Brain health level evaluation system and method based on neural image

Abstract

The invention relates to the technical field of neural images, and discloses a brain health level evaluation system and method based on neural image.The brain health level evaluation system comprises an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a center unit, a treatment unit and an evaluation unit, the preprocessing unit preprocesses the neural image, the segmentation unit segments the preprocessed neural image, the central unit receives an area difference value and a graying deviation value and calculates a form value XT and an activity value HD, and the evaluation unit calculates an evaluation value P according to the form value XT and the activity value HD. According to the method, the generated nerve image is processed, the accuracy of the nerve image is ensured, the evaluation value P calculated by adopting the form value XT and the activity value H can accurately reflect the self condition of the examiner, and the smaller the evaluation value P is, the higher the brain health level is.

Description

Technical Field

[0001] The present invention relates to the field of neuroimaging technology, and more specifically to a system and method for evaluating brain health level based on neuroimaging. Background Art

[0002] Neuroimaging examinations usually include skull radiographs, angiography, computed tomography (CT) scans, and magnetic resonance imaging (MRI). Neuroimaging examinations can be used to diagnose a variety of neurological diseases, such as brain tumors, multiple sclerosis, and cerebrovascular accidents. With the aging of the population and the rising incidence of neurological diseases, brain health assessment has become an important direction in clinical medicine and public health. Furthermore, the continuous development of neuroimaging technologies (such as magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), computed tomography (CT), and positron emission tomography (PET)) has provided a data basis for the quantitative analysis of brain structure and function. However, existing brain health assessment methods still have the following limitations: Traditional assessments mainly rely on doctors' visual interpretation of images and empirical judgment, which are highly subjective and inconsistent. In particular, they are prone to missed diagnoses in the identification of early lesions or mild cognitive impairment, making it difficult to achieve a comprehensive quantitative assessment of brain health. Very small changes in the early stages cannot be discovered in a timely manner, and the collected neural impact images are not precise enough, resulting in inaccurate final image assessments. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the implementation regulations of the present invention provide a brain health level assessment system and method based on neuroimaging to solve the technical problems raised in the background technology.

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a brain health level assessment system based on neuroimaging, comprising an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a central unit, a treatment unit, and an assessment unit, wherein the imaging unit is used to generate neuroimaging, the preprocessing unit preprocesses the neuroimaging, the segmentation unit segments the preprocessed neuroimaging, the analysis unit receives the segmented neuroimaging image and calculates an area difference value and a grayscale deviation value, the central unit receives the area difference value and the grayscale deviation value and calculates a morphological value XT and an activity value HD, the assessment unit calculates an assessment value P based on the morphological value XT and the activity value HD to assess the brain health level, and the treatment unit is used to perform brain examination and treatment; The preprocessing unit includes a denoising module and an interpolation module. The interpolation module supplements the time difference of different slices of the imaging unit during image acquisition, and the interpolation module uses linear interpolation to supplement the time difference interpolation. The calculation formula of the linear interpolation method is: , where is the corrected signal, I1 is the signal value at the initial point before slicing, I2 is the signal value at slicing, t2 is the slicing time, t1 is the time, t R The required standard time.

[0005] Furthermore, the denoising module uses deep learning to perform denoising processing. Deep learning directly predicts noise through residual learning and outputs the residual of the clean image and noise. It then uses an encoder and decoder structure, combined with jump connections, to compress the input into a low-dimensional space and then reconstruct it to remove noise from the image.

[0006] Furthermore, the segmentation unit segments the neural image processed by the preprocessing unit. The segmentation unit segments the neural image using an atlas segmentation method. The segmentation unit aligns the received neural image with a standard brain atlas and segments the target brain area using the label information of the atlas. The label information of the atlas includes the cerebral cortex area, the brainstem area, the cerebellum area and the limbic area. The segmentation unit segments the neural image into four areas: the cerebral cortex area, the brainstem area, the cerebellum area and the limbic area and sends the segments to the analysis unit.

