Acupuncture positioning method based on image processing

Abstract

The invention discloses an acupuncture positioning method based on image processing, and relates to the technical field of traditional Chinese medicine, and the method comprises the steps: carrying out the feature recognition of image data through constructing two neural network models, and recognizing a feature point location, an acupoint, related information and a correction coefficient; a standard database is constructed, and a position tree structure is adopted for classification; quick comparison can be carried out when image data is input into the model, the operand is reduced, and the recognition accuracy is improved; the acupuncture point position can be directly marked in the human body in cooperation with laser or projection equipment, and a doctor only needs to conduct acupuncture treatment according to the acupuncture point position.

Description

Technical Field

[0001] The invention relates to the technical field of traditional Chinese medicine, and in particular to an acupuncture positioning method based on image processing. Background Art

[0002] Acupuncture is a general term for acupuncture and moxibustion.

[0003] Acupuncture refers to inserting needles (usually filiform needles) into the patient's body at a certain angle under the guidance of traditional Chinese medicine theory, and using acupuncture techniques such as twisting and lifting to stimulate specific parts of the human body to achieve the purpose of treating diseases. The insertion points are called human acupoints, or acupoints for short. According to the latest acupuncture textbooks, there are 361 regular acupoints in the human body; therefore, how to accurately determine the specific location of acupoints is an essential skill for acupuncture; bone measurement is a common positioning method in traditional Chinese medicine, and this method can be used for positioning regardless of whether the patient is male or female, young or old, tall or short, fat or thin; in the process of learning acupoints, it is generally based on the doctor's experience and the use of a bone measurement ruler to assist in positioning; The existing technology mainly relies on the experience of traditional Chinese medicine practitioners and bone measurement rulers to assist in positioning; the precision and accuracy of positioning are greatly affected by the personal experience of traditional Chinese medicine practitioners; and with the development of big data and image recognition technology, computer image processing can be used to quickly identify acupoints, which can play an auxiliary role in the process of traditional Chinese medicine acupuncture; how to use big data technology to improve the accuracy of acupuncture positioning has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention is proposed in view of the above-mentioned background problems.

[0005] The problem to be solved by the present invention is how to use image recognition technology to accurately identify the location of acupuncture points from pictures.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: The first aspect of the present invention provides an acupuncture positioning method based on image processing, comprising the following specific steps: S1: Collect image data of acupuncture points in public databases; S2: preprocessing the collected image data; S3: constructing a first neural network model to perform feature recognition on the collected image information; S4: Establish a database of feature points and classify them according to different parts; S5: In the constructed database, identify and retain each joint point that has a corresponding point in the bone measurement method; S6: Based on the bone measurement method, the positioning joint point is used as the starting point or the end point to determine each preset route, and the correction coefficient k corresponding to these routes is calculated; S7: using the data of different sizes collected in step S1 as a training set and the size data of the standard model in the bone measurement method as a verification set to train the second neural network model; after the training is completed, the second neural network model outputs a prediction correction coefficient k1 between the data of different sizes and the standard model; S8: Collect image information of acupuncture sites, identify feature points through the first neural network model, and determine the prediction correction coefficient k1 through the second neural network model; classify the feature point database with different correction coefficients as identification points to build a tree-structured database; S9: According to the predicted correction coefficient k1 of the person to be tested and the positions of each positioning joint point, the specific position of each acupoint of the person to be tested is determined according to the positioning rules of the acupoints of the human body.

[0007] Preferably, the image data collected from the public database in step S1 includes data of different parts, different sizes or different models, as well as the specific location and physiological characteristics of each acupoint.

[0008] Preferably, in step S2, the collected data is subjected to data standardization, and image cleaning, image enhancement and segmentation processing steps are performed for subsequent feature recognition.

[0009] Preferably, in step S3, feature recognition is performed on the collected data based on a cascaded convolutional neural network model combined with the joint points in the bone measurement method, and the identified feature points are matched one-to-one with the joint points in the bone measurement method.

