CN107684438B - Pain degree detection method and device based on ultrasonic image - Google Patents
Pain degree detection method and device based on ultrasonic image Download PDFInfo
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Abstract
The invention relates to the technical field of ultrasonic image processing, in particular to a pain degree detection method and device based on ultrasonic images. The pain degree detection method based on the ultrasonic image comprises the following steps: step a: collecting dynamic ultrasonic images of muscles at a pain part, and grading the pain degree; step b: extracting muscle thickness parameters of a region of interest in the dynamic ultrasonic image; step c: and carrying out Pelson correlation analysis on the extracted muscle thickness parameters and the pain degree scoring result, and detecting the pain degree according to the correlation analysis result. According to the invention, the muscle thickness parameters of the ultrasonic image are extracted by collecting the dynamic ultrasonic image of the muscle, the correlation analysis is carried out on the muscle thickness parameters and the pain degree, and the pain degree is scientifically and objectively evaluated according to the analysis result, so that the accuracy is high; meanwhile, the device has the advantages of small volume, simple operation, good real-time performance and visibility, safety and sanitation, easy acceptance by patients and easy realization.
Description
Technical Field
The invention relates to the technical field of ultrasonic image processing, in particular to a pain degree detection method and device based on ultrasonic images.
Background
The human body movement is accomplished by taking bones as levers, joints as hinges and muscle contraction as power and under the control of a nervous system. Muscle contraction is the action of the engine in human body movement, provides power for human body movement, and plays a very important role in movement. Patients with low back pain have long-term work or strain which causes lesions in the waist muscles, thereby causing slow motor neuron release impulse and reduced working capacity.
While lumbago is a syndrome of pain in the back, lumbosacral area and buttocks, 80% of the lumbago clinically is caused by long-term work, abnormal exercise posture required for a specific occupation type and insufficient rest, and lumbago is one of the biggest killers of occupational diseases. Chronic pain associated with chronic lumbago is a great hazard to patients, and long-term persistent pain seriously affects the physical function and physical health of patients, so that the patients generate bad emotion, and the life quality and work efficiency of the patients are affected. The pain degree of the chronic lumbago is scientifically, objectively and quantitatively estimated, and the key of rehabilitation training and rehabilitation estimation of the lumbago patient is how to carry out scientific, objective and quantitative estimation on the pain degree of the chronic lumbago.
Generally, the pain level is estimated mainly by artificial self-perception, and currently, relatively effective self-estimation modes include: scoring scales such as visual analog scoring (Visual analog scale, VAS), text description scoring (Verbal Descriptor Scale, VDS), digital scoring (Numeric RATING SCALE, NRS), concise pain questionnaires (Brief pain Inventory, BPI), mcGill pain questionnaires (MCGILL PAIN Questionnaire, MPQ) and the like, in clinical practice, a relatively appropriate scoring scale is selected to evaluate the pain level of a patient according to the patient's condition and environment. The research shows that the incidence rate of depression and anxiety of chronic patients is 30% -60%, in addition, pain is often influenced by external environment, individual emotion and self-perceived pain intensity factors, the pain evaluation parameters obtained by the evaluation method can not truly reflect the pain degree of the patients, and larger deviation is easy to generate, so that correct diagnosis and treatment are greatly hindered, doctors can not carry out corresponding effective treatment according to the real pathological characteristics of the patients, and therefore, a relatively objective pain evaluation mode is needed to truly reflect the pain degree of the patients, and help the doctors to make true diagnosis and adopt effective treatment measures.
Whether the change in muscle structure can be used to assess the pain level of a patient requires further investigation. The current approach used to explore the relatively simple and acceptable changes in muscle structure is ultrasound imaging.
Ultrasonic imaging is an imaging technique that uses a mechanical vibration wave excited in an elastic medium by a mechanical vibration source of 20KHz-25MHz ultrasonic waves, which is reflected and refracted when encountering an interface in the human body, and is essentially in the form of a stress wave to transfer vibration energy. The ultrasonic image has the advantages of no radiation, strong real-time performance, safe use, high imaging speed, low price, convenient use and the like, and is the result of the interaction of the ultrasonic beam and the tissue microstructure. Muscles are important tissues constituting the human body, thereby producing corresponding movements. In the process of human body movement, ultrasonic waves pass through the human body and encounter different tissues, and waves with different brightness degrees reflected by different tissues and different dielectric constants are mapped on a screen to form an ultrasonic image, so that the structural change of muscles in the process of muscle movement is recorded.