[0007] Furthermore, the analysis unit receives the four brain regions sent by the segmentation unit. The analysis unit includes an area module and a color mode. The area module calculates the area difference between the cerebral cortex region, brainstem region, cerebellum region and marginal region sent by the segmentation unit and the standard cerebral cortex region, brainstem region, cerebellum region and marginal region. The area difference values ​​calculated by the area module include the area difference value T1 of the cerebral cortex region, the area difference value T2 of the brainstem region, the area difference value T3 of the cerebellum region and the area difference value T4 of the marginal region and send them to the central unit.

[0008] Furthermore, the color module performs grayscale processing on the received image data information of the four brain regions. The grayscale processing formula is H=0.3R+0.58G+0.12B, where H is the grayscale value after processing, R is the red image, G is the green image, and B is the blue image, and R, G, and B are all within the grayscale value range of 0-255. The color module calculates the grayscale deviation value between the grayscale value H after the grayscale processing of the four brain regions and the grayscale value of the standard four brain regions. The grayscale deviation value calculated by the color module includes the grayscale deviation value H1 of the cerebral cortex region, the grayscale deviation value H2 of the brainstem region, the grayscale deviation value H3 of the cerebellum region, and the grayscale deviation value H4 of the marginal region and sends them to the central unit.

[0009] Furthermore, the central unit receives all the data information sent by the area module and the color module in the analysis unit and calculates the morphology value XT and the activity value HD. The calculation formula of the morphology value XT is: , and the calculation formula of the activity value HD in the central unit is , where k1, k2, k3, k4 and l1, l2, l3, l4 are weights.

[0010] Furthermore, the central unit compares the calculated morphological value XT with its internal morphological threshold XY. When the morphological value XT is greater than the morphological threshold XY, the central unit sends an inspection instruction to the treatment unit. The treatment unit receives the inspection instruction and performs a review. The central unit compares the calculated activity value HD with its internal activity threshold HY. When the activity value HD is greater than the activity threshold HY, the central unit sends an inspection instruction to the treatment unit. When the morphological value XT is less than or equal to the morphological threshold XY and the activity value HD is less than or equal to the activity threshold HY, the central unit does not send any instruction.

[0011] Furthermore, the central unit sends the calculated morphology value XT and activity value HD to the evaluation unit, which receives the morphology value XT and activity value HD and calculates the evaluation value P. The calculation formula of the evaluation value P is: The evaluation unit arranges the evaluation values ​​P in ascending order from low to high, and the brain health level gradually decreases from low to high.

[0012] A method for assessing brain health level based on neuroimaging, comprising the following steps: Step S1, generating a neuroimage image, performing denoising on the generated neuroimage image, and interpolating and supplementing the denoised neuroimage image with time differences between different slices; Step S2, segmenting the interpolated and supplemented neuroimaging image, calculating the area difference value and the grayscale deviation value after segmentation, and calculating the morphology value XT and the activity value HD based on the area difference value and the grayscale deviation value; Step S3, morphology value XT and activity value HD, when morphology value XT>morphology threshold value XY and activity value HD>activity threshold value HY, perform inspection and treatment, when morphology value XT≤morphology threshold value XY and activity value HD≤activity threshold value HY, perform evaluation; Step S4: During the evaluation, the evaluation value P is calculated based on the morphological value XT and the activity value HD, and the evaluation values ​​P are arranged in ascending order from low to high. The health level decreases in the order of arrangement.