[0010] Preferably, in step S4, a data set with feature point coordinate data as elements is established for each different part.

[0011] Preferably, the correction coefficient in step S6 is calculated as follows: ; in, k is the correction factor; L a It is the length actually measured between the preset lines; L s is the standard length of the line in the standard model; based on this, the line between feature point a and feature point b A The standard length in the standard model is A 1 , then the line A The calculation formula is as follows: ; By calculating all the lines between different feature points, the data of all feature points in the image area can be obtained; Assume height H, then calculate the height of each part according to the bone measurement method: Head height: ; Upper body length: ; Leg length: ; chest circumference: ; waistline: ; Hips: ; Where: H h is the head height; H u is the length of the upper body; H l is the leg length; C c is the chest circumference, K c is the correction coefficient for individual differences in chest circumference, with a value of 1.5-2; C w is the waist circumference, K w is the correction coefficient for individual differences in waist circumference, with a value of 1.0-1.5; C h is the hip circumference, K h is the correction coefficient for individual differences in hip circumference, and its value is 1.5-2.0.

[0012] Preferably, the tree-structured database is searched by tree indexing; the database establishes identification codes according to the parent level, child level, and grandchild level; Among them, the parent level is classified and coded in order according to the acupuncture area; including the head, face, chest, abdomen, waist, inner side of the calf, and inner side of the foot; The child level is classified and coded in order according to the characteristic points in the corresponding area; the grandchild level is classified and coded in order according to the size of the prediction correction coefficient; When a new image is collected, the region to which it belongs is first determined, and the database of the corresponding region is searched; then, the feature points are identified, and the prediction correction coefficients k1 in 2-3 lines are calculated in chronological order according to the identified feature points; the data under the same prediction correction coefficient k1 in the corresponding database is retrieved, and this is used as a verification set, and the subsequent identified feature points are compared using random numbers. If the comparison is successful, the results are quickly output based on the feature points and acupoint locations recorded in the database; if the comparison is unsuccessful, more feature points are identified until all feature points in the image area are identified, a new prediction correction coefficient is generated, and the new prediction correction coefficient is entered into the database.

[0013] Preferably, the following formula is used to calculate the prediction correction coefficients in 2-3 lines according to the characteristic points: ; In the formula, k 11 、k 12 、k 1n are the prediction correction coefficients in the 1st, 2nd, and nth lines respectively; n is the total number of predicted lines; The calculated prediction correction coefficient k1 is traversed and searched with the data under several adjacent grandchildren codes in the database to improve the data comparison speed.

[0014] A second aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned acupuncture positioning method based on image processing when executing the computer program.

[0015] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned acupuncture positioning method based on image processing.

[0016] The beneficial effects of the present invention are: The present invention performs feature recognition on image data by constructing two neural network models, identifying feature points, acupoints, related information and correction coefficients; classifies by constructing a standard database and adopting a position tree structure; when the image data is input into the model, it can be quickly compared, reducing the amount of calculation and improving the accuracy of recognition; the present invention can directly mark the acupoint positions in the human body in conjunction with laser or projection equipment, and the doctor only needs to perform acupuncture treatment according to the acupoint positions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 Flow chart of the acupuncture positioning method based on image processing.

