;

[0031] S4: Generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback function.

[0032] Beneficial effects of the present invention:

[0033] The present invention effectively identifies and classifies injury types according to the actual injury conditions of patients, which not only reduces the waiting time of patients, but also improves the efficiency of the diagnosis and treatment process; the automated evaluation and recommendation system reduces the possible omissions or misdiagnoses that may occur when doctors are busy, thereby improving the safety and accuracy of decision-making. For certain high-risk cases, the system can provide early warnings to help doctors make more appropriate treatment decisions, improve the accuracy, efficiency and personalization of identification and decision-making, reduce human errors, and help patients better understand and choose decision-making plans;

[0034] The present invention automatically recommends appropriate decision-making plans based on the actual injury conditions of the patients, which not only reduces the waiting time of patients, but also improves the efficiency of the diagnosis and treatment process; the automated evaluation and recommendation system reduces the possible omissions or misdiagnoses that may occur when doctors are busy, thereby improving the safety and accuracy of decision-making. For certain high-risk cases, the system can provide early warnings to help doctors make more appropriate treatment decisions, improve the accuracy, efficiency and personalization of identification and decision-making, reduce human errors, and help patients better understand and choose decision-making plans;

[0035] The present invention reduces human errors and ensures accurate identification results by automatically generating reports and providing real-time feedback; displays damage analysis results and decision-making proposals in real time, enabling dentists to make decisions quickly and avoid delays in treatment; provides concise and clear reports and a transparent treatment process, allowing patients to understand their condition and decision-making plans and enhance their confidence; and recommends the best decision-making plan in real time based on different damage types and patient needs to improve results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 is a schematic diagram of system module connection of the present invention;

[0038] Figure 2 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0040] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0042] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] According to an embodiment of the present invention, Figure 1 As shown, a tooth image damage recognition and processing system is provided, the system comprising: an image acquisition module, a server, a damage recognition module, a damage processing module and a user interface and output module;

[0044] The image acquisition module uses a high-definition and high-resolution oral endoscope to collect images of teeth, preprocess the image data, and send the processed images to the server for storage; the preprocessing steps of tooth images are: obtain the collected tooth images and convert them into grayscale images; use Gaussian filters to remove noise in the images, then use histogram equalization to enhance the contrast of the images, and finally use the Canny edge detection algorithm to extract edge features to ensure that the images are clear, less noisy, and have obvious damage characteristics;

[0045] The damage recognition module analyzes and processes the tooth image to identify and classify the tooth damage type; the specific steps are as follows: obtain the pre-processed tooth image, divide it into several image blocks of the same size, and mark it as i, where i=1, 2, 3...I, I is a positive integer, I represents the total number of segmented images, and i represents the sequence number of any segmented image; pixelate each image block and convert it into a two-dimensional matrix composed of pixel values, and set the total number of pixels in the image block to M; use the edge detection algorithm (Canny algorithm) to find the edge features in the image, extract the damage features in the image, and mark them according to different damage types (such as caries, cracks and missing parts). Caries usually appear as dark areas in the image with obvious edges, and the color changes, texture changes and shape features in the detection area are detected; cracks are detected by It is often manifested as a linear edge feature with a small width, through edge detection and morphological analysis; the missing area usually has a more obvious missing area or cavity; according to the samples in the damaged tooth image library, the grayscale values ​​of the caries images representing the typical grayscale features are averaged to obtain the standard caries grayscale value, and its value is taken and marked as G'; the grayscale values ​​of the crack images representing the typical grayscale features are averaged to obtain the standard crack grayscale value, and its value is taken and marked as L'; the grayscale values ​​of the missing images representing the typical grayscale features are averaged to obtain the standard missing grayscale value, and its value is taken and marked as Q'; the tooth image i is marked as the target image, the grayscale value of the target image is obtained and marked as Pi, and the grayscale value Pi of the target image, the standard caries grayscale value G', the standard crack grayscale value L' and the standard missing grayscale value Q' are substituted into the set formula group Calculate the target image's caries similarity value Gi, crack similarity value Li and missing similarity value Qi;