[0013] The technical effects and advantages of the present invention are as follows: The present invention processes the generated neurological images to ensure their accuracy. The morphological value XT and activity value HD calculated based on area changes and color changes are sufficiently accurate. When the morphological value XT and activity value HD exceed their corresponding thresholds, a re-inspection instruction is promptly issued. On the basis of being less than or equal to the threshold, the evaluation value P calculated using the morphological value XT and activity value H can accurately reflect the examinee's own condition. The smaller the evaluation value P, the higher the brain health level. The present invention uses deep learning to perform denoising. Deep learning can learn the characteristics of different types of noise and adaptively adjust the denoising strategy to apply to a wider range of noise types. This allows for continuous adjustment to ensure that the denoising level continues to increase. After denoising, linear interpolation is used to supplement the temporal difference interpolation, which can align time series images to reduce motion artifacts, making the processed neuroimaging images more accurate. In the present invention, when the morphology value XT is greater than the morphology threshold XY and the activity value HD is greater than the activity threshold HY, it indicates that the area and color changes of the brain exceed the standard range, and the examinee is more likely to have a brain problem. Therefore, the examinee is directly re-examined and treated as soon as possible to avoid worsening of the condition. When the morphology value XT is less than or equal to the morphology threshold XY and the activity value HD is less than or equal to the activity threshold HY, the evaluation value P is calculated and arranged in ascending order from low to high, and the brain health level gradually decreases from low to high, so that the examinee's own brain health level can be accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the overall system structure of the present invention.

[0015] Figure 2 Schematic diagram of the evaluation method of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The brain health level assessment system and method based on neuroimaging involved in the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] Reference Figure 1The present invention provides a brain health level assessment system based on neuroimaging, including an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a central unit, a treatment unit and an assessment unit. The imaging unit is used to generate neuroimaging, the preprocessing unit preprocesses the neuroimaging, the segmentation unit segments the preprocessed neuroimaging, the analysis unit receives the segmented neuroimaging image and calculates the area difference value and the grayscale deviation value, the central unit receives the area difference value and the grayscale deviation value and calculates the morphological value XT and the activity value HD, the assessment unit calculates the assessment value P according to the morphological value XT and the activity value HD to perform brain health level assessment, and the treatment unit is used to perform brain examination and treatment.

[0018] In an embodiment of the present application, when evaluating the brain health level based on neuroimaging, the present application first processes the generated neuroimaging image. After processing, the accuracy of the neuroimaging image is guaranteed. On the basis of sufficient accuracy of the neuroimaging image, the morphological value XT and the activity value HD calculated based on the area change and color change are sufficiently accurate. When the morphological value XT and the activity value HD exceed their corresponding thresholds respectively, it indicates that there is a high possibility that there is a problem with the brain of the person performing the neuroimaging inspection, and an instruction for re-inspection is issued in time. On the basis of being less than or equal to the threshold, the evaluation value P calculated using the morphological value XT and the activity value H can accurately reflect the inspector's own situation. The smaller the evaluation value P, the higher the brain health level of the inspector.

[0019] Reference Figure 1 The preprocessing unit includes a denoising module and an interpolation module. The denoising module uses deep learning to perform denoising. Deep learning directly predicts noise through residual learning and outputs the residual of the clean image and noise. Then, the encoder and decoder structure are combined with jump connections to compress the input into a low-dimensional space and then reconstruct it to remove the noise in the image. The interpolation module supplements the time difference of different slices of the imaging unit during image acquisition, and the interpolation module uses linear interpolation to perform time difference interpolation. The calculation formula of the linear interpolation method is , where is the corrected signal, I1 is the signal value at the initial point before slicing, I2 is the signal value at slicing, t2 is the slicing time, t1 is the time, t R The required standard time.

[0020] In the embodiment of the present application, in the preprocessing of the neural imaging image, the present application first adopts the deep learning method to perform denoising. Deep learning can learn different types of noise characteristics, adaptively adjust the denoising strategy, and be applicable to a wider range of noise types, and then can be continuously adjusted to ensure that the denoising level continues to increase. After denoising, linear interpolation is used to perform time difference interpolation supplement. Since slicing is required when generating neural images, and when slicing, the acquisition time of the time series of slicing is different, artifacts will be generated at this time due to the inability to align. After the linear interpolation method is used for processing, the time series images can be aligned to reduce motion artifacts, and the processed neural imaging images are more accurate.

