A respiratory motion-based intensive care unit image processing method and system

Abstract

The present invention provides a respiratory motion-based intensive care unit image processing method and system, which belongs to the field of image processing and solves the problem of low efficiency in patient respiratory motion monitoring. The method is specifically as follows: Step S1: Obtain the patient's name, medical examination report and monitoring video; Step S2: Enhance the monitoring video, determine the patient's respiratory periodicity information, and judge whether the patient's respiratory rate is normal; if so, do not process it; if not, immediately alarm and obtain pathological information; Step S3: Construct a lesion index change equation based on the pathological information; Predict the patient's condition change based on the lesion index change equation, obtain diagnostic information, and provide feedback; Step S4: Continuously update and analyze the monitoring video until the patient recovers. The present invention determines whether the patient's breathing is normal by acquiring, analyzing and processing the patient's monitoring video, and issues a real-time alarm, thereby reducing the burden on medical staff.

Description

Technical Field

[0001] The present invention discloses a respiratory motion-based intensive care unit image processing method and system, relating to the field of image processing. Background Art

[0002] Existing methods or systems for processing respiratory motion images have the following deficiencies:

[0003] High requirements for image quality: Existing image processing systems rely on high-quality image data for analysis; if the image quality is poor (such as blur, noise interference, etc.), less image features will be provided, which will directly affect the monitoring accuracy and reliability of the system.

[0004] High requirements for operators: Existing image processing systems require professional operators to operate and maintain them; operators need to have certain medical knowledge and image processing technology background to accurately interpret the monitoring results output by the system and formulate corresponding treatment plans.

[0005] High equipment cost: The deployment of existing image processing systems usually requires high-performance image acquisition equipment and computing equipment. The cost of these devices is usually high, which limits the popularity and application of the system in medical institutions. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a respiratory motion-based intensive care unit image processing method and system, aiming to solve the problem of low efficiency in patient respiratory motion monitoring.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions: A respiratory motion-based ICU image processing method includes:

[0008] Step S1: Obtain the patient's name and medical examination report, and obtain the patient's monitoring video in the intensive care unit;

[0009] Step S2: Enhance the monitoring video and mark the patient's chest contour using background modeling technology; detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis on the patient's respiratory movement to obtain periodic information of the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no action is taken; if not, an alarm is immediately issued, and medical records of all target hospitals are obtained to analyze the patient's pathological condition;

[0010] Step S3: extracting medical record information with the same name as the patient's illness as pathological information; constructing a lesion index change equation based on the pathological information; predicting the patient's condition change based on the lesion index change equation, obtaining diagnostic information, and providing feedback;

[0011] Step S4: Continuously update and analyze the monitoring video according to the lesion condition and diagnostic information until the patient recovers.

[0012] Furthermore, the specific steps of step S2 are as follows:

[0013] Step S21: defining a processing step for marking the patient's chest contour;

[0014] Get the monitoring video resolution Lb×Hb;

[0015] Let time be t, and take the image at time t in the monitoring video as the monitoring image;

[0016] Define triple 2-1: (r （x，y） , g （x，y） , b （x，y） );(r （x，y） , g （x，y） , b （x，y） ) represents the RGB value of the pixel at (x, y) on the monitoring image; r （x，y） , g （x，y） and b （x，y） , respectively represent the color intensity of red, green and blue at the pixel point (x, y) on the monitoring image; the value range of x is: 1 ~ Lb, the value range of y is: 1 ~ Hb;

[0017] Step S22: taking the monitoring image as image A;

[0018] The RGB values ​​of all pixels on image A are recorded as (r （1，1） , g （1，1） , b （1，1） )~(r （Lb，Hb） , g （Lb，Hb） , b （Lb，Hb） ); calculate r （1，1） ~r （Lb，Hb） The average value of ar,g （1，1） ~g （Lb，Hb） The average value of ag,b （1，1） ~b （Lb，Hb） The average value ab;

[0019] The number of Gaussian filters is recorded as N, and the scale of the 1st to Nth Gaussian filters is recorded as d （1） ~d （N） , the standard deviation is denoted as σ （1） ~σ （N） ;

[0020] Calculate the weights of the 1st to Nth Gaussian filters and get ω （1） ~ω （N） ;

[0021] Step S23: Perform a transformation on the monitoring image to obtain the grayscale value gr （1，1） ~gr （Lb，Hb） .

