A traditional chinese medicine diagnosis face division method and system based on big data

Abstract

The application discloses a Chinese medicine face diagnosis face division method and system based on big data; the application discloses a modular face diagnosis division model constructed after face preprocessing based on Chinese medicine theory, different faces are divided according to the modular face image features after preprocessing by the trained model, and the face color part or the face gas part is divided respectively by dividing the face image based on Chinese medicine theory. The modular face diagnosis division model built by the application reduces the misdiagnosis rate caused by the subjective diagnosis method of face color, and improves the efficiency of the face color and face gas division diagnosis by using the improved model based on Chinese medicine big data.

Description

Technical Field

[0001] This invention belongs to the field of intelligent medical image processing technology, and in particular relates to a method and system for facial segmentation in traditional Chinese medicine based on big data observation. Background Technology

[0002] The four diagnostic methods of Traditional Chinese Medicine (TCM) are observation, auscultation, inquiry, and palpation. Observation refers to observing the complexion; auscultation to listening to sounds; inquiry to asking about symptoms; and palpation to feeling the pulse. These are collectively known as the four diagnostic methods. Observation is the first of the four, and its main components are face diagnosis and tongue diagnosis.

[0003] Facial observation refers to assessing the health of a person and their internal organs by observing changes in the complexion and color of their facial features, thus providing a basis for diagnosis and treatment. Traditional Chinese medicine views the human body as an organic whole, considering the five internal organs as the center, meridians as transmission channels, and qi and blood as the medium connecting the organs, tissues, skin, senses, and limbs. The face and internal organs are seen as having an invisible reflection; by observing changes in the complexion and color of the facial features, one can indirectly understand the condition of the five internal organs.

[0004] Dong Mengqing et al. used a digital detection instrument for TCM facial diagnosis to analyze the facial color characteristics of 259 patients with chronic renal failure, 128 patients with coronary heart disease, and 232 patients with chronic hepatitis B. The results showed that there were significant differences in facial color index among the three diseases.

[0005] The existing TCM facial diagnosis equipment - the TCM Four Diagnosis Instrument - integrates a large number of modern technological achievements and the clinical experience of many TCM experts, combining TCM tongue diagnosis, facial diagnosis, pulse diagnosis and questioning. The TCM Four Diagnosis Instrument facial diagnosis requires the acquisition of a facial image, which includes the face, hair and environmental background. It is necessary to extract the face part of the image or remove the hair and environmental background.

[0006] The disadvantages of existing technology are:

[0007] 1. Nowadays, traditional Chinese medicine diagnoses patients' conditions by subjectively observing their facial color. However, since the doctor's observation of facial color is subjective, inaccurate diagnostic results may occasionally occur.

[0008] 2. In addition, existing facial diagnostic models mostly use multiple models to process image features to obtain an overall facial segmentation image, which makes the detection process cumbersome and the equipment structure complex.

[0009] 3. In existing technologies, facial segmentation images only divide the face into areas such as the Mingtang, Que, Ting, Fan, and Bi, rather than using the five-color diagnosis method in traditional Chinese medicine to differentiate between observing the qi or observing the color of the face, resulting in inaccurate diagnosis.

[0010] Therefore, it is necessary to redesign a traditional Chinese medicine facial diagnosis method and system based on big data to address the existing problems. Summary of the Invention

[0011] To address the aforementioned technical problems, this invention proposes a method and system for facial segmentation in traditional Chinese medicine based on big data observation.

[0012] In a first aspect of the present invention, a method for facial segmentation in traditional Chinese medicine based on big data observation is provided, characterized by comprising the following steps:

[0013] A1. Obtain the first color image of the face;

[0014] A2. Based on the preprocessing of the first face color image, a first dark module image and a first light module image are obtained, and the first dark region face segmentation method and the first light region face segmentation method are respectively acquired from the first dark module image and the first light module image.

[0015] A3. Construct a modular facial diagnosis segmentation model based on the first input features composed of the first dark module image and the first light module image, and the first output features composed of the first dark region facial segmentation method and the first light region facial segmentation method.