[0019] Figure 2 This is a schematic diagram of the locations of Ruzhong point and Tanzhong point. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1: Figure 1 As shown, the present invention provides an acupuncture positioning method based on image processing, comprising the following specific steps: S1: Collect image data of acupuncture points in public databases; In step S1, the image data collected from the acupoint database in the public platform includes data of different parts, different sizes or different models, as well as the specific location and physiological characteristics of each acupoint. For example, image data of acupoint data maps of different parts can be collected from the acupoint database of the Traditional Chinese Medicine Resource Network (www.tcmdoc.cn); S2: preprocessing the collected image data; In step S2, the collected data is subjected to data standardization, and image cleaning, image enhancement and segmentation processing steps are performed for subsequent feature recognition; S3: constructing a first neural network model to perform feature recognition on the collected image information; In step S3, feature recognition is performed on the collected data based on the cascaded convolutional neural network model combined with the joint points in the bone measurement method, and the identified feature points are matched one by one with the joint points in the bone measurement method. The cascaded convolutional neural network (Cascaded CNN) gradually and finely locates feature points through multiple cascaded CNN models; each cascaded CNN model further optimizes and adjusts the output of the previous model; the cascaded CNN model can gradually narrow the search range of feature points and improve the accuracy of positioning. At the same time, since each cascaded model is relatively lightweight, it can achieve a faster running speed while ensuring accuracy.

[0024] S4: Establish a database of feature points and classify them according to different parts; In step S4, a data set with feature point coordinate data as elements is established for each different part.