[0046] Compare the caries similarity value Gi, the crack similarity value Li and the missing similarity value Qi. When the caries similarity value Gi is the smallest, the target image damage type is determined to be caries; when the crack similarity value Li is the smallest, the target image damage type is determined to be crack; when the missing similarity value Qi is the smallest, the target image damage type is determined to be missing; set a damage threshold, compare and analyze the value with the smallest similarity value with the set damage threshold. When the similarity value of the target image is greater than the set threshold, it means that the target image has a low similarity with the standard damage type, and it is marked as a non-damaged tooth image; when the similarity value of the target image is less than the set threshold, it means that the target image has a high similarity with the standard damage type, and it is marked as a damaged tooth image and sent to the damage processing module;

[0047] Through automated image processing and analysis, dental damage can be identified quickly and accurately, greatly reducing the time and workload of manual identification. Doctors can obtain image analysis results more quickly, thereby improving diagnosis and treatment efficiency. Through quantitative calculation and feature matching, more objective identification results are provided. Through the preset standard damage image library and grayscale feature values, the damage identification module can provide a standardized analysis process. Different tooth image analyses will be performed according to the same standards and methods, ensuring the consistency of identification results.

[0048] The damage processing module analyzes the damaged tooth images to accurately assess caries, cracks and missing tooth damage, and provides specific decision-making recommendations based on the assessment results. The specific steps are as follows:

[0049] The edge of the caries area is monitored by the edge detection algorithm, and the caries area is located; the number of pixels in the caries area is obtained, and the number of pixels in the caries area is summed to obtain the area of ​​the caries area, and it is marked as Ei; the total pixel area of ​​all teeth in the same position is averaged to obtain the average total pixel area of ​​each tooth in the same position, and it is marked as E 总 ; Using the formula The regional intensity Di of caries is calculated; the area of ​​the caries region Ei and the regional intensity Di of caries are substituted into the formula W1=a1×Ei+a2×Di to calculate the caries damage assessment value W1, where a1 and a2 are respectively preset weight correction factors, and the sizes are customized by professional and technical personnel in the field;

[0050] The crack area is accurately located by edge detection algorithm; the width of the crack is small, usually showing as a slender edge, the number of cracks in the crack area is obtained, and it is marked as Ti; the gray value range of the crack area is obtained, and the maximum gray value and the minimum gray value of the crack are calculated by difference to obtain the crack depth value, and it is marked as Bi; the numerical values ​​of the number of cracks Ti and the crack depth value Bi in the crack area are substituted into the set formula W2=a3×Ti+a4×Bi to calculate the damage assessment value W2 of the crack, where a3 and a4 are preset weight correction factors, and their sizes are customized by professional and technical personnel in this field; according to the hollow area in the image, the contour features of the missing area are extracted and marked as the missing area; the area of ​​the missing area, that is, the area with lower gray value, is determined by reverse edge detection, and the pixel points in the area with lower gray value are summed to calculate the missing area, and it is marked as Ri; using the formula The missing strength Ci of the cavity is calculated; the area Ri of the missing area and the missing strength Ci are substituted into the set formula W3=a5×Ri+a6×Ci to calculate the missing damage assessment value W3, where a5 and a6 are preset weight correction factors, and their sizes are customized by professional and technical personnel in this field; a sample library of caries damage assessment values ​​is set, and the maximum value of the caries damage assessment is obtained, and it is marked as W 龋齿 ; Assume that there is a crack damage assessment sample library, obtain the maximum value of the crack damage assessment, and mark it as W 裂纹 ; Assume that there is a missing damage assessment sample library, obtain the maximum value of the missing damage assessment, and mark it as W 缺失 ; The target image's caries damage assessment value W1, crack damage assessment value W2, missing damage assessment value W3, caries damage assessment sample maximum value W 龋齿 , the maximum value of crack damage assessment sample W 裂纹 and the maximum value of the missing damage assessment sample W 缺失 Substitute the value of into the set formula group The standardized standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3' are calculated; the values ​​of the standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3 are substituted into the set formula Ki=a7×W1'+a8×W2'+a9×W3' to calculate the damage degree value Ki; wherein a7, a8 and a9 are respectively preset weight correction factors, and the sizes are customized by professional and technical personnel in this field; it can be seen from the formula that the larger the caries damage assessment value, the larger the damage degree value; the larger the crack damage assessment value, the larger the damage degree value; the larger the missing damage assessment value, the larger the damage degree value;