[0016] A4. Receive the second face color image, and generate a second output feature composed of the second dark region face segmentation method and the second light region face segmentation method according to the modular facial diagnosis segmentation model.

[0017] A5. Based on the second output feature composed of the second dark area facial segmentation method and the second light area facial segmentation method, the face is divided into areas for visual observation or areas for visual assessment, assisting doctors in making visual diagnosis.

[0018] Furthermore, before the preprocessing of the first face color image to obtain the first dark module image and the first light module image, the first face color image or the second face color image is divided into five colors—green, red, yellow, white, and black—according to the five-color diagnosis theory of traditional Chinese medicine. Red and black are designated as dark colors, and green, white, and yellow are designated as light colors. The color gamut feature values ​​for dark colors are then defined. Calculation formula and color gamut feature value set as light color .

[0019] Furthermore, the modular facial diagnosis segmentation model is constructed using deep learning methods.

[0020] Furthermore, red and black are defined as dark colors, cyan, white and yellow are defined as light colors, and the variance F of the color intensity values ​​between dark and light colors is defined.

[0021] Furthermore, the color intensity value variance F is used to distinguish between dark and light colors. Regions where the average intensity value of the RGB three color channels in the first or second face color image is greater than the color intensity value variance F are considered light-colored regions, and regions where the average intensity value of the RGB three color channels in the first or second face color image is less than the color intensity value variance F are considered dark-colored regions. The color gamut feature value of the light-colored region... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ).

[0022] Furthermore, the deep learning method employs an improved convolutional neural network.

[0023] A facial segmentation system based on big data in traditional Chinese medicine is also provided, characterized in that the system includes:

[0024] Color face image acquisition module: acquires a first color face image; also acquires a second color face image;

[0025] Facial image color preprocessing module: Based on the preprocessing of the first face color image, a first dark module image and a first light module image are obtained, and the first dark region facial segmentation method and the first light region facial segmentation method are respectively acquired from the first dark module image and the first light module image.

[0026] Modular facial diagnosis segmentation model construction module: Construct a modular facial diagnosis segmentation model based on the first input features composed of the first dark module image and the first light module image, and the first output features composed of the corresponding first dark region facial segmentation method and the first light region facial segmentation method;

[0027] The facial segmentation processing module for visual diagnosis receives a second color image of a face and generates a second output feature composed of a second dark region facial segmentation method and a second light region facial segmentation method according to the modular facial visual diagnosis segmentation model. Based on the second output feature composed of the second dark region facial segmentation method and the second light region facial segmentation method, the module divides the face into areas for visual diagnosis or areas for visual aura analysis to assist doctors in making visual diagnosis judgments.

[0028] Furthermore, before the preprocessing of the first face color image to obtain the first dark module image and the first light module image, the first face color image or the second face color image is divided into five colors—green, red, yellow, white, and black—according to the five-color diagnosis theory of traditional Chinese medicine. Red and black are designated as dark colors, and green, white, and yellow are designated as light colors. The color gamut feature values ​​for dark colors are then defined. and color gamut feature values ​​set to light colors .

[0029] Furthermore, red and black are defined as dark colors, cyan, white and yellow are defined as light colors, and the variance F of the color intensity values ​​between dark and light colors is defined.

[0030] The color intensity variance F is used to distinguish between dark and light colors. Regions where the average intensity value of the RGB channels in either the first or second face color image is greater than the color intensity variance F are considered light-colored regions, and regions where the average intensity value of the RGB channels is less than the color intensity variance F are considered dark-colored regions. The color gamut characteristic value of the light-colored region... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ).

[0031] Furthermore, the modular facial diagnosis segmentation model is constructed using a deep learning method, which employs an improved convolutional neural network.

[0032] This invention not only utilizes the subjective observation of a patient's facial color in Traditional Chinese Medicine (TCM) but also incorporates image processing technology to segment the patient's face for diagnosis, reducing the likelihood of diagnostic errors. Furthermore, the facial diagnosis model employs image feature preprocessing based on the TCM five-color theory to obtain an overall facial segmentation image before comprehensive facial segmentation, reducing the complexity of subsequent feature recognition. Only a client capable of image recognition needs to process the data to obtain a facial segmentation method for different areas of the face, thus more accurately distinguishing between observing the "qi" (vital energy) and observing the "color" (facial complexion) in TCM's five-color diagnosis. The facial feature segmentation required for TCM facial diagnosis utilizes a convolutional neural network with an improved activation function, making the model based on large-scale facial data processing more accurate and reliable.