[0025] S5: In the constructed database, identify and retain the joint points corresponding to the points in the bone measurement method; the following table 1 is a commonly used bone measurement table; Table 2 is a table of locating joint points in the bone measurement table; Table 1 Common bone measurement table Location Start and end points Discount Metrics illustrate head From the middle of the front hairline to the middle of the back hairline 12 inches Direct measurement Used to determine the longitudinal distance of acupoints on the head From the middle of the eyebrow to the front hairline 3 inches Direct measurement Used to determine the longitudinal distance between the front or back hairline and its head acupoints Between the hairlines of the two temples 9 inches Horizontal amount Used to determine the lateral distance of acupoints on the front of the head Between the two mastoid processes behind the ear 9 inches Horizontal amount Used to determine the lateral distance of acupoints at the back of the head Chest, abdomen and flank From the suprasternal fossa to the midpoint of the sternocostal junction 9 inches Direct measurement Used to determine the longitudinal distance of the Renmai acupoints on the chest From the midpoint of the sternocostal junction to the center of the navel 8 inches Direct measurement Used to determine the longitudinal distance of acupoints in the upper abdomen From the middle of the umbilicus to the upper edge of the pubic symphysis 5 inches Direct measurement Used to determine the longitudinal distance of the lower abdominal acupoints Between the nipples 8 inches Horizontal amount Used to determine the lateral distance of chest and abdominal acupoints Back and waist From medial edge of scapula to posterior midline 3 inches Horizontal amount Used to determine the horizontal distance of the back and waist acupoints Upper limbs From the anterior and posterior axillary lines to the transverse lines of the elbow (at the level of the ulnar olecranon) 9 inches Direct measurement Used to determine the longitudinal distance of acupoints in the upper arm From the elbow crease (at the level of the olecranon) to the distal crease of the palmar (dorsal) side of the wrist 12 inches Direct measurement Used to determine the longitudinal distance of acupoints on the forearm Lower limbs From the upper edge of the pubic symphysis to the patellar base 18 inches Direct measurement Used to determine the longitudinal distance of acupoints on the thigh Patellar base to medial malleolus tip (below the medial tibial condyle) 13+15 inches Direct measurement Measure the distance to the lower part of the medial tibial condyle and the tip of the medial malleolus respectively Greater trochanter of femur to popliteal crease (level with patellar tip) 19 inches Direct measurement Used to determine the longitudinal distance of the acupoints on the anterolateral part of the thigh From gluteal groove to popliteal crease (level with patellar tip) 14 inches Direct measurement Used to determine the longitudinal distance of acupoints on the back of the thigh Popliteal crease (at the level of patellar tip) to lateral malleolus tip 16 inches Direct measurement Used to determine the longitudinal distance of the acupoints on the lateral side of the lower leg Table 2. Positioning joint points in the bone measurement table Positioning joints Starting point End point size illustrate From the middle of the front hairline to the middle of the back hairline Middle of front hairline Middle of the back hairline 12 Used to determine the longitudinal distance of acupuncture points on the head From the brow (Yintang) to the middle of the front hairline Between eyebrows (Yintang) Middle of front hairline 3 - From below the spinous process of the 7th cervical vertebra (Dazhui) to the middle of the posterior hairline Below the spinous process of the 7th cervical vertebra (Dazhui) Middle of the back hairline 3 - From the brow (Yintang) to the middle of the back hairline, below the spinous process of the 7th cervical vertebra (Dazhui) Between eyebrows (Yintang) The middle of the posterior hairline is below the spinous process of the 7th cervical vertebra (Dazhui) 18 - Between the two hair corners of the forehead Left frontal hair corner Right forehead hair corner 9 Used to determine the lateral distance of the acupuncture points on the front of the head Between the two mastoid processes (bone) behind the ear Left mastoid process Right mastoid process 9 Used to determine the lateral distance of the acupuncture points at the back of the head From the suprasternal fossa (Tiantu) to the midpoint of the sternoxiphoid joint (Xiugu) Suprasternal fossa (Tiantu) Midpoint of sternoxiphoid joint (discussus) 9 Used to determine the longitudinal distance of the Ren Meridian acupoints on the chest From the midpoint of the sternocostal joint (the xiphoid process) to the middle of the umbilicus (the sacrum) Midpoint of sternoxiphoid joint (discussus) Umbilicus (Shenque) 8 Used to determine the longitudinal distance of acupuncture points in the upper abdomen From the middle of the umbilicus (Shenque) to the upper edge of the pubic symphysis (Qugu) Umbilicus (Shenque) Upper edge of pubic symphysis (pubic bone) 5 Used to determine the longitudinal distance of the acupuncture points in the lower abdomen Between the nipples Left nipple Right nipple 8 Used to determine the lateral distance of chest and abdominal meridian points From the axillary apex to the free end of the 11th rib (Zhangmen) Axillary apex Free end of 11th rib (Zhangmen) 12 Used to determine the lateral distance of the acupoints in the flank From the inner edge of the scapula (close to the lateral point of the spine) to the posterior midline Inner edge of scapula Posterior