[0022] Furthermore, the step S2 further includes:

[0023] Step S24: Count the number of pixels with gray value 0 and record it as ga （0） ;

[0024] Similarly, the number of pixels with a grayscale value of 255 is recorded as ga （255） ;

[0025] Let pj （z） Indicates the frequency of pixels whose grayscale value is less than or equal to z; ga （i） Represents the number of pixels whose grayscale value is i and whose grayscale value is less than or equal to z;

[0026] Define calculation formula 2-6: ;

[0027] Calculate the frequency pj of pixels with grayscale values ​​less than or equal to 0 to 255 （0） ~pj （255） ;

[0028] Step S25: Define calculation formula 2-7:

[0029] ;

[0030] Among them, gr （x，y） Indicates (Rr （x，y） , Rg （x，y） , Rb （x，y） ), Indicates that the grayscale value is less than or equal to gr （x，y） Pixel frequency, xf （x，y） Indicates gr （x，y） The transformation value of

[0031] Perform adaptive contrast enhancement on the monitoring image to obtain xf （1，1） ~xf （Lb，Hb） ;

[0032] xf （1，1） ~xf （Lb，Hb） The corresponding image is taken as the quasi-image;

[0033] Use the pyrMeanShiftFiltering function in the OpenCV library to align the image for background removal, and then use the adaptivethreshold function to perform adaptive threshold segmentation on the image to mark the patient's chest contour;

[0034] Step S26: Get the current time, recorded as dh hours dm minutes;

[0035] Extract the patient's monitoring video within dh hour (dm-1) minutes as the target video; use the picture per second in the target video as the target picture; calculate the patient's chest movement information in the past 1 minute;

[0036] Step S27: Based on the patient's chest transverse expansion and contraction velocity uv in the past 1 minute （1） ~uv （60） and the thoracic longitudinal velocity vv （1） ~vv （60） , analyzing the patient's respiratory movements.

[0037] Furthermore, the specific steps of step S22 are as follows:

[0038] Step S221: Calculate r （1，1） ~r （Lb，Hb） The mean square error Sr: ;

[0039] g （1，1） ~g （Lb，Hb） The mean square error Sg: ;

[0040] b （1，1） ~b （Lb，Hb） The mean square error Sb: ;

[0041] Step S222: back up image A to obtain a duplicate image;

[0042] Extract d （1） ~d （N） The minimum value dl in the equation 2-2 is defined as follows:

[0043] ;

[0044] Among them, Fr （k）（x，y） 、Fr （k）（x，y） 、Fr （k）（x，y） , respectively represent the color intensity of red, green and blue at (x, y) of the monitoring image after being processed by the k-th Gaussian filter;

[0045] d （k） and σ （k） , respectively represent the scale and standard deviation of the k-th Gaussian filter;

[0046] Use the 1st to Nth Gaussian filters to process the heavy image and obtain image A （1） ~Image A （N） ;

[0047] Step S223: Calculate image A（1） The mean square deviation of the color intensities of red, green, and blue is obtained as sr （1） 、sg （1） and sb （1） ;

[0048] Image A （1） The RGB values ​​of all pixels are recorded as (r （1）（1，1） , g （1）（1，1） , b （1）（1，1） )~(r （1）（Lb，Hb） , g （1）（Lb，Hb） , b （1）（Lb，Hb） ); calculate r （1）（1，1） ~r （1）（Lb，Hb） The average value of （1） , g （1）（1，1） ~g （1）（Lb，Hb） The average value of ag （1） , b （1）（1，1） ~b （1）（Lb，Hb） The average value of ab （1） .

[0049] Furthermore, the subsequent steps of step S223 are as follows:

[0050] Step S224: Calculate r （1）（1，1） ~r （1）（Lb，Hb） The mean square error sr （1） : ;

[0051] g （1）（1，1） ~g （1）（Lb，Hb） The mean square error sg （1） : ;

[0052] b （1）（1，1） ~b （1）（Lb，Hb） The mean square error sb （1） : ;

[0053] Step S225: Calculate image A （2） To Image A （N） The mean square deviation of the color intensities of red, green, and blue is obtained as sr （2） ~sr （N） , sg （2） ~sg （N） and sb （2） ~sb （N） ;

[0054] Let image A （k） The mean square deviation of the color intensity of red, green and blue is sr （k） 、sg （k） and sb （k）;The weight coefficient of the kth Gaussian filter is zq （k） ;

[0055] Define calculation formula 2-3: ;

[0056] Calculate the weight coefficients zq of the 1st to Nth Gaussian filters （1） ~zq （N） ;

[0057] Step S226: Calculate zq （1） ~zq （N） The sum of is denoted as aq;

[0058] The weight coefficient of the kth Gaussian filter is recorded as zq （k） , the weight is denoted as ω （1） , define calculation formula 2-4: ω （k） =(zq （k） / aq);

[0059] Calculate the weights ω of the 1st to Nth Gaussian filters （1） ~ω （N） .