[0021] Reference Figure 1 The segmentation unit segments the neural images processed by the preprocessing unit. The segmentation unit segments the neural images using the atlas segmentation method. The segmentation unit aligns the received neural images with the standard brain atlas and segments the target brain area using the atlas label information. The atlas label information includes the cerebral cortex area, brainstem area, cerebellum area and marginal area. The segmentation unit segments the neural images into four areas: cerebral cortex area, brainstem area, cerebellum area and marginal area and sends them to the analysis unit.

[0022] In an embodiment of the present application, when performing neural image segmentation, when using the atlas segmentation method to segment the neural image, different thresholds can be applied to different areas of the image to make it more compatible with the complex brain areas of the present application. After segmentation, the present application divides the entire brain into four areas, which are the cerebral cortex area, the brainstem area, the cerebellum area, and the limbic area, respectively, which can include all brain contents.

[0023] Reference Figure 1The analysis unit receives the four brain regions sent by the segmentation unit. The analysis unit includes an area module and a color mode. The area module calculates the area difference between the cerebral cortex region, brainstem region, cerebellum region, and marginal region sent by the segmentation unit and the standard cerebral cortex region, brainstem region, cerebellum region, and marginal region. The area difference values ​​calculated by the area module include the area difference value T1 of the cerebral cortex region, the area difference value T2 of the brainstem region, the area difference value T3 of the cerebellum region, and the area difference value T4 of the marginal region and send them to the central unit. The color module grayscales the image data information of the four brain regions received. The grayscale processing formula is H=0.3R+0.58G+0.12B, where H is the grayscale value after processing, R is the red image, G is the green image, and B is the blue image, and R, G, and B are all within the grayscale value range of 0-255. The color module calculates the grayscale deviation value between the grayscale value H after grayscale processing of the four brain regions and the grayscale value of the standard four brain regions. The grayscale deviation values ​​calculated by the color module include the grayscale deviation value H1 of the cerebral cortex region, the grayscale deviation value H2 of the brainstem region, the grayscale deviation value H3 of the cerebellum region, and the grayscale deviation value H4 of the edge region and send them to the central unit.

[0024] In the embodiment of the present application, after segmentation, the analysis module judges it in two ways, namely, area judgment and color judgment. The area arrangement can understand the area in each region, so when a problem occurs in a certain area of ​​the brain, there will be obvious regional changes, which can be understood in time, and the color will directly reflect the activity of the brain nerves. Therefore, the present application performs calculations in different directions to ensure that the present application can make a more comprehensive judgment on brain health. In addition, when calculating the difference value T1 of the cerebral cortex area, the ratio of the difference between the area of ​​the examined cerebral cortex area and the area of ​​the standard cerebral cortex area to the area of ​​the standard cerebral cortex area is calculated. The calculation formula is: , where Ta is the area of ​​the examined cerebral cortex region, B is the area of ​​the standard cerebral cortex region, and the remaining data are calculated in this way.

[0025] Reference Figure 1 The central unit receives all the data information sent by the area module and color module in the analysis unit and calculates the morphology value XT and activity value HD. The calculation formula of the morphology value XT is , and the calculation formula of the activity value HD in the central unit is , where k1, k2, k3, k4 and l1, l2, l3, l4 are all weights. The central unit compares the calculated morphological value XT with its internal morphological threshold XY. When the morphological value XT>morphological threshold XY, the central unit sends an inspection instruction to the treatment unit. The treatment unit receives the inspection instruction and conducts a review. The central unit compares the calculated activity value HD with its internal activity threshold HY. When the activity value HD>activity threshold HY, the central unit sends an inspection instruction to the treatment unit. When the morphological value XT≤morphological threshold XY and the activity value HD≤activity threshold HY, the central unit does not send an instruction. The central unit sends the calculated morphological value XT and activity value HD to the evaluation unit. The evaluation unit receives the morphological value XT and activity value HD and calculates the evaluation value P. The calculation formula of the evaluation value P is: The evaluation unit arranges the evaluation values ​​P in ascending order from low to high, and the brain health level gradually decreases from low to high.

