Auricle data model construction method and system based on normal auricle morphology classification

Abstract

The present invention relates to the field of medical care, and discloses an auricle data model construction method and system based on normal auricle morphology classification, comprising the following steps: an auricle feature extraction module receives target auricle image data, extracts corresponding auricle validity features according to the target auricle image data, judges the validity of the target auricle image data according to the auricle validity features, obtains the auricle type in the auricle image data, and if it is a normal auricle morphology, the auricle feature extraction module obtains the associated auricle morphology parameters according to the auricle validity features, compares the auricle morphology parameters with the standard normal auricle morphology parameters in the cloud data server, obtains the auricle data model correction parameters, and the auricle model construction module constructs the target auricle data model according to the auricle data model correction parameters and the auricle standard data model. Through the present invention, the subtle features of the auricle can be accurately captured, the accuracy of the auricle data model can be significantly improved, and the high-precision application requirements can be met.

Description

Technical Field

[0001] The present invention relates to the field of medical care, and in particular to a method and system for constructing an auricle data model based on normal auricle morphology classification. Background Art

[0002] With the rapid development of modern medicine, audiology research, and virtual reality (VR) and augmented reality (AR) technologies, it has become crucial to accurately and efficiently obtain and construct individual auricle data models. As one of the important external structures of the human body, the auricle has diverse and complex shapes, which not only affects the auditory function, but also involves many aspects such as aesthetics. Traditional methods such as manual measurement and simple scanning often have limited accuracy and are difficult to fully capture the subtle features of the auricle. In addition, the ability to process abnormal auricle shapes is limited, and it cannot meet the needs of high-precision and personalized customization.

[0003] In recent years, with the continuous advancement of computer vision, image processing, artificial intelligence and other technologies, automated auricle morphology analysis and modeling based on image data has become possible. However, most existing technologies focus on the recognition and modeling of normal auricle morphology, and the processing of abnormal auricle morphology (such as deformity, injury, wearing auxiliary equipment, etc.) is still challenging. In addition, how to improve data processing efficiency and reduce manual intervention while ensuring model accuracy is also a problem that current technology urgently needs to solve. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for constructing an auricle data model based on normal auricle morphology classification, comprising the following steps:

[0005] Step 1: The auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and judges the validity of the target auricle image data according to the auricle validity feature. If it is valid, proceed to step 2; if it is invalid, re-collect the target auricle image data and send it to the auricle feature extraction module;

[0006] Step 2, obtaining the auricle type in the auricle image data, if it is a normal auricle shape, proceed to step 3; if it is an abnormal auricle shape, proceed to step 4;

[0007] Step 3: The auricle feature extraction module obtains the associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with the standard normal auricle morphological parameters in the cloud data server, obtains the auricle data model correction parameters, and proceeds to step 6;

[0008] Step 4: The auricle feature extraction module obtains auricle morphological parameters associated with the auricle validity feature, and matches the auricle morphological parameters with extensive auricle morphological data stored in the cloud database to obtain auricle morphological data corresponding to the auricle morphological parameters. The cloud database sends the auricle morphological data corresponding to the auricle morphological parameters to the auricle model construction module, and then proceeds to step 5;

[0009] Step 5: The auricle model building module compares the auricle morphology data corresponding to the auricle morphology parameters sent by the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, and then proceeds to step 6;

[0010] Step six, the auricle model construction module constructs a target auricle data model according to the auricle data model correction parameters and the auricle standard data model.

[0011] Furthermore, the auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and determines the validity of the target auricle image data according to the auricle validity feature, including:

[0012] The auricle validity features include the clarity of the auricle image data, the integrity of the auricle image, and the angle and distance between the auricle and the head. If the clarity of the auricle image data is not less than the set clarity threshold and the integrity of the auricle image is not less than the set integrity threshold, the target auricle image data is valid.