midline 3 Used to determine the horizontal distance of the back and waist meridian points From acromion edge to posterior midline Acromion margin Posterior midline 8 Used to determine the horizontal distance of the acupuncture points on the shoulder and back From the front and back of the axilla to the transverse line of the elbow (level with the elbow tip) Front and back axillary wrinkles Elbow horizontal line (flat elbow tip) 9 Used to determine the longitudinal distance of acupuncture points on the arm From the elbow crease (elbow tip) to the wrist crease (dorsal) Elbow horizontal line (flat elbow tip) Palmar (dorsal) side horizontal lines 12 Used to determine the longitudinal distance of acupuncture points in the forearm From the upper edge of the pubic symphysis to the upper edge of the medial epicondyle of the femur Upper edge of pubic symphysis Superior edge of medial epicondyle of femur 18 Used to determine the longitudinal distance of the acupoints of the three Yin meridians of the foot on the medial side of the lower limb Below the medial tibial condyle to the tip of the medial malleolus Below the medial tibial condyle Medial malleolus 13 - Greater trochanter to popliteal crease Greater trochanter Popliteal crease 19 - Popliteal crease to lateral malleolus Popliteal crease Lateral malleolus 16 - S6: Based on the bone measurement method, the positioning joint point is used as the starting point or end point to determine each preset route and calculate the correction coefficient corresponding to these routes k ; In step S6, the correction coefficient is calculated as follows: ; in, k is the correction factor; L a It is the length actually measured between the preset lines; L s is the standard length of the line in the standard model; based on this, the standard length of line A between feature point a and feature point b in the standard model is A 1 , then the line A The calculation formula is as follows: ; By calculating all the lines between different feature points, the data of all feature points in the image area can be obtained; Assume height H , then calculate the height of each part according to the bone measurement method: Head height: ; Upper body length: ; Leg length: ; chest circumference: ; waistline: ; Hips: ; Where: H h is the head height; H u is the length of the upper body; H l is the leg length; Cc is the chest circumference, K c is the correction coefficient for individual differences in chest circumference, with a value of 1.5-2; C w is the waist circumference, K w is the correction coefficient for individual differences in waist circumference, with a value of 1.0-1.5; C h is the hip circumference, K h is the correction coefficient for individual differences in hip circumference, with a value of 1.5-2.0; S7: using the data of different sizes collected in step S1 as a training set and the size data of the standard model in the bone measurement method as a verification set to train the second neural network model; after the training is completed, the second neural network model outputs a prediction correction coefficient k1 between the data of different sizes and the standard model; S8: Collect image information of acupuncture sites, identify feature points through the first neural network model, and determine the prediction correction coefficient k1 through the second neural network model; classify the feature point database with different correction coefficients as identification points to build a tree-structured database; The tree-structured database is searched by tree index; the database establishes identification codes according to the parent level, child level, and grandchild level; Among them, the parent level is classified and coded in order according to the acupuncture area; including the head, face, chest, abdomen, waist, inner side of the calf, and inner side of the foot; The child level is classified and coded in order according to the characteristic points in the corresponding area; the grandchild level is classified and coded in order according to the size of the prediction correction coefficient; When a new image is collected, first determine the region to which it belongs, and search in the database of the corresponding region; then identify the feature points, and calculate the prediction correction coefficient k1 in 2-3 lines in order of the time of the identified feature points; retrieve the data under the same prediction correction coefficient k1 in the corresponding database, and use it as a verification set to compare the subsequent identified feature points in a random number manner. If the comparison is successful, the result is quickly output with the feature points and acupoint positions recorded in the database; if the comparison is unsuccessful, continue to identify more feature points until all feature points in the image area are identified, generate new prediction correction coefficients, and enter the new prediction correction coefficients into the database; The following formula is used to calculate the prediction correction coefficient in 2-3 lines based on the characteristic points: ; In the formula, k 11 、k 12、k 1n are the prediction correction coefficients in the 1st, 2nd, and nth lines respectively; n is the total number of predicted lines; The calculated prediction correction coefficient k1 is used to perform traversal retrieval with the data under several adjacent grandchildren codes in the database to improve the data comparison speed; S9: According to the predicted correction coefficient k1 of the person to be tested and the positions of each positioning joint point, the specific position of each acupoint of the person to be tested is determined according to the positioning rules of the acupoints of the human body.