Changes in muscle structure reflect the pathological nature of the muscle, and the nature between the pain in the waist and the change in muscle structure is unknown. The common test method for measuring the muscle thickness is many, and is mostly an automatic muscle thickness recognition mode, and the automatic muscle thickness recognition method has the advantages of being fast in speed, capable of rapidly recognizing large sample data and relieving pressure of workers, but automatically recognizing the factors inaccurate in recognition of the region of interest, only by means of programming algorithms of programmers, objectively recognizing the region of interest recognized by the algorithms, having certain deviation, and manually recognizing the region of interest, manually selecting unified region standards according to different acquired pictures, and recognizing the region of interest more accurately.
The invention patent 201510043452.2 adopts three-dimensional display of height values to automatically identify the region of interest in the ultrasonic image, the method can better identify the region of interest, and the method is better for identifying the region of interest of a large number of ultrasonic images. However, the automatic identification method has high requirements on pixels of the image, and lower pixels cannot be identified, so that errors are generated. The patent also does not use the muscle thickness parameter for pain assessment, and does not find the relation between the degree of pain and the thickness of the muscle, and has certain limitation on the application of the device.
In addition, the ultrasonic thickness is calculated by adopting an automatic identification and tracking mode at present, but the mode is more convenient and rapid, but the selection of the interested region of the muscle is deviated from a certain range, and the automatic identification method for the short time can generate great variability due to the adoption of different algorithms.
Disclosure of Invention
The invention provides a pain degree detection method and device based on an ultrasonic image, which aim to solve at least one of the technical problems in the prior art to a certain extent.
In order to solve the problems, the invention provides the following technical scheme:
A pain degree detection method based on an ultrasonic image, comprising the steps of:
step a: collecting dynamic ultrasonic images of muscles at a pain part, and grading the pain degree;
step b: extracting muscle thickness parameters of a region of interest in the dynamic ultrasonic image;
step c: and carrying out Pelson correlation analysis on the extracted muscle thickness parameters and the pain degree scoring result, and detecting the pain degree according to the correlation analysis result.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: the step a further comprises: and programming the acquired dynamic ultrasonic image, and converting the dynamic ultrasonic image into an ultrasonic image frame by frame.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: in the step b, the extracting the muscle thickness parameter of the region of interest in the dynamic ultrasonic image specifically includes: designing a visual muscle thickness extraction interface, displaying an ultrasonic picture through the visual muscle thickness extraction interface, and manually determining and extracting muscle thickness parameters of a region of interest in the ultrasonic picture.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: the muscle thickness parameter extraction mode is as follows: taking the maximum vertical position distance as a muscle thickness parameter, wherein the muscle thickness is equal to the distance between the lower edge of the aponeurosis and the upper edge of the deep aponeurosis; the extraction formula of the muscle thickness parameter is as follows:
In the above formula, TC is the amount of change in muscle thickness, the lower edge of the a fascia, and B is the upper edge of the deep fascia.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: the step c specifically comprises the following steps: carrying out Pelson correlation analysis on the extracted muscle thickness parameters and pain degree scoring results to obtain correlation coefficients between the muscle thickness parameters and the pain degree; judging whether the obtained correlation coefficient is in the set correlation coefficient range, obtaining the correlation degree of the muscle thickness parameter and the pain degree, and detecting the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
The embodiment of the invention adopts another technical scheme that: the pain degree detection device based on the ultrasonic image comprises an image acquisition module, a parameter extraction module, a data analysis module and a pain evaluation module; the image acquisition module is used for acquiring dynamic ultrasonic images of muscles at the pain part; the parameter extraction module is used for extracting muscle thickness parameters of the region of interest in the dynamic ultrasonic image; the data analysis module is used for obtaining a pain degree scoring result of a pain scoring scale and carrying out Pelson correlation analysis on the extracted muscle thickness parameters and the pain degree scoring result; the pain evaluation module is used for detecting the pain degree according to the correlation analysis result.
The technical scheme adopted by the embodiment of the invention further comprises an image conversion module, wherein the image conversion module is used for carrying out programming processing on the acquired dynamic ultrasonic images and converting the dynamic ultrasonic images into ultrasonic pictures frame by frame.