[0026] In the embodiment of the present application, the morphology value XT and the activity value HD are calculated jointly using the absolute values ​​of the differences between the four area differences and the grayscale deviation values. When the morphology value XT is greater than the morphology threshold XY, it indicates that the area difference value of a certain part has exceeded the standard range. At this time, the examinee is likely to have a brain problem, so they should be re-examined and treated as soon as possible to avoid worsening of the condition. When the activity value HD is greater than the activity threshold HY, it also indicates that the examinee themselves has a major problem and a quick inspection instruction may be issued. When the morphology value XT is less than the morphology threshold XY and the activity value HD is less than the activity threshold HY, the examinee does not have a major problem. At this time, the evaluation value P is calculated based on the morphology value XT and the activity value HD. The larger the evaluation value P, the larger the morphology value XT and the activity value HD, the greater the possibility of a problem, and therefore the lower the health level. Therefore, the present application arranges the evaluation values ​​P in ascending order from low to high, and the brain health level gradually decreases from low to high, which can accurately evaluate the examinee's own brain health level.

[0027] Reference Figure 2 , a method for assessing brain health level based on neuroimaging, comprising the following steps: Step S1, generating a neuroimage image, performing denoising on the generated neuroimage image, and interpolating and supplementing the denoised neuroimage image with time differences between different slices; Step S2, segmenting the interpolated and supplemented neuroimaging image, calculating the area difference value and the grayscale deviation value after segmentation, and calculating the morphology value XT and the activity value HD based on the area difference value and the grayscale deviation value; Step S3, morphology value XT and activity value HD, when morphology value XT>morphology threshold value XY and activity value HD>activity threshold value HY, perform inspection and treatment, when morphology value XT≤morphology threshold value XY and activity value HD≤activity threshold value HY, perform evaluation; Step S4: During the evaluation, the evaluation value P is calculated based on the morphological value XT and the activity value HD, and the evaluation values ​​P are arranged in ascending order from low to high. The health level decreases in the order of arrangement.

[0028] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The units and algorithm steps of each example described in the embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0029] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0030] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0031] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A brain health assessment system based on neuroimaging, characterized by: The system comprises an imaging unit, a preprocessing unit, a segmentation unit, an analysis unit, a central unit, a treatment unit, and an evaluation unit. The imaging unit is used to generate a neuroimage. The preprocessing unit preprocesses the neuroimage. The segmentation unit segments the preprocessed neuroimage. The analysis unit receives the segmented neuroimage and calculates an area difference value and a grayscale deviation value. The central unit receives the area difference value and the grayscale deviation value and calculates a morphology value XT and an activity value HD. The evaluation unit calculates an evaluation value P based on the morphology value XT and the activity value HD to evaluate the brain health level. The treatment unit is used to perform brain examination and treatment. The preprocessing unit includes a denoising module and an interpolation module. The interpolation module supplements the time difference of different slices of the imaging unit during image acquisition, and the interpolation module uses linear interpolation to supplement the time difference interpolation. The calculation formula of the linear interpolation method is: , where is the corrected signal, I1 is the signal value at the initial point before slicing, I2 is the signal value at slicing, t2 is the slicing time, t1 is the time, t R The required standard time.

2. The brain health level assessment system based on neuroimaging according to claim 1, characterized in that: The denoising module uses deep learning to perform denoising. Deep learning directly predicts noise through residual learning and outputs the residual of the clean image and the noise. It then uses an encoder and decoder structure combined with jump connections to compress the input into a low-dimensional space and reconstruct it to remove noise from the image.

3. The brain health level assessment system based on neuroimaging according to claim 1, characterized in that: The segmentation unit segments the neural image processed by the preprocessing unit, and the segmentation unit segments the neural image using an atlas segmentation method. The segmentation unit aligns the received neural image with a standard brain atlas, and segments the target brain area using the atlas label information. The atlas label information includes the cerebral cortex area, the brainstem area, the cerebellum area, and the limbic area. The segmentation unit segments the neural image into four areas: the cerebral cortex area, the brainstem area, the cerebellum area, and the limbic area, and sends the segments to the analysis unit.