[0033] Further embodiments and improvements of the present invention will be described in conjunction with the accompanying drawings and specific examples. Attached Figure Description

[0034] Figure 1 This is a flowchart of a traditional Chinese medicine facial segmentation method based on big data observation according to the present invention;

[0035] Figure 2 This is a schematic diagram of a traditional Chinese medicine facial segmentation system based on big data observation according to the present invention;

[0036] Figure 3 This is a schematic diagram illustrating the division of dark and light regions in an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram illustrating the basic principle of the convolutional neural network in the embodiments of this invention;

[0038] Figure 5 This is a schematic diagram of an electronic device structure for implementing the method of the present invention in an embodiment of the present invention. Detailed Implementation

[0039] The invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0040] To address the aforementioned technical problems, this invention provides a method and system for facial segmentation in traditional Chinese medicine based on big data observation.

[0041] In a first aspect of the present invention, a method for facial segmentation in traditional Chinese medicine based on big data observation is provided, characterized by comprising the following steps:

[0042] A1. Obtain the first color image of the face;

[0043] A2. Based on the preprocessing of the first face color image, a first dark module image and a first light module image are obtained, and the first dark region face segmentation method and the first light region face segmentation method are respectively acquired from the first dark module image and the first light module image.

[0044] A3. Construct a modular facial diagnosis segmentation model based on the first input features composed of the first dark module image and the first light module image, and the first output features composed of the first dark region facial segmentation method and the first light region facial segmentation method.

[0045] A4. Receive the second face color image, and generate a second output feature composed of the second dark region face segmentation method and the second light region face segmentation method according to the modular facial diagnosis segmentation model.

[0046] A5. Based on the second output feature composed of the second dark area facial segmentation method and the second light area facial segmentation method, the face is divided into areas for visual observation or areas for visual assessment, assisting doctors in making visual diagnosis.

[0047] Furthermore, before the preprocessing of the first face color image to obtain the first dark module image and the first light module image, the first face color image or the second face color image is divided into five colors—green, red, yellow, white, and black—according to the five-color diagnosis theory of traditional Chinese medicine. Red and black are designated as dark colors, and green, white, and yellow are designated as light colors. The color gamut feature values ​​for dark colors are then defined. Calculation formula and color gamut feature value set as light color The calculation formula is:

[0048]

[0049]

[0050] In the formula, , , These are the gamut weight values ​​for the three color channels RGB, where R, G, and B are the intensity characteristic values ​​of the red, green, and blue color channels. This indicates the area of ​​the face occupied by darker colors. The area of ​​the face represented by the lighter color is S, where S represents the face area. In this embodiment... , , The values ​​are 0.34, 0.36, and 0.40.

[0051] The face area is obtained through a general face recognition system, where the dark and light areas are obtained after preprocessing and segmentation.

[0052] In this embodiment, the values ​​of R, G, and B range from 0 to 255. The RGB values ​​for pure cyan are (0, 255, 255), while the values ​​of G and B generally fluctuate within a certain range. The RGB values ​​for pure red are (255, 0, 0). If we distinguish them by pure color, we will miss the corresponding color system image features. Therefore, in this embodiment, we only distinguish between dark and light colors based on the variance F of the color intensity value.

[0053] Furthermore, the modular facial diagnosis segmentation model is constructed using deep learning methods.

[0054] Furthermore, red and black are defined as dark colors, and cyan, white, and yellow are defined as light colors. The variance F of the color intensity values ​​between dark and light colors is defined as follows:

[0055]

[0056] In the formula, g is the average intensity value of the RGB three color channels of the first or second face color image. The mean value of the R intensity value of the color channel in either the first or second face color image. The average intensity value of the color channel G in either the first or second face color image.