The technical scheme adopted by the embodiment of the invention further comprises an interface design module, wherein the interface design module is used for designing a visual muscle thickness extraction interface, an ultrasonic image is displayed through the visual muscle thickness extraction interface, and the parameter extraction module manually determines and extracts muscle thickness parameters of a region of interest in the ultrasonic image through the visual muscle thickness extraction interface.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: the muscle thickness parameter extraction mode of the parameter extraction module is as follows: taking the maximum vertical position distance as a muscle thickness parameter, wherein the muscle thickness is equal to the distance between the lower edge of the aponeurosis and the upper edge of the deep aponeurosis; the extraction formula of the muscle thickness parameter is as follows:
In the above formula, TC is the amount of change in muscle thickness, the lower edge of the a fascia, and B is the upper edge of the deep fascia.
The technical scheme adopted by the embodiment of the invention further comprises the following steps: the data analysis module performs pearson correlation analysis on the extracted muscle thickness parameters and pain degree scoring results to obtain correlation coefficients between the muscle thickness parameters and the pain degree; the pain evaluation module judges whether the obtained correlation coefficient is in a set correlation coefficient range, obtains the correlation degree of the muscle thickness parameter and the pain degree, and detects the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: according to the pain degree detection method and device based on the ultrasonic image, the muscle dynamic ultrasonic image is collected, the muscle thickness parameter of the ultrasonic image is obtained by manually identifying the region of interest through the visual interface, the correlation analysis is carried out on the muscle thickness parameter and the pain degree, the pain degree is scientifically and objectively evaluated according to the analysis result, the influence of subjective factors is avoided, and the accuracy is high; is favorable for doctors to select proper and effective therapeutic measures and rehabilitation training modes and helps patients get rid of pains early. Meanwhile, the device has the advantages of small volume, simple operation, good real-time performance and visibility, safety and sanitation, easy acceptance by patients and easy realization.
Drawings
FIG. 1 is a flow chart of a method for ultrasound image-based pain level detection in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a visual muscle thickness extraction interface according to an embodiment of the invention;
FIG. 3 is a schematic representation of muscle thickness extraction in accordance with an embodiment of the present invention;
FIG. 4 is a graph showing the correlation of the thickness of the multi-split muscle with the pain degree according to the embodiment of the present invention; wherein, fig. 4 (a) is the correlation between the thickness of the left side multifidus muscle and the pain degree, and fig. 4 (b) is the correlation between the thickness of the right side multifidus muscle and the pain degree;
fig. 5 is a schematic structural view of an ultrasonic image-based pain degree detection device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Pain can limit muscle activity to varying degrees, resulting in deterioration of muscle function, while a decrease in the contractile capacity of the lumbar back muscles can directly affect the structural stability of the lumbar spine, resulting in injury to the intervertebral facet joints and their surrounding ligament tissues and intervertebral discs, and thus lower back pain. Thus, lower back pain of various causes is associated to varying degrees with dysfunctions of the muscular system, in particular the core muscle group associated with maintaining lumbar stabilization. The lumbar core stabilization muscle group is taken as a main component of the main subsystem of the spine, and plays an important role in maintaining the stability and mobility of the spine. Tension generated during contraction of trunk muscles protects the spine while pressure generated acts on the spine, so that when the spine is damaged and structural changes occur, the muscular system tends to change before other structures. According to the embodiment of the invention, the muscle thickness parameters of the ultrasonic image are obtained by manually identifying the region of interest through the visual interface, the pain degree of the patient is quantitatively and scientifically and objectively evaluated through the muscle thickness parameters, the method is not influenced by subjective factors such as personal emotion of the patient, objective pain evaluation is performed on the patient, a doctor is facilitated to select proper and effective treatment measures and rehabilitation training modes, and the patient is helped to get rid of pain early. The following examples of the present invention will be described by way of example only, with the understanding that the present invention is equally applicable to pain detection in other areas, such as the legs, etc.
Referring specifically to fig. 1, a flowchart of a method for detecting pain level based on an ultrasound image according to an embodiment of the present invention is shown. The pain degree detection method based on the ultrasonic image comprises the following steps of:
Step 100: scoring the pain level of the patient by a pain scoring scale;
In step 100, the pain scoring scale includes, but is not limited to, visual analog scoring, text description scoring, digital scoring, concise pain questionnaires, mcGill pain questionnaires, and the like.