[0026] The following uses a specific example to introduce the application process of this solution in detail; Specifically, in this embodiment, the chest acupuncture points are taken as an example. Figure 2 As shown; First, a chest image of the patient is collected through the image acquisition module, and the trained first neural network model is used to identify the features of the image. The most easily determined feature point for the chest image is the nipple (Ruzhong point), and according to the bone measurement method, Ruzhong point is located in the center of the nipple, while Tanzhong point is located at the midpoint between the two nipples, and Tanzhong point is four inches away from Ruzhong point. Based on this, the correction coefficient can be obtained by measuring the length of the preset line between the two Ruzhong points and comparing it with the standard data in the standard model. For example, the actual distance between the two Ruzhong points is measured to be 6 inches. Compared with 4 inches in the standard model, the predicted correction coefficient can be calculated using the formula. Then, the distance to other acupoints in the image area is calculated using Ruzhong point as the feature point and marked in the picture. The acupoint positions can be directly marked in the human body with the help of laser or projection equipment, and the doctor only needs to perform acupuncture treatment according to the acupoint positions.

[0027] The tree-structured database established in this embodiment can easily find the corresponding data, and can quickly compare with the existing historical data, thereby improving the recognition speed and accuracy. Specifically, firstly, a different identification code is set for each group of data in the database; the identification code is arranged in the sequence of parent, child, and grandchild; for example, an 8-digit number is used as the identification code; the first two digits are the parent code, the middle 3 digits are the child code, and the last 3 digits are the grandchild code; the identification code 03023012 means that the data is stored in 03 parent, 023 child, 012 grandchild The parent level is classified and coded in order by acupuncture area; for example, head 01, face 02, chest 03, abdomen 04, waist 05, inner side of calf 06, inner side of foot 07, and so on; the child level is classified and coded in order by the feature points in the corresponding area; for example, in the chest data, the Ruzhong acupoint feature point number is 023; the grandchild level is classified and coded in order by the size of the prediction correction coefficient; therefore, the above identification code 03023012 should retrieve the data segment in the chest data where the Ruzhong acupoint is the feature point correction coefficient number 012; When the feature points in the image are identified, they are sorted in chronological order; take feature points abcd as an example; a is the first feature point identified, and d is the last feature point identified; when using this solution, first determine the area and feature point where the feature point is located, and then determine the parent and child data locations in the database; then establish a line between ab, calculate the correction coefficient, and obtain the first correction coefficient; then establish a line between ac and bc, calculate the correction coefficient, and obtain the second correction coefficient; when calculating the predicted correction coefficient in 2-3 lines according to the feature point, the following formula is used: ; In the formula, k 11 、k 12 、k 1n are the prediction correction coefficients in the 1st, 2nd, and nth lines respectively; n is the total number of predicted lines; Use the prediction correction coefficient to retrieve the corresponding and nearby two data segments; use the data in the data segment as verification data for verification; Afterwards, a line is established between ad, bd, and cd, and the correction coefficient is calculated to obtain the third correction coefficient. The third correction coefficient is compared with the data segment in the database. If the similarity is greater than 99%, the comparison is considered successful, and other feature points and acupoints in the data segment in the database are directly retrieved and output, which greatly reduces the amount of calculation; when the correction coefficient similarity is low, continue to look for the next feature point, and repeat the above steps until the comparison with the data segment is successful; if all feature points in the image area are traversed and still not compared successfully, the actual operation result of the model is output, and the data is saved, and the identification code is set in the above encoding step to be saved in a separate data segment in the database.

[0028] Embodiment 2: The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the acupuncture positioning method based on image processing in Embodiment 1 when executing the computer program.

[0029] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0030] Embodiment 3: The present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the acupuncture positioning method based on image processing are implemented.

[0031] The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination of them, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0032] In summary, the present invention utilizes data from existing public databases and the bone measurement method to establish a standard model; constructs a first neural network model and a second neural network model by analyzing and processing acupoint models of different sizes, different parts and different types on the market; wherein the first neural network model is used to perform feature recognition on image data, identify feature points, acupoints and related information; the second neural network model is used to output correction coefficients; a standard database is constructed and classified using a position tree structure; when the model inputs image data, it can be quickly compared to reduce the amount of calculation and improve the accuracy of recognition; the present invention can directly mark the acupoint positions in the human body in conjunction with laser or projection equipment, and doctors only need to perform acupuncture treatment according to the acupoint positions.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An acupuncture positioning method based on image processing, characterized in that: The specific steps include: S1: Collect image data of acupuncture points in public databases; S2: preprocessing the collected image data; S3: constructing a first neural network model to perform feature recognition on the collected image information; S4: Establish a database of feature points and classify them according to different parts; S5: In the constructed database, identify and retain each joint point that has a corresponding point in the bone measurement method; S6: Based on the bone measurement method, the positioning joint point is used as the starting point or the end point to determine each preset route, and the correction coefficient k corresponding to these routes is calculated; S7: using the data of different sizes collected in step S1 as a training set and using the size data of the standard model in the bone measurement method as a verification set to train the second neural network model; After the training is completed, the second neural network model outputs the prediction correction coefficient k1 between the data of different size models and the standard model; S8: Collect image information of acupuncture sites, identify feature points through the first neural network model, and determine the prediction correction coefficient k1 through the second neural network model; classify the feature point database with different correction coefficients as identification points to build a tree-structured database; S9: According to the predicted correction coefficient k1 of the person to be tested and the positions of each positioning joint point, the specific position of each acupoint of the person to be tested is determined according to the positioning rules of the acupoints of the human body.