4. The brain health level assessment system based on neuroimaging according to claim 3, characterized in that: The analysis unit receives the four brain regions sent by the segmentation unit. The analysis unit includes an area module and a color mode. The area module calculates the area difference between the cerebral cortex region, brainstem region, cerebellum region and marginal region sent by the segmentation unit and the standard cerebral cortex region, brainstem region, cerebellum region and marginal region. The area difference values ​​calculated by the area module include the area difference value T1 of the cerebral cortex region, the area difference value T2 of the brainstem region, the area difference value T3 of the cerebellum region and the area difference value T4 of the marginal region and send them to the central unit.

5. The brain health level assessment system based on neuroimaging according to claim 4, characterized in that: The color module performs grayscale processing on the received image data information of the four brain regions. The grayscale processing formula is H=0.3R+0.58G+0.12B, where H is the grayscale value after processing, R is the red image, G is the green image, and B is the blue image, and R, G, and B are all within the grayscale value range of 0-255. The color module calculates the grayscale deviation value between the grayscale value H after the grayscale processing of the four brain regions and the grayscale value of the standard four brain regions. The grayscale deviation value calculated by the color module includes the grayscale deviation value H1 of the cerebral cortex region, the grayscale deviation value H2 of the brainstem region, the grayscale deviation value H3 of the cerebellum region, and the grayscale deviation value H4 of the edge region and sends them to the central unit.

6. The brain health level assessment system based on neuroimaging according to claim 1, characterized in that: The central unit receives all the data information sent by the area module and the color module in the analysis unit and calculates the morphology value XT and the activity value HD. The calculation formula of the morphology value XT is: , and the calculation formula of the activity value HD in the central unit is , where k1, k2, k3, k4 and l1, l2, l3, l4 are weights.

7. The brain health level assessment system based on neuroimaging according to claim 6, characterized in that: The central unit compares the calculated morphological value XT with its internal morphological threshold XY. When the morphological value XT is greater than the morphological threshold XY, the central unit sends an inspection instruction to the treatment unit. The treatment unit receives the inspection instruction and performs a review. The central unit compares the calculated activity value HD with its internal activity threshold HY. When the activity value HD is greater than the activity threshold HY, the central unit sends an inspection instruction to the treatment unit. When the morphological value XT is less than or equal to the morphological threshold XY and the activity value HD is less than or equal to the activity threshold HY, the central unit does not send any instruction.

8. The brain health level assessment system based on neuroimaging according to claim 7, characterized in that: The central unit sends the calculated morphology value XT and activity value HD to the evaluation unit, which receives the morphology value XT and activity value HD and calculates the evaluation value P. The calculation formula of the evaluation value P is: The evaluation unit arranges the evaluation values ​​P in ascending order from low to high, and the brain health level gradually decreases from low to high.

9. A method for assessing brain health based on neuroimaging, characterized by: Applying a neuroimaging-based brain health assessment system as described in claims 1-8 comprises the following steps: Step S1, generating a neuroimage image, performing denoising on the generated neuroimage image, and interpolating and supplementing the denoised neuroimage image with time differences between different slices; Step S2, segmenting the interpolated and supplemented neuroimaging image, calculating the area difference value and the grayscale deviation value after segmentation, and calculating the morphology value XT and the activity value HD based on the area difference value and the grayscale deviation value; Step S3, morphology value XT and activity value HD, when morphology value XT>morphology threshold value XY and activity value HD>activity threshold value HY, perform inspection and treatment, when morphology value XT≤morphology threshold value XY and activity value HD≤activity threshold value HY, perform evaluation; Step S4: During the evaluation, the evaluation value P is calculated based on the morphological value XT and the activity value HD, and the evaluation values ​​P are arranged in ascending order from low to high. The health level decreases in the order of arrangement.

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