[0013] Furthermore, the method of obtaining the auricle type in the auricle image data includes:

[0014] If the angle and distance between the auricle and the skull are within the preset normal auricle standard range, it is a normal auricle shape; otherwise it is an abnormal auricle shape.

[0015] Furthermore, the auricle feature extraction module obtains associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with standard normal auricle morphological parameters in the cloud data server, and obtains auricle data model correction parameters, including:

[0016] The auricle morphological parameters include the angle and distance between the auricle and the skull, and the parameter difference between the corresponding parameters in the standard normal auricle morphological parameters in the cloud data server is obtained. The parameter difference constitutes the auricle data model correction parameter.

[0017] Furthermore, the auricle morphology parameters are matched with a wide auricle morphology database stored in a cloud database to obtain auricle morphology data corresponding to the auricle morphology parameters, including:

[0018] According to the angle and distance between the auricle and the head in the auricle morphology parameters, the broad auricle morphology database stored in the cloud database is matched to obtain matched broad auricle morphology data, and the similarity between each broad auricle morphology data and the target auricle image data is obtained respectively, among which the broad auricle morphology data with the greatest similarity is the auricle morphology data corresponding to the auricle morphology parameters.

[0019] Furthermore, the auricle model building module compares the auricle morphology data corresponding to the auricle morphology parameters sent by the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, including:

[0020] The parameter difference between the auricle morphology data corresponding to the auricle morphology parameters and the standard normal auricle morphology parameters constitutes the auricle data model correction parameters.

[0021] Furthermore, the auricle model construction module constructs a target auricle data model according to the auricle data model correction parameters and the auricle standard data model, including:

[0022] The auricle model construction module corrects the auricle standard data model according to the auricle data model correction parameters to obtain the target auricle data model.

[0023] An auricle data analysis system based on normal auricle morphology classification, applying the auricle data model construction method based on normal auricle morphology classification, comprising: a cloud data server, an auricle feature extraction module, a data processing module, a communication module, an auricle image data acquisition module and an auricle model construction module;

[0024] The communication module, auricle image data acquisition module, auricle feature extraction module and auricle model construction module are respectively connected to the data processing module, and the cloud data server is communicatively connected to the communication module.

[0025] The beneficial effects of the present invention are: improving modeling accuracy: through refined feature extraction and parameter comparison, the present invention can accurately capture the subtle features of the auricle, significantly improve the accuracy of the auricle data model, and meet high-precision application requirements.

[0026] Enhanced applicability: The present invention is not only applicable to normal auricle morphology, but can also effectively handle abnormal auricle morphology, thereby broadening the scope of application and improving the versatility and practicality of the system.

[0027] Improve efficiency: The automated process reduces manual intervention, making the entire process from image acquisition to model building efficient and fast, reducing time and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1A flowchart of a method for constructing an auricle data model based on normal auricle morphology classification;

[0029] Figure 2 It is a schematic diagram of the principle of the auricle data analysis system based on the classification of normal auricle morphology;

[0030] Figure 3 Schematic diagram of the principle of the auricle image data acquisition module. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0032] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0033] like Figure 1 As shown, the method for constructing an auricle data model based on normal auricle morphology classification includes the following steps:

[0034] Step 1: The auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and judges the validity of the target auricle image data according to the auricle validity feature. If it is valid, proceed to step 2; if it is invalid, re-collect the target auricle image data and send it to the auricle feature extraction module;

[0035] Step 2, obtaining the auricle type in the auricle image data, if it is a normal auricle shape, proceed to step 3; if it is an abnormal auricle shape, proceed to step 4;

[0036] Step 3: The auricle feature extraction module obtains the associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with the standard normal auricle morphological parameters in the cloud data server, obtains the auricle data model correction parameters, and proceeds to step 6;

[0037] Step 4: The auricle feature extraction module obtains auricle morphological parameters associated with the auricle validity feature, and matches the auricle morphological parameters with extensive auricle morphological data stored in the cloud database to obtain auricle morphological data corresponding to the auricle morphological parameters. The cloud database sends the auricle morphological data corresponding to the auricle morphological parameters to the auricle model construction module, and then proceeds to step 5;

[0038] Step 5: The auricle model building module compares the auricle morphology data corresponding to the auricle morphology parameters sent by the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, and then proceeds to step 6;

[0039] Step six, the auricle model construction module constructs a target auricle data model according to the auricle data model correction parameters and the auricle standard data model.