Step 200: collecting dynamic ultrasonic images of left and right multifidus muscles of the waist of a patient;
In step 200, the acquisition mode includes: dynamic ultrasound images of the left and right lumbar multifidus muscles of a patient during a specific stance standing (stance: trunk effort bend forward-return stance) are acquired.
Step 300: carrying out Matlab2010b programming treatment on the acquired dynamic ultrasonic images, and converting the dynamic ultrasonic images into ultrasonic pictures frame by frame;
Step 400: displaying an ultrasonic image through a visual muscle thickness extraction interface, and manually extracting muscle thickness parameters of a region of interest in the ultrasonic image;
In step 400, the embodiment of the invention designs a visual muscle thickness extraction interface through Matlab software programming, and the ultrasonic image is directly displayed through the visual muscle thickness extraction interface, so that the muscle thickness parameters of the interested region in the ultrasonic image can be manually determined and extracted, the interested region is identified by a unified standard, and the extracted muscle thickness parameter values are more reliable and real. Specifically, as shown in fig. 2, a visual muscle thickness extraction interface is schematically shown in an embodiment of the present invention.
The method for extracting the thickness of the muscle is shown in fig. 3, which is a schematic diagram of extracting the thickness of the muscle according to an embodiment of the invention. Taking the maximum vertical position distance as a muscle thickness parameter, adopting the same test standard for each picture of each tester, wherein the muscle thickness is equal to the distance from the lower edge A of the aponeurosis to the upper edge point B of the deep aponeurosis, and the muscle thickness extraction formula is as follows:
The measurable depth of the ultrasonic probe is 70mm, the uppermost edge pixel coordinate point of one ultrasonic image is 470, and the lowermost edge pixel coordinate point is 85, so that the distance corresponding to each pixel point is 0.1818mm, and the muscle thickness is obtained as shown in formula (1). The pain degree of the patients with low back pain was evaluated with the muscle thickness variation (TC) as the characteristic parameter.
Step 500: performing pearson bilateral correlation analysis on the extracted muscle thickness parameters and pain degree scoring results of a pain scoring scale to obtain correlation coefficients between the muscle thickness parameters and the pain degree;
In step 500, after the muscle thickness parameter is extracted, the muscle thickness parameter is statistically analyzed by SPSS19.0, and the significance level is represented as p <0.05, which is statistically significant. Bilateral pearson correlation analysis was performed on muscle thickness and pain level. The magnitude of the pearson correlation coefficient r value reflects the strength of correlation between parameters, and when r is less than 0.20, the correlation is poor; when 0.21< r <0.40, the correlation is poor; when 0.41< r <0.60, the correlation is general; when 0.61< r <0.80, the correlation is good; when 0.81< r <1.00, the correlation is high.
Based on the pearson correlation determination, the lower back pain tester had a high correlation between the thickness of the multifidus muscle and the degree of pain during the anteversion exercise. Specifically, as shown in fig. 4, a correlation diagram of the thickness of the multi-split muscle and the pain degree according to the embodiment of the present invention is shown; wherein fig. 4 (a) is a correlation between the thickness of the left side multifidus muscle and the pain degree, and fig. 4 (b) is a correlation between the thickness of the right side multifidus muscle and the pain degree.
As shown in fig. 4, there is a significant correlation between left-side multi-split muscle thickness and pain level (p=0.044 <0.05, r= 0.9564), and right-side multi-split muscle thickness and pain level (p=0.044 <0.05, r= 0.9565). As the pain level increases, the thickness of the multi-split muscle decreases, indicating that the more severe the pain level, the worse the motor function of the muscle, resulting in a change in the structure of the muscle.
Therefore, the high correlation between the muscle thickness parameter and the pain degree proves that the pain degree of the patient can be objectively evaluated by adopting the muscle thickness change acquired by the ultrasonic image, the unreal reflection of the pain degree can not be caused by the influence of subjective factors such as emotion, mood and external environment of the patient, and the pain level can be more effectively reflected. In clinical application and rehabilitation training, the method can guide doctors to diagnose and help patients to heal early.
Step 600: judging whether the obtained correlation coefficient is in the set correlation coefficient range, obtaining the correlation degree of the muscle thickness parameter and the pain degree, and detecting the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
Referring to fig. 5, a schematic structural diagram of an apparatus for detecting pain level based on an ultrasonic image according to an embodiment of the invention is shown. The pain degree detection device based on the ultrasonic image comprises an interface design module, an image acquisition module, an image conversion module, a parameter extraction module, a data analysis module and a pain evaluation module.