[0051] A tooth damage degree threshold interval is set, and the damage degree value of the target image is compared with the set tooth damage degree threshold interval for analysis; when the damage degree value of the target image is greater than the maximum value of the set tooth damage degree threshold interval, it indicates that the image is of a severe damage type, a severely damaged image, and matches the corresponding preset severe decision plan; when the damage degree value of the target image is within the set tooth damage degree threshold interval, it indicates that the image is of a slight damage type, a slightly damaged image, and matches the corresponding preset mild decision plan; when the damage degree value of the target image is less than the minimum value of the set tooth damage degree threshold interval, it indicates that the image has no obvious damage and is an undamaged tooth image; the slight damage decision plan includes: for caries, filling materials can be used for repair; for cracks, surface repair or reinforcement is performed; for missing parts, coatings are used for covering; the severe damage decision plan includes: for caries, root canal decision is considered; for cracks, dental implant restoration is recommended; for missing parts, dental implant restoration or denture restoration is recommended; it should be noted that the above recommended plans are customized by professional and technical personnel in this field and can be replaced and changed;

[0052] By effectively identifying and classifying the types of injuries according to the actual injury conditions of the patients, it not only reduces the waiting time of patients, but also improves the efficiency of the diagnosis and treatment process; the automated assessment and recommendation system reduces the possible omissions or misdiagnoses that may occur when doctors are busy, thereby improving the safety and accuracy of decision-making. For certain high-risk cases, the system can provide early warnings to help doctors make more appropriate treatment decisions, improve the accuracy, efficiency and personalization of identification and decision-making, reduce human errors, and help patients better understand and choose decision-making plans;

[0053] The user interface and output module generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback. The specific steps are as follows:

[0054] Obtain injury information from the injury identification module and the injury treatment module, including injury type and assessment value; automatically fill in the report content according to the preset template, including patient information, injury analysis, and decision-making recommendations; format the report in the form of images, text, charts, etc. to ensure that the content is clear and easy to read; generate the final identification report, support different formats of output, and can be exported and printed for reference by patients and dentists; all generated identification reports should be automatically archived in the system database and classified according to the patient's ID, decision time, etc.; dentists can query the patient's historical identification report through the system to understand the patient's decision-making process;

[0055] The generated identification report lists the types of tooth damage (such as caries, cracks, and missing teeth), and attaches relevant images or annotations to help dentists understand the nature of the damage; based on the image analysis results, the location of the damaged area is automatically marked (for example, on which surface of the tooth the caries is located, the starting point of the crack, etc.) and displayed in the report; based on the damage assessment value (W1, W2, W3), the report automatically assesses the severity of the damage and provides a detailed rating (such as mild, severe); based on the damage assessment results, the system automatically recommends the corresponding decision plan (such as filling, root canal decision, implant restoration, etc.); based on the type and severity of the damage, the report will list the expected decision steps and effects;

[0056] Dentists can interact with the system and click on a damaged area to view detailed information or adjust assessment parameters (such as damage intensity, area size, etc.). The system also enhances image interaction and supports different types of annotations (such as color, lines, arrows, etc.) and highlighting, so that dentists can quickly understand the details of the damage when viewing the image. It provides a toolbar so that dentists can annotate, modify or edit the image, optimize the recognition results of the damaged area, display the modified results immediately, and automatically update the recognition and decision-making suggestions. If there is new image data or assessment results during the decision-making process, it will be automatically updated and fed back to the interface in real time to ensure the accuracy and timeliness of the information.

[0057] By automatically generating reports and providing real-time feedback, human errors are reduced and the identification results are accurate. The damage analysis results and decision-making plan recommendations are displayed in real time, allowing dentists to make decisions quickly and avoid delays. Concise and clear reports and transparent decision-making processes are provided to enable patients to understand their condition and decision-making plans, and enhance their confidence in decision-making. According to different damage types and patient needs, the best decision-making plan is recommended in real time to improve decision-making results.

[0058] According to an embodiment of the present invention, Figure 2 As shown, a method for identifying and processing tooth image damage is also provided, the method comprising the following steps:

[0059] S1: Use a high-definition and high-resolution oral intraoral camera to collect images of teeth, pre-process the image data, and send the processed images to the server for storage;

[0060] S2: Identify and classify the types of tooth damage by analyzing and processing tooth images;

[0061] S3: Through the analysis of damaged tooth images, accurate assessment of caries, cracks and missing tooth damage is achieved, and specific decision-making recommendations are provided based on the assessment results; the specific steps are as follows:

[0062] Assume that there is a sample library of caries damage assessment values, obtain the maximum value of caries damage assessment, and mark it as W龋齿 ; Assume that there is a crack damage assessment sample library, obtain the maximum value of the crack damage assessment, and mark it as W 裂纹 ; Assume that there is a missing damage assessment sample library, obtain the maximum value of the missing damage assessment, and mark it as W 缺失 ; The target image's caries damage assessment value W1, crack damage assessment value W2, missing damage assessment value W3, caries damage assessment sample maximum value W 龋齿 , the maximum value of crack damage assessment sample W 裂纹 and the maximum value of the missing damage assessment sample W 缺失 Substitute the values ​​into the formula The standardized standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3' are calculated; the values ​​of the standard caries damage assessment value W1', the standard crack damage assessment value W2' and the standard missing damage assessment value W3 are substituted into the formula Ki=a7×W1'+a8×W2'+a9×W3' to calculate the damage degree value Ki; wherein a7, a8 and a9 are respectively preset weight correction factors, and the sizes thereof are customized by professional and technical personnel in the field;

[0063] A tooth damage degree threshold interval is set, and the damage degree value of the target image is compared and analyzed with the set tooth damage degree threshold interval; when the damage degree value of the target image is greater than the maximum value of the set tooth damage degree threshold interval, it indicates that the image is of a severe damage type, a severe damage image, and matches the corresponding preset severe decision scheme; when the damage degree value of the target image is within the set tooth damage degree threshold interval, it indicates that the image is of a slight damage type, a slight damage image, and matches the corresponding preset mild decision scheme; when the damage degree value of the target image is less than the minimum value of the set tooth damage degree threshold interval, it indicates that the image has no obvious damage and is a non-damaged tooth image;

[0064] S4: Generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback function.

[0065] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A tooth image damage recognition and processing system, characterized in that: include: Damage identification module, damage processing module and user interface and output module; The damage recognition module analyzes and processes tooth images to identify and classify tooth damage types; The damage processing module analyzes the damaged tooth images to accurately assess caries, cracks and missing tooth damage, and provides specific decision-making recommendations based on the assessment results. The specific steps are as follows: The maximum value of the caries damage assessment, the maximum value of the crack damage assessment, and the maximum value of the missing damage assessment are obtained; the caries damage assessment value, the crack damage assessment value, the missing damage assessment value, the maximum value of the caries damage assessment sample, the maximum value of the crack damage assessment sample, and the maximum value of the missing damage assessment sample of the target image are normalized to obtain a standard caries damage assessment value, a standard crack damage assessment value, and a standard missing damage assessment value, and then processed to obtain a damage degree value; Compare and analyze the damage degree value of the target image with the set tooth damage degree threshold interval: when the damage degree value of the target image is greater than the maximum value of the set tooth damage degree threshold interval, match the corresponding preset severe decision plan; when the damage degree value of the target image is within the set tooth damage degree threshold interval, match the corresponding preset mild decision plan; when the damage degree value of the target image is less than the minimum value of the set tooth damage degree threshold interval, it is a non-damaged tooth image; The user interface and output module generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback functions.

2. A tooth image damage recognition and processing system according to claim 1, characterized in that: Also includes an image acquisition module and a server; The image acquisition module uses a high-definition and high-resolution oral intraoral camera to collect images of teeth, pre-processes the image data, and sends the processed images to the server for storage.

3. A tooth image damage recognition and processing system according to claim 1, characterized in that: The specific analysis of caries damage assessment value is as follows: The edge of the caries area is monitored by an edge detection algorithm, and the caries area is located; the number of pixels in the caries area is obtained, the number of pixels in the caries area is summed up to calculate the area of ​​the caries area, and the area is marked; The total pixel area of ​​all teeth at the same position is averaged to obtain the average total pixel area of ​​each tooth at the same position, and the tooth is marked; The area of ​​the caries region and the average total pixel area of ​​each tooth are normalized to obtain the regional intensity of caries; the area of ​​the caries region and the numerical value of the regional intensity of caries are normalized to obtain the damage assessment value of caries.

4. A tooth image damage recognition and processing system according to claim 1, characterized in that: The specific analysis of the crack damage assessment value is: The crack area is accurately located through the edge detection algorithm, the number of cracks in the crack area is obtained, and it is marked; the gray value range of the crack area is obtained, and the maximum gray value and the minimum gray value of the crack are calculated by difference to obtain the crack depth value, and it is marked; The number of cracks and the crack depth in the crack area are normalized to obtain the damage assessment value of the crack.