[0040] The auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and determines the validity of the target auricle image data according to the auricle validity feature, including:

[0041] The auricle validity features include the clarity of the auricle image data, the integrity of the auricle image, and the angle and distance between the auricle and the head. If the clarity of the auricle image data is not less than the set clarity threshold and the integrity of the auricle image is not less than the set integrity threshold, the target auricle image data is valid.

[0042] The method of obtaining the auricle type in the auricle image data includes:

[0043] If the angle and distance between the auricle and the skull are within the preset normal auricle standard range, it is a normal auricle shape; otherwise it is an abnormal auricle shape.

[0044] The auricle feature extraction module obtains the associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with the standard normal auricle morphological parameters in the cloud data server, and obtains the auricle data model correction parameters, including:

[0045] The auricle morphological parameters include the angle and distance between the auricle and the skull, and the parameter difference between the corresponding parameters in the standard normal auricle morphological parameters in the cloud data server is obtained. The parameter difference constitutes the auricle data model correction parameter.

[0046] The auricle morphology parameters are matched with a wide auricle morphology database stored in a cloud database to obtain auricle morphology data corresponding to the auricle morphology parameters, including:

[0047] According to the angle and distance between the auricle and the head in the auricle morphology parameters, the broad auricle morphology database stored in the cloud database is matched to obtain matched broad auricle morphology data, and the similarity between each broad auricle morphology data and the target auricle image data is obtained respectively, among which the broad auricle morphology data with the greatest similarity is the auricle morphology data corresponding to the auricle morphology parameters.

[0048] The auricle model building module compares the auricle morphology data corresponding to the auricle morphology parameters received from the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, including:

[0049] The parameter difference between the auricle morphology data corresponding to the auricle morphology parameters and the standard normal auricle morphology parameters constitutes the auricle data model correction parameters.

[0050] The auricle model construction module constructs a target auricle data model according to the auricle data model correction parameters and the auricle standard data model, including:

[0051] The auricle model construction module corrects the auricle standard data model according to the auricle data model correction parameters to obtain the target auricle data model.

[0052] like Figure 2 As shown, an auricle data analysis system based on normal auricle morphology classification, applying the auricle data model construction method based on normal auricle morphology classification, comprises: a cloud data server, an auricle feature extraction module, a data processing module, a communication module, an auricle image data acquisition module and an auricle model construction module;

[0053] The communication module, auricle image data acquisition module, auricle feature extraction module and auricle model construction module are respectively connected to the data processing module, and the cloud data server is communicatively connected to the communication module.

[0054] like Figure 3 As shown, the auricle image data acquisition module includes an auricle image acquisition module, an image data processing module and an image data storage device; the auricle image acquisition module is connected to the image data processing module, and the image data processing module and the image data storage device are respectively connected to the data processing module.

[0055] Specifically, the present invention proposes a method for constructing an auricle data model based on the classification of normal auricle morphology. The method integrates advanced technologies such as image processing, feature extraction, data analysis and modeling, aiming to achieve efficient and accurate modeling of auricle morphology. The specific steps are as follows:

[0056] Step 1: Preprocessing and validity judgment of auricle image data

[0057] Auricle image data reception: The auricle image data acquisition module obtains the image data of the target auricle through a high-resolution camera or other image capture device, and transmits it to the auricle feature extraction module.