[0060] Furthermore, the specific steps of step S23 are as follows:

[0061] Step S231: Define calculation formula 2-5:

[0062] ;

[0063] Among them, Rr （x，y） , Rg （x，y） and Rb （x，y） , respectively represent the color intensity of the pixel point (x, y) on the monitoring image after red, green and blue transformation;

[0064] Fr （k）（x，y） 、Fg （k）（x，y） 、Fb （k）（x，y） , respectively represent the color intensity of red, green and blue at (x, y) of the monitoring image after being processed by the k-th Gaussian filter;

[0065] Step S232: Perform a transformation on the monitoring image to obtain (Rr （1，1） , Rg （1，1） , Rb （1，1） )~(Rr （Lb，Hb） , Rg （Lb，Hb） , Rb （Lb，Hb） );

[0066] (Rr （1，1） , Rg （1，1） , Rb （1，1）)~(Rr （Lb，Hb） , Rg （Lb，Hb） , Rb （Lb，Hb） ) is converted into grayscale value and gr （1，1） ~gr （Lb，Hb） .

[0067] Furthermore, the specific steps of step S26 are as follows:

[0068] Step S261: Set the picture at the dtth second in the target video as picture Pr （dt） , the (dt+1) second picture is picture Pr （dt+1） ;

[0069] Step S262: Count the pictures Pr （dt） The number of pixels of the patient's chest contour in st;

[0070] Put the picture Pr （dt） The pixel points of the patient's chest contour are used as punctuation points;

[0071] The positions of the 1st to the stth punctuation marks are recorded as (sx （1） ,sy （1） )~(sx （st） ,sy （st） );

[0072] In the picture Pr （dt） In the above example, the grayscale values ​​of the 1st to the stth punctuation points are extracted and recorded as Ii (sx （1） ,sy （1） )~Ii(sx （st） ,sy （st） );

[0073] Step S263: In the picture Pr （dt+1） Extract the positions corresponding to the 1st to stth punctuation marks (tx （1） ,ty （1） )~(tx （st） ,ty （st） );

[0074] Extract (tx （1） ,ty （1） )~(tx （st） ,ty （st） ) is the gray value of the pixel Io (tx （1） ,ty （1） )~Io(tx （st） ,ty （st） ).

[0075] Furthermore, the subsequent steps of step S263 are as follows:

[0076] Step S264: Picture Pr（dt） In the example, the position of the rth punctuation mark (sx （r） ,sy （r） ), grayscale Ii (sx （r） ,sy （r） );

[0077] Picture Pr （dt+1） In the , the position of the rth punctuation mark (tx （r） ,ty （r） ), Grayscale Io (tx （r） ,ty （r） ); Define calculation formula 2-8:

[0078] ;

[0079] Among them, Ix （r） , Iy （r） and Iio （r） , respectively represent the horizontal gradient, vertical gradient and brightness gradient of the rth punctuation point;

[0080] Calculate the horizontal gradient Ix from the 1st to the stth mark （1） ~Ix （st） , longitudinal gradient Iy （1） ~Iy （st） , brightness gradient 110 （1） ~Iio （st） ;

[0081] Step S265: Record the patient's chest transverse expansion and contraction velocity at dt seconds as uv （dt） , the longitudinal velocity of the thoracic cavity is recorded as vv （dt） ;

[0082] Define calculation formula 2-9:

[0083] ;

[0084] Step S266: Extract the patient's monitoring image at dh hour (dm-2) minute 59 seconds as the reference image; set the value of dt to 1, and calculate the patient's chest transverse expansion and contraction velocity uv within dh hour (dm-1) minute （1） ~uv （60） , chest longitudinal velocity vv （1） ~vv （60） ;

[0085] The uv （1） ~uv （60） and vv （1） ~vv （60） As chest movement information.