[0057] Furthermore, the color intensity value variance F is used to distinguish between dark and light colors. Regions where the average intensity value of the RGB three color channels in the first or second face color image is greater than the color intensity value variance F are considered light-colored regions, and regions where the average intensity value of the RGB three color channels in the first or second face color image is less than the color intensity value variance F are considered dark-colored regions. The color gamut feature value of the light-colored region... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ).

[0058] Activation functions are an extremely important feature of artificial neural networks. They determine whether a neuron should be activated. Activation means that the information received by the neuron is related to the given information. Activation functions perform nonlinear transformations on the input information and then pass the transformed output information as input information to the next layer of neurons. Therefore, activation functions are very important to neural networks.

[0059] Furthermore, the deep learning method employs an improved convolutional neural network with an activation function... The calculation formula is as follows:

[0060]

[0061] x is either the first input feature or the second input feature. This indicates the area of ​​the face occupied by darker colors. This indicates the area of ​​the face occupied by lighter colors. For dark color gamut feature values, The color gamut feature value is for light colors.

[0062] The output features derived from the convolutional neural network model with the improved activation function in this embodiment are used to classify facial features into areas for visual observation or areas for visual assessment, assisting doctors in making diagnostic judgments through observation.

[0063] In this embodiment, the corresponding dark region face segmentation method and light region face segmentation method are performed based on the output features of the output layer of the final improved convolutional neural network. Specifically, the final output layer vector is (0, 1) or (1, 0) or (0, 0) or (1, 1). When the output layer vector is (0, 1) or (1, 0), it is the dark region face segmentation method, that is, the face is segmented by color. If the final output layer vector is (0, 0), the face needs to be segmented by aura. If the final output layer vector is (1, 1), it is necessary to not only segment the face by aura but also segment the face by color.

[0064] A facial segmentation system based on big data in traditional Chinese medicine is also provided, characterized in that the system includes:

[0065] Color face image acquisition module: acquires a first color face image; also acquires a second color face image;

[0066] Facial image color preprocessing module: Based on the preprocessing of the first face color image, a first dark module image and a first light module image are obtained, and the first dark region facial segmentation method and the first light region facial segmentation method are respectively acquired from the first dark module image and the first light module image.

[0067] Modular facial diagnosis segmentation model construction module: Construct a modular facial diagnosis segmentation model based on the first input features composed of the first dark module image and the first light module image, and the first output features composed of the corresponding first dark region facial segmentation method and the first light region facial segmentation method;

[0068] The facial segmentation processing module for visual diagnosis receives a second color image of a face and generates a second output feature composed of a second dark region facial segmentation method and a second light region facial segmentation method according to the modular facial visual diagnosis segmentation model. Based on the second output feature composed of the second dark region facial segmentation method and the second light region facial segmentation method, the module divides the face into areas for visual diagnosis or areas for visual aura analysis to assist doctors in making visual diagnosis judgments.

[0069] Furthermore, before the preprocessing of the first face color image to obtain the first dark module image and the first light module image, the first face color image or the second face color image is divided into five colors—green, red, yellow, white, and black—according to the five-color diagnosis theory of traditional Chinese medicine. Red and black are designated as dark colors, and green, white, and yellow are designated as light colors. The color gamut feature values ​​for dark colors are then defined. Calculation formula and color gamut feature value set as light color The calculation formula is:

[0070]

[0071]

[0072] In the formula, , , These are the gamut weight values ​​for the three color channels RGB, where R, G, and B are the intensity characteristic values ​​of the red, green, and blue color channels. This indicates the area of ​​the face occupied by darker colors. The area represented by the light color is the face area, and S represents the face area.

[0073] Furthermore, red and black are defined as dark colors, and cyan, white, and yellow are defined as light colors. The variance F of the color intensity values ​​between dark and light colors is defined as follows:

[0074]

[0075] In the formula, g is the average intensity value of the RGB three color channels of the first or second face color image. The mean value of the R intensity value of the color channel in either the first or second face color image. The average intensity value of the color channel G in either the first or second face color image;

[0076] The color intensity variance F is used to distinguish between dark and light colors. Regions where the average intensity value of the RGB channels in either the first or second face color image is greater than the color intensity variance F are considered light-colored regions, and regions where the average intensity value of the RGB channels is less than the color intensity variance F are considered dark-colored regions. The color gamut characteristic value of the light-colored region... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ).