[0058] Feature extraction and validity judgment: After receiving the target auricle image data, the auricle feature extraction module first performs image preprocessing, including denoising, contrast enhancement, etc., to improve image quality. Subsequently, the auricle validity features are extracted based on the target auricle image data. These features include but are not limited to the clarity of the auricle image data, the integrity of the auricle image, and the angle and distance between the auricle and the head. The clarity is evaluated by calculating the edge sharpness or frequency domain energy distribution of the image; the integrity is judged by detecting the continuity and integrity of the auricle contour in the image; the angle and distance are obtained by analyzing the geometric features in the image.

[0059] Validity decision: According to the preset clarity threshold and integrity threshold, if the clarity of the auricle image data is not less than the set clarity threshold and the integrity of the auricle image is not less than the set integrity threshold, the target auricle image data is judged to be valid and proceed to step 2; otherwise, it is judged to be invalid, the target auricle image data is recollected, and sent to the auricle feature extraction module for verification again.

[0060] Step 2: Identify the auricle type

[0061] Determination of auricle type: Based on the angle and distance characteristics between the auricle and the skull extracted in step 1, the auricle is compared with the preset normal auricle standard range. If these parameters are within the normal range, the auricle is determined to be normal and the process goes to step 3; otherwise, the auricle is determined to be abnormal and the process goes to step 4.

[0062] Step 3: Comparison and correction of normal auricle morphological parameters

[0063] Morphological parameter extraction: The auricle feature extraction module further extracts detailed auricle morphological parameters, such as the specific size and shape characteristics of the auricle, from the valid normal auricle image data.

[0064] Parameter comparison: The extracted auricle morphology parameters are compared with the standard normal auricle morphology parameters stored in the cloud data server, and the difference between the parameters is calculated.

[0065] Correction parameter generation: Generate auricle data model correction parameters based on parameter differences for adjustment during subsequent model construction.

[0066] Step 4: Matching and Correcting Abnormal Auricle Shape

[0067] Extensive morphological data matching: For abnormal auricle morphology, the auricle feature extraction module matches the extracted morphological parameters with extensive auricle morphological data stored in the cloud database. The matching process is based on feature similarity calculations, such as shape context, Euclidean distance, etc.

[0068] Best match selection: From the matched wide range of auricle morphology data, the one with the highest similarity to the target auricle image data is selected as the reference data.

[0069] Correction parameter generation: The selected reference data is compared with the standard normal auricle morphology parameters to generate correction parameters for abnormal auricle morphology.

[0070] Step 5: Abnormal auricle morphology data comparison and secondary correction

[0071] This step is a further refinement of step 4 to ensure that the correction of abnormal auricle morphology is more accurate. The auricle model building module receives the matching auricle morphology data sent by the cloud database and compares it with the standard normal auricle morphology parameters again to verify and optimize the correction parameters.

[0072] Step 6: Building the auricle data model

[0073] Model correction: The auricle model building module corrects the auricle standard data model according to the auricle data model correction parameters generated in step 3 or step 5. The correction process may involve operations such as geometric transformation and shape adjustment.

[0074] Model generation: The calibrated standard auricle data model is the target auricle data model, which can accurately reflect the morphological characteristics of the target auricle and provide a basis for subsequent applications.

[0075] System Configuration

[0076] In order to implement the above method, the present invention also designs an auricle data analysis system based on normal auricle morphology classification, the system comprising:

[0077] Cloud data server: stores standard normal auricle morphology parameters, extensive auricle morphology database and algorithm models, providing data support and computing resources for the system.

[0078] Auricle feature extraction module: responsible for extracting validity features and morphological parameters from auricle image data, and is the core of system identification and analysis.

[0079] Data processing module: coordinates data transmission and processing between modules to ensure efficient operation of the system.

[0080] Communication module: realizes data exchange between the system and the cloud data server to ensure the real-time and accuracy of information.

[0081] Auricle image data acquisition module: responsible for collecting the image data of the target auricle and is the starting point of the system input.

[0082] Auricle model construction module: Constructing the target auricle data model based on the correction parameters and the auricle standard data model is the key to the system output.