2. The acupuncture positioning method based on image processing as claimed in claim 1, characterized in that: The image data collected in the public database in step S1 include data of different parts, different sizes or different models, as well as the specific location and physiological characteristics of each acupoint.

3. The acupuncture positioning method based on image processing as claimed in claim 1, characterized in that: In step S2, the collected data is subjected to data standardization, and image cleaning, image enhancement and segmentation processing steps are performed for subsequent feature recognition.

4. The acupuncture positioning method based on image processing as claimed in claim 1, characterized in that: In step S3, feature recognition is performed on the collected data based on the cascade convolutional neural network model combined with the joint points in the bone measurement method, and the identified feature points are matched one-to-one with the joint points in the bone measurement method.

5. The acupuncture positioning method based on image processing as claimed in claim 1, characterized in that: In step S4, a data set with feature point coordinate data as elements is established for each different part.

6. The acupuncture positioning method based on image processing according to claim 1, characterized in that: In step S6, the correction coefficient is calculated as follows: ； in, k is the correction factor; L a It is the length actually measured between the preset lines; L s is the standard length of the line in the standard model; based on this, the line between feature point a and feature point b A The standard length in the standard model is A 1 , then the line A The calculation formula is as follows: ； By calculating all the lines between different feature points, the data of all feature points in the image area can be obtained; Assume height H , then calculate the height of each part according to the bone measurement method: Head height: ; Upper body length: ; Leg length: ; chest circumference: ; waistline: ; Hips: ; Where: H h is the head height; H u is the length of the upper body; H l is the leg length; C c is the chest circumference, K c is the correction coefficient for individual differences in chest circumference, with a value of 1.5-2; C w is the waist circumference, K w is the correction coefficient for individual differences in waist circumference, with a value of 1.0-1.5; C h is the hip circumference, K h is the correction coefficient for individual differences in hip circumference, and its value is 1.5-2.

0.

7. The acupuncture positioning method based on image processing as claimed in claim 1, characterized in that: The tree-structured database is searched by tree index; the database establishes identification codes according to the parent level, child level, and grandchild level; Among them, the parent level is classified and coded in order according to the acupuncture area; including the head, face, chest, abdomen, waist, inner side of the calf, and inner side of the foot; The child level is classified and coded in order according to the characteristic points in the corresponding area; the grandchild level is classified and coded in order according to the size of the prediction correction coefficient; When a new image is collected, the region to which it belongs is first determined, and the database of the corresponding region is searched; then, the feature points are identified, and the prediction correction coefficients k1 in 2-3 lines are calculated in chronological order according to the identified feature points; the data under the same prediction correction coefficient k1 in the corresponding database is retrieved, and this is used as a verification set, and the subsequent identified feature points are compared using random numbers. If the comparison is successful, the results are quickly output based on the feature points and acupoint locations recorded in the database; if the comparison is unsuccessful, more feature points are identified until all feature points in the image area are identified, a new prediction correction coefficient is generated, and the new prediction correction coefficient is entered into the database.

8. The acupuncture positioning method based on image processing as claimed in claim 7, characterized in that: The following formula is used to calculate the prediction correction coefficient in 2-3 lines based on the characteristic points: ； In the formula, k 11 、k 12 、k 1n are the prediction correction coefficients in the 1st, 2nd, and nth lines respectively; n is the total number of predicted lines; The calculated prediction correction coefficient k1 is traversed and searched with the data under several adjacent grandchildren codes in the database to improve the data comparison speed.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the acupuncture positioning method based on image processing described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the acupuncture positioning method based on image processing described in any one of claims 1 to 8 are implemented.

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