[0077] Furthermore, the modular facial diagnosis segmentation model is constructed using a deep learning method, which employs an improved convolutional neural network with a specific activation function. The calculation formula is as follows:

[0078]

[0079] x is either the first input feature or the second input feature. This indicates the area of ​​the face occupied by darker colors. This indicates the area of ​​the face occupied by lighter colors. For dark color gamut feature values, The color gamut feature value is for light colors.

[0080] This invention not only utilizes the subjective observation of a patient's facial color in Traditional Chinese Medicine (TCM) but also incorporates image processing technology to segment the patient's face for diagnosis, reducing the likelihood of diagnostic errors. Furthermore, the facial diagnosis model employs image feature preprocessing based on the TCM five-color theory to obtain an overall facial segmentation image before comprehensive facial segmentation, reducing the complexity of subsequent feature recognition. Only a client capable of image recognition needs to process the data to obtain a facial segmentation method for different areas of the face, thus more accurately distinguishing between observing the "qi" (vital energy) and observing the "color" (facial complexion) in TCM's five-color diagnosis. The facial feature segmentation required for TCM facial diagnosis utilizes a convolutional neural network with an improved activation function, making the model based on large-scale facial data processing more accurate and reliable.

[0081] For any module structures not specifically defined in this invention, the existing technical specifications shall prevail. The existing technical specifications mentioned in the foregoing background and specific embodiments sections are considered part of this invention and are used to understand the meaning of certain technical features or parameters. The scope of protection of this invention is determined by the actual contents of the claims.

Claims

1. A method for facial segmentation in Traditional Chinese Medicine based on big data observation, characterized in that, Includes the following steps: A1. Obtain the first color image of the face; A2. Based on the preprocessing of the first facial color image, a first dark module image and a first light module image are obtained. The first dark region facial segmentation method and the first light region facial segmentation method are then collected from the first dark module image and the first light module image, respectively. The preprocessing divides the facial color image into five colors—green, red, yellow, white, and black—according to the Five Color Diagnosis Theory of Traditional Chinese Medicine. Red and black are designated as dark colors, and green, white, and yellow are designated as light colors. The color gamut feature values ​​for dark colors are then defined. and color gamut feature values ​​set to light colors The calculation formula is: ； ； In the formula, k1, k2, and k3 are the gamut weight values ​​for the three color channels RGB, and R, G, and B are the intensity characteristic values ​​of the red, green, and blue color channels. This indicates the area of ​​the face occupied by dark colors. Let S represent the area of ​​the face occupied by the light-colored part, and k1, k2, and k3 take values ​​of 0.34, 0.36, and 0.40, respectively. A3. A modular facial diagnosis segmentation model is constructed based on the first input features composed of the first dark module image and the first light module image, and the first output features composed of the corresponding first dark region facial segmentation method and the first light region facial segmentation method; the modular facial diagnosis segmentation model is constructed using deep learning methods. The deep learning method employs an improved convolutional neural network, whose activation function... The calculation formula is as follows: ； x is either the first input feature or the second input feature. This indicates the area of ​​the face occupied by dark colors. This indicates the area of ​​the face occupied by lighter colors. For dark color gamut feature values, The color gamut feature value is for light colors; A4. Receive the second face color image, and generate a second output feature composed of the second dark region face segmentation method and the second light region face segmentation method according to the modular facial diagnosis segmentation model. A5. Based on the second output feature composed of the second dark area facial segmentation method and the second light area facial segmentation method, the face is divided into areas for visual observation or areas for visual assessment, to assist doctors in making visual diagnosis. Red and black are defined as dark colors, and cyan, white, and yellow are defined as light colors. The variance F of the color intensity values ​​between dark and light colors is defined as follows: ； In the formula, g is the average intensity value of the RGB three color channels of the first or second face color image. The mean value of the R intensity value of the color channel in either the first or second face color image. The average intensity value of the color channel G in the first or second face color image; The color intensity variance F is used to distinguish between dark and light colors. Regions where the average intensity value of the RGB channels in either the first or second face color image is greater than the color intensity variance F are considered light-colored regions, and regions where the average intensity value of the RGB channels is less than the color intensity variance F are considered dark-colored regions. The color gamut characteristic value of the light-colored region... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ); Based on the output features of the final improved convolutional neural network output layer, corresponding methods for dark-region face segmentation and light-region face segmentation are performed. Specifically, the final output layer vector is (0, 1) or (1, 0) or (0, 0) or (1, 1). If the output layer vector is (0, 1) or (1, 0), it is a dark-region face segmentation method, which is to perform color segmentation on the face. If the final output layer vector is (0, 0), it is necessary to perform aura segmentation on the face. If the final output layer vector is (1, 1), it is necessary to perform both aura segmentation and color segmentation on the face.