[0083] Example 1: Application in personalized earphone customization

[0084] As consumers' demands for sound quality, wearing comfort and personalized appearance of headphones grow, traditional headphone design and manufacturing methods can no longer meet market demand. In particular, the wearing comfort and sound quality of earbud and in-ear headphones depend largely on the fit between the headphones and the auricle. Therefore, personalized headphone customization based on individual auricle morphology has become a new trend in the industry.

[0085] Implementation process: Auricular image acquisition: A specially designed auricular image acquisition device is used, which integrates a high-resolution camera and a positioning sensor to quickly and accurately capture 3D image data of the user's ears.

[0086] Auricle feature extraction and validity judgment: The collected auricle image data is transmitted to the auricle feature extraction module, which first performs image preprocessing to remove noise and enhance image details. Subsequently, the validity features of the auricle are extracted, including clarity, integrity, and the angle and distance between the auricle and the head to ensure the validity of the image data.

[0087] Auricle type identification and morphological parameter extraction: For auricle image data that is determined to be valid, the system further identifies the auricle type (normal or abnormal). For normal auricle morphology, the system extracts detailed morphological parameters, such as auricle contour, ear canal entrance position, concha cavity size, etc.

[0088] Model correction and personalized earphone design: The extracted auricle morphology parameters are compared with the standard normal auricle morphology parameters in the cloud data server to generate correction parameters. The auricle model construction module adjusts the auricle standard data model according to the correction parameters to obtain an accurate target auricle data model. Based on this model, designers can design personalized earphones that fit the user's auricle perfectly to ensure the best wearing comfort and sound quality performance.

[0089] Headphone manufacturing and delivery: After the design is completed, personalized headphones are manufactured through 3D printing or other advanced manufacturing technologies and delivered to users.

[0090] Effect: Through this embodiment, users can obtain personalized headphones that fully match their auricle shape, which not only improves wearing comfort but also optimizes the sound quality experience. At the same time, personalized customization also meets the user's unique needs for the appearance of headphones and improves the market competitiveness of the product.

[0091] Example 2: Application in audiology research

[0092] Audiology is a discipline that studies the function of the auditory system and its disorders. The relationship between auricle morphology and hearing function is one of the important contents of audiology research. However, due to the differences in auricle morphology between individuals, traditional research methods often find it difficult to obtain accurate and reliable data.

[0093] Subject recruitment and auricle image acquisition: A certain number of subjects were recruited, and 3D images of both ears of each subject were acquired using a high-precision auricle image acquisition device.

[0094] Auricle feature extraction and classification: Extract features from the collected auricle image data, including the validity features and morphological parameters of the auricle. According to the morphological parameters, the subjects were divided into a normal auricle morphology group and an abnormal auricle morphology group.

[0095] Auricle data model construction: For each group of subjects, an auricle data model was constructed. For the normal auricle morphology group, the extracted morphological parameters were directly compared with the standard normal auricle morphological parameters to generate correction parameters and construct the model; for the abnormal auricle morphology group, the reference data with the highest similarity was selected by matching the extensive auricle morphology database, and the model was constructed after secondary correction.

[0096] Auditory function test and data analysis: Each subject was given an auditory function test, including hearing threshold test, acoustic immittance test, etc. The test results were correlated with the constructed auricle data model to explore the relationship between auricle morphology and auditory function.

[0097] Summary and publication of research results: Based on the data analysis results, summarize the influence of auricle morphology on auditory function, write a research report and publish it in a professional journal, providing a new perspective and basis for audiology research.

[0098] Effect: Through this embodiment, researchers can obtain a large amount of accurate and reliable auricle morphology and hearing function data, which provides strong support for in-depth exploration of the relationship between auricle morphology and hearing function. At the same time, the publication of research results also promotes academic exchanges and cooperation in the field of audiology and promotes the further development of the discipline.