The interface design module is used for programming and designing a visual muscle thickness extraction interface through Matlab software; according to the embodiment of the invention, the visual muscle thickness extraction interface is designed through Matlab software programming, the ultrasonic image is directly displayed through the visual muscle thickness extraction interface, and the muscle thickness parameters of the interested region in the ultrasonic image can be manually determined and extracted, so that the interested region is identified by a unified standard, and the extracted muscle thickness parameter values are more reliable and real.
The image acquisition module is used for acquiring dynamic ultrasonic images of left and right multifidus muscles of the waist of the patient;
The image conversion module is used for carrying out Matlab2010b programming treatment on the acquired dynamic ultrasonic images and converting the dynamic ultrasonic images into ultrasonic pictures frame by frame;
The parameter extraction module is used for extracting muscle thickness parameters of the region of interest in the ultrasonic picture through the visual muscle thickness extraction interface; the specific way of extracting the thickness of the muscle is shown in fig. 3, which is a schematic diagram of extracting the thickness of the muscle according to the embodiment of the invention. Taking the maximum vertical position distance as a muscle thickness parameter, adopting the same test standard for each picture of each tester, wherein the muscle thickness is equal to the distance from the lower edge A of the aponeurosis to the upper edge point B of the deep aponeurosis, and the muscle thickness extraction formula is as follows:
The measurable depth of the ultrasonic probe is 70mm, the uppermost edge pixel coordinate point of one ultrasonic image is 470, and the lowermost edge pixel coordinate point is 85, so that the distance corresponding to each pixel point is 0.1818mm, and the muscle thickness is obtained as shown in formula (1). The pain degree of the patients with low back pain was evaluated with the muscle thickness variation (TC) as the characteristic parameter.
The data analysis module is used for obtaining a pain degree scoring result of the pain scoring scale, and carrying out pearson bilateral correlation analysis on the extracted muscle thickness parameter and the pain degree scoring result of the pain scoring scale to obtain a correlation coefficient between the muscle thickness parameter and the pain degree; the correlation analysis of the muscle thickness parameter and the pain degree shows that the multi-split muscle thickness has high correlation with the pain degree; according to the pearson correlation judgment, the lower back pain tester has high correlation between the thickness of the multi-split muscle and the pain degree in the forward tilting movement, and the thickness of the multi-split muscle is continuously reduced along with the increase of the pain degree, which indicates that the more serious the pain degree is, the worse the movement function of the muscle is, and the structure of the muscle is changed.
Therefore, the high correlation between the muscle thickness parameter and the pain degree proves that the pain degree of the patient can be objectively evaluated by adopting the muscle thickness change acquired by the ultrasonic image, the unreal reflection of the pain degree can not be caused by the influence of subjective factors such as emotion, mood and external environment of the patient, and the pain level can be more effectively reflected. In clinical application and rehabilitation training, the method can guide doctors to diagnose and help patients to heal early.
The pain evaluation module is used for judging whether the obtained correlation coefficient is in the set correlation coefficient range, obtaining the correlation degree of the muscle thickness parameter and the pain degree, and detecting the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
To further illustrate the feasibility of the present embodiments, the present embodiments are illustrated with ultrasound images acquired of 44 low back pain testers. Firstly, a tester stands upright in a standard posture, the trunk is inclined forwards as much as possible under the requirement of a director, the tester returns to the standing position after reaching the bending maximum position of the tester, the ultrasonic dynamic images of the left and right side multifidus muscles of the tester are collected for 5 times continuously, each tester scores the pain degree of the tester through a visual simulation scoring table, and all information of the tester is shown in the following table 1:
TABLE 1 tester information
For dynamic ultrasonic images of 44 testers, matlab2010 programming is adopted to convert dynamic ultrasonic videos into 128 ultrasonic pictures of one frame, and then the maximum and minimum thicknesses of muscles in the region of interest are manually extracted through a visual muscle thickness extraction interface. After the muscle thickness extraction is carried out on the ultrasonic pictures of the muscles of 44 testers, the bilateral pearson correlation analysis is carried out on the muscle thickness and the pain degree, so that the high correlation between the change amount of the muscle thickness and the VSA pain degree is obtained, and the muscle thickness can be proved to be used as a mode for objectively evaluating the back pain degree. The pain evaluation mode is simple to operate, low in cost, free from influence of subjective factors and high in accuracy.