2. A facial segmentation system for traditional Chinese medicine diagnosis based on big data, characterized in that, The system includes: Color face image acquisition module: acquires a first color face image; also acquires a second color face image; Facial image color preprocessing module: Based on the preprocessing of the first face color image, a first dark module image and a first light module image are obtained, and the first dark region facial segmentation method and the first light region facial segmentation method are respectively acquired from the first dark module image and the first light module image. The preprocessing method divides the color image of the face into five colors—cyan, red, yellow, white, and black—based on the five-color diagnosis theory of traditional Chinese medicine. Red and black are set as dark colors, while cyan, white, and yellow are set as light colors. The color gamut feature values ​​set as dark colors are... and color gamut feature values ​​set to light colors The calculation formula is: ； ； In the formula, k1, k2, and k3 are the gamut weight values ​​for the three color channels RGB, and R, G, and B are the intensity characteristic values ​​of the red, green, and blue color channels. This indicates the area of ​​the face occupied by dark colors. Let S represent the area of ​​the face occupied by the light-colored part, and k1, k2, and k3 take values ​​of 0.34, 0.36, and 0.40, respectively. Modular facial diagnosis segmentation model construction module: A modular facial diagnosis segmentation model is constructed based on the first input features composed of the first dark-colored module image and the first light-colored module image, and the corresponding first output features composed of the first dark-colored region facial segmentation method and the first light-colored region facial segmentation method. The modular facial diagnosis segmentation model is constructed using a deep learning method, specifically an improved convolutional neural network with a specific activation function. The calculation formula is as follows: ； In the formula, x is either the first input feature or the second input feature. This indicates the area of ​​the face occupied by dark colors. This indicates the area of ​​the face occupied by lighter colors. For dark color gamut feature values, The color gamut feature value is for light colors; The facial segmentation processing module for visual diagnosis: receives a second color image of a face, generates a second output feature composed of a second dark region facial segmentation method and a second light region facial segmentation method according to the modular facial visual diagnosis segmentation model; and divides the face into visual color areas or visual qi areas according to the second output feature composed of the second dark region facial segmentation method and the second light region facial segmentation method to assist doctors in visual diagnosis. Red and black are defined as dark colors, and cyan, white, and yellow as light colors. The variance F of the color intensity values ​​between dark and light colors is defined. Dark and light colors are distinguished using this variance F. Regions where the average intensity value of the RGB channels in either the first or second face color image is greater than the variance F are considered light regions, and regions where the average intensity value of the RGB channels is less than the variance F are considered dark regions. The color gamut characteristic value of the light regions is... and the color gamut characteristic values ​​of dark colors The input features P are obtained by combining them, and the vector representation of P is P( , ); Based on the output features of the final improved convolutional neural network output layer, corresponding methods for dark-region face segmentation and light-region face segmentation are performed. Specifically, the final output layer vector is (0, 1) or (1, 0) or (0, 0) or (1, 1). If the output layer vector is (0, 1) or (1, 0), it is a dark-region face segmentation method, which is to perform color segmentation on the face. If the final output layer vector is (0, 0), it is necessary to perform aura segmentation on the face. If the final output layer vector is (1, 1), it is necessary to perform both aura segmentation and color segmentation on the face.

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