Claims

1. A method for constructing an auricle data model based on normal auricle morphology classification, characterized in that: An auricle data analysis system applied to normal auricle morphology classification, comprising: a cloud data server, an auricle feature extraction module, a data processing module, a communication module, an auricle image data acquisition module and an auricle model construction module; The communication module, auricle image data acquisition module, auricle feature extraction module and auricle model construction module are respectively connected to the data processing module, and the cloud data server is communicatively connected to the communication module; It also includes an auditory function test and data analysis module, which is used to perform an auditory function test on each subject, including a hearing threshold test and an acoustic immittance test; and to perform correlation analysis between the test results and the constructed auricle data model to obtain the relationship between the auricle morphology and the auditory function; The steps include: Step 1: The auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and judges the validity of the target auricle image data according to the auricle validity feature. If it is valid, proceed to step 2; if it is invalid, re-collect the target auricle image data and send it to the auricle feature extraction module; Step 2, obtaining the auricle type in the auricle image data, if it is a normal auricle shape, proceed to step 3; if it is an abnormal auricle shape, proceed to step 4; Step 3: The auricle feature extraction module obtains the associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with the standard normal auricle morphological parameters in the cloud data server, obtains the auricle data model correction parameters, and proceeds to step 6; Step 4: The auricle feature extraction module obtains auricle morphological parameters associated with the auricle validity feature, and matches the auricle morphological parameters with extensive auricle morphological data stored in the cloud database to obtain auricle morphological data corresponding to the auricle morphological parameters. The cloud database sends the auricle morphological data corresponding to the auricle morphological parameters to the auricle model construction module, and then proceeds to step 5; Step 5: The auricle model building module compares the auricle morphology data corresponding to the auricle morphology parameters sent by the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, and then proceeds to step 6; Step 6: The auricle model building module builds a target auricle data model according to the auricle data model correction parameters and the auricle standard data model; The auricle feature extraction module receives the target auricle image data, extracts the corresponding auricle validity feature according to the target auricle image data, and determines the validity of the target auricle image data according to the auricle validity feature, including: The auricle validity features include the clarity of the auricle image data, the integrity of the auricle image, and the angle and distance between the auricle and the skull. If the clarity of the auricle image data is not less than the set clarity threshold and the integrity of the auricle image is not less than the set integrity threshold, the target auricle image data is valid; The method of obtaining the auricle type in the auricle image data includes: If the angle and distance between the auricle and the skull are within the preset normal auricle standard range, it is a normal auricle shape; otherwise, it is an abnormal auricle shape; The auricle feature extraction module obtains the associated auricle morphological parameters according to the auricle validity features, compares the auricle morphological parameters with the standard normal auricle morphological parameters in the cloud data server, and obtains the auricle data model correction parameters, including: The auricle morphology parameters include the angle and distance between the auricle and the skull, and the parameter difference between the corresponding parameters in the standard normal auricle morphology parameters in the cloud data server is obtained, and the parameter difference constitutes the auricle data model correction parameter; The auricle morphology parameters are matched with a wide auricle morphology database stored in a cloud database to obtain auricle morphology data corresponding to the auricle morphology parameters, including: According to the angle and distance between the auricle and the head in the auricle morphology parameters, the broad auricle morphology database stored in the cloud database is matched to obtain matched broad auricle morphology data, and the similarity between each broad auricle morphology data and the target auricle image data is obtained respectively, wherein the broad auricle morphology data with the greatest similarity is the auricle morphology data corresponding to the auricle morphology parameters; the auricle model construction module compares the auricle morphology data corresponding to the auricle morphology parameters sent by the cloud database with the standard normal auricle morphology parameters in the cloud data server to obtain the auricle data model correction parameters, including: The parameter difference between the auricle morphology data corresponding to the auricle morphology parameters and the standard normal auricle morphology parameters constitutes the auricle data model correction parameters; The auricle model construction module constructs a target auricle data model according to the auricle data model correction parameters and the auricle standard data model, including: The auricle model construction module corrects the auricle standard data model according to the auricle data model correction parameters to obtain the target auricle data model.

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