According to the pain degree detection method and device based on the ultrasonic image, the muscle dynamic ultrasonic image is collected, the muscle thickness parameter of the ultrasonic image is obtained by manually identifying the region of interest through the visual interface, the correlation analysis is carried out on the muscle thickness parameter and the pain degree, the pain degree is scientifically and objectively evaluated according to the analysis result, the influence of subjective factors is avoided, and the accuracy is high; is favorable for doctors to select proper and effective therapeutic measures and rehabilitation training modes and helps patients get rid of pains early. Meanwhile, the device has the advantages of small volume, simple operation, good real-time performance and visibility, safety and sanitation, easy acceptance by patients and easy realization.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A pain degree detection method based on an ultrasonic image, comprising the steps of:
Step a: collecting dynamic ultrasonic images of the multi-split muscle at the pain part in the forward tilting movement, and grading the pain degree;
step b: the muscle thickness parameters of the region of interest in the dynamic ultrasonic image are extracted, specifically: designing a visual muscle thickness extraction interface, displaying an ultrasonic picture through the visual muscle thickness extraction interface, and manually determining and extracting muscle thickness parameters of a region of interest in the ultrasonic picture; the muscle thickness parameter extraction mode is as follows: taking the maximum vertical position distance as a muscle thickness parameter, wherein the muscle thickness is equal to the distance between the lower edge of the aponeurosis and the upper edge of the deep aponeurosis; the extraction formula of the muscle thickness parameter is as follows:
in the above formula, TC is the amount of change in muscle thickness, the lower edge of the a fascia, and B is the upper edge of the deep fascia;
step c: and carrying out Pelson correlation analysis on the extracted muscle thickness parameters and the pain degree scoring result, and detecting the pain degree according to the correlation analysis result.
2. The method for detecting the pain level based on the ultrasonic image according to claim 1, wherein the step a further comprises: and programming the acquired dynamic ultrasonic image, and converting the dynamic ultrasonic image into an ultrasonic image frame by frame.
3. The method for detecting pain level based on ultrasonic image according to claim 2, wherein said step c specifically comprises: carrying out Pelson correlation analysis on the extracted muscle thickness parameters and pain degree scoring results to obtain correlation coefficients between the muscle thickness parameters and the pain degree; judging whether the obtained correlation coefficient is in the set correlation coefficient range, obtaining the correlation degree of the muscle thickness parameter and the pain degree, and detecting the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
4. The pain degree detection device based on the ultrasonic image is characterized by comprising an image acquisition module, a parameter extraction module, a data analysis module, a pain evaluation module and an interface design module; the image acquisition module is used for acquiring dynamic ultrasonic images of the multi-split muscle at the pain part in the forward tilting movement; the interface design module is used for designing a visual muscle thickness extraction interface, and displaying an ultrasonic picture through the visual muscle thickness extraction interface; the parameter extraction module manually determines and extracts muscle thickness parameters of the region of interest in the ultrasonic image through a visual muscle thickness extraction interface; the data analysis module is used for obtaining a pain degree scoring result of a pain scoring scale and carrying out Pelson correlation analysis on the extracted muscle thickness parameters and the pain degree scoring result; the pain evaluation module is used for detecting the pain degree according to the correlation analysis result; the muscle thickness parameter extraction mode of the parameter extraction module is as follows: taking the maximum vertical position distance as a muscle thickness parameter, wherein the muscle thickness is equal to the distance between the lower edge of the aponeurosis and the upper edge of the deep aponeurosis; the extraction formula of the muscle thickness parameter is as follows:
In the above formula, TC is the amount of change in muscle thickness, the lower edge of the a fascia, and B is the upper edge of the deep fascia.
5. The ultrasound image-based pain level detection device of claim 4, further comprising an image conversion module for programmatically processing the acquired dynamic ultrasound images to convert the dynamic ultrasound images into ultrasound pictures frame by frame.
6. The ultrasonic image-based pain degree detection device according to claim 4, wherein the data analysis module performs pearson correlation analysis on the extracted muscle thickness parameter and the pain degree scoring result to obtain a correlation coefficient between the muscle thickness parameter and the pain degree; the pain evaluation module judges whether the obtained correlation coefficient is in a set correlation coefficient range, obtains the correlation degree of the muscle thickness parameter and the pain degree, and detects the pain degree of the patient according to the correlation degree of the muscle thickness parameter and the pain degree.
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