5. A tooth image damage recognition and processing system according to claim 1, characterized in that: The specific analysis of missing damage assessment values ​​is as follows: According to the hole area in the image, the contour features of the missing area are extracted and marked as the missing area; the area of ​​the missing area is determined by reverse edge detection, that is, the area with lower grayscale value, and the missing area is calculated by summing up the pixel points in the area with lower grayscale value and marked; The missing area and the average total pixel area of ​​each tooth were normalized to obtain the missing intensity of the cavity; The area of ​​the missing region and the value of the missing intensity are normalized to obtain the missing damage assessment value.

6. A tooth image damage identification and processing system according to claim 1, characterized in that: Identify and classify the type of tooth damage. The specific steps are: Obtain a preprocessed tooth image and divide it into several image blocks of the same size; perform pixelation processing on each image block and convert it into a two-dimensional matrix composed of pixel values; use an edge detection algorithm to find edge features in the image, extract damage features in the image, and mark them according to different damage types; based on samples in a damaged tooth image library, calculate the mean of the grayscale values ​​of caries images representing typical grayscale features to obtain a standard caries grayscale value, take its value and mark it; calculate the mean of the grayscale values ​​of crack images representing typical grayscale features to obtain a standard crack grayscale value, take its value and mark it; The grayscale values ​​of the images representing typical grayscale feature missing images are averaged to obtain standard missing grayscale values, and the values ​​are taken and marked to obtain the grayscale values ​​of the target image. The grayscale values ​​of the target image, the standard caries grayscale values, the standard crack grayscale values, and the standard missing grayscale values ​​are normalized to obtain caries similarity values, crack similarity values, and missing similarity values ​​of the target image. The caries similarity value, crack similarity value and missing similarity value are compared in size. When the caries similarity value is the smallest, the target image damage type is determined to be caries; when the crack similarity value is the smallest, the target image damage type is determined to be crack; when the missing similarity value is the smallest, the target image damage type is determined to be missing; a damage threshold is set, and the value with the smallest similarity value is compared and analyzed with the set damage threshold. When the similarity value of the target image is greater than the set threshold, it means that the target image has a low similarity with the standard damage type, and it is marked as a non-damaged tooth image; when the similarity value of the target image is less than the set threshold, it means that the target image has a high similarity with the standard damage type, and it is marked as a damaged tooth image and sent to the damage processing module.

7. A tooth image damage identification and processing system according to claim 1, characterized in that: Generate an identification report based on the damage identification and decision information of the target image. The specific steps are as follows: Obtain injury information from the injury identification module and the injury treatment module, generate the final identification report, support different formats of output, export and print; all generated identification reports should be automatically archived in the system database and classified according to the patient's ID and decision time; dentists can query the patient's historical identification report through the system to understand the patient's decision-making process; The generated identification report lists the types of tooth damage and attaches relevant images or annotations; based on the image analysis results, the location of the damaged area is automatically marked and displayed in the report; Based on the damage assessment value, the report automatically assesses the severity of the damage and provides a detailed rating; based on the damage assessment results, the system automatically recommends the corresponding decision-making plan; based on the damage type and severity, the report lists the expected decision-making steps and effects; Dentists interact with the system through smart terminals, click on a damaged area to view detailed information or adjust assessment parameters, and enhance image interaction functions, support different types of annotations and highlights, so that dentists can quickly understand the details of the damage when viewing the image; provide a toolbar for dentists to annotate, modify or edit the image, optimize the recognition results of the damaged area, instantly display the modified results, and automatically update the recognition and decision suggestions; If there is new image data or evaluation results during the decision-making process, it will be automatically updated and fed back to the interface in real time to ensure the accuracy and timeliness of the information.

8. A method for identifying and processing tooth image damage, characterized in that Applied to a tooth image damage recognition and processing system as claimed in any one of claims 1 to 7, the method comprises the following steps: S1: Use a high-definition and high-resolution oral endoscope to collect images of teeth, pre-process the image data, and send the processed images to the server for storage; S2: Identify and classify the types of tooth damage by analyzing and processing tooth images; S3: Through the analysis of damaged tooth images, accurate assessment of caries, cracks and missing tooth damage can be achieved, and specific decision-making recommendations can be provided based on the assessment results; S4: Generates an identification report based on the damage identification and decision information of the target image, and helps dentists make quick decisions through real-time feedback function.

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