[0086] Furthermore, the specific steps of step S27 are as follows:

[0087] Step S271: Assume that the patient's chest cavity transverse expansion and contraction velocity at the qth second is uv （q） , the longitudinal velocity of the chest cavity is vv （q） ;

[0088] Obtain the chest cavity transverse expansion and contraction velocity interval of the first to the hoth healthy person within 1 minute, recorded as [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ]; the interval of the longitudinal expansion and contraction velocity of the chest cavity is recorded as [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ]; where ho represents the number of healthy people;

[0089] Step S272: Assume that the patient's chest cavity transverse expansion and contraction velocity at the uth second within 1 minute is uvr （u） , the longitudinal velocity of the chest cavity is vvr （u） , respiratory frequency is ff; the value range of u is: 1~60;

[0090] Define the relation S:

[0091] ;

[0092] Among them, uvr （s） and vvr （s） , respectively represent the transverse expansion and contraction velocity and longitudinal velocity of the chest cavity at the sth second within 1 minute;

[0093] ε represents the judgment value; j represents the imaginary unit;

[0094] Step S273: [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ] and [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ] is substituted into the relation S to calculate the respiratory frequency range of a healthy person, and we get [fh （1） , fh （ho） ];

[0095] The uv （1） ~uv （60） and vv （1） ~vv （60） Substituting into the relationship S, calculate the patient's respiratory rate, recorded as hf;

[0096] Step S274: Determine whether hf is in [fh （1） , fh （ho） ] interval;

[0097] If it exists, no processing will be done;

[0098] If not, call the police immediately and analyze the patient's pathological condition.

[0099] A respiratory motion-based ICU image processing system includes:

[0100] Image acquisition module: used to obtain the patient's name and medical examination report, and obtain the patient's monitoring video in the intensive care unit;

[0101] Image analysis module: used to enhance monitoring videos and mark the patient's chest contour through background modeling technology; detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis on the patient's respiratory movement to obtain periodic information of the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no action is taken; if not, an alarm is immediately issued, and medical records of all target hospitals are obtained to analyze the patient's pathological condition;

[0102] Patient monitoring module: used to extract medical records with the same name as the patient's disease as pathological information; construct a lesion index change equation based on the pathological information; predict the patient's condition changes based on the lesion index change equation, obtain diagnostic information, and provide feedback;

[0103] Continuous monitoring module: used to continuously update and analyze monitoring videos until the patient recovers.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] Non-contact monitoring: The present invention adopts a non-contact monitoring method, which does not require direct contact with the patient's body, avoiding the risk of cross-infection that may be brought about by traditional monitoring methods; and non-contact monitoring also makes patients more comfortable during the monitoring process, reduces the sense of restraint caused by the monitoring equipment, and is conducive to the patient's recovery.

[0106] Real-time monitoring and early warning: The present invention can monitor the patient's respiratory status in real time, including key indicators such as respiratory rate and respiratory amplitude; once these indicators exceed the normal range, the system can immediately issue an early warning signal to remind medical staff to take timely measures.

[0107] Data recording and analysis: The present invention can automatically record the patient's respiratory movement data. By analyzing a large amount of data, medical staff can gain an in-depth understanding of the changing patterns of the patient's respiratory status, providing a basis for formulating more scientific and reasonable treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0109] Figure 1 Schematic diagram of the method of the present invention;

[0110] Figure 2 Schematic diagram of the system of the present invention;

[0111] Figure 3 It is a schematic diagram of a ward of the present invention. DETAILED DESCRIPTION

[0112] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0113] Example 1

[0114] See also Figure 1 and Figure 3 , a respiratory motion-based ICU image processing method includes:

[0115] Step S1: Obtain the patient's name and medical examination report, and obtain the patient's monitoring video in the intensive care unit;

[0116] Step S2: Enhance the monitoring video and mark the patient's chest contour using background modeling techniques (such as Gaussian mixture models); detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis of the patient's respiratory movement (using Fourier transform) to obtain periodic information about the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no action is taken (and step S3 is skipped); if not, an alarm is immediately issued, and medical records from all target hospitals are obtained to analyze the patient's pathological condition;

[0117] It should be noted that the "target hospital" in the present invention refers to: a municipal hospital that uses the present invention (a respiratory motion-based intensive care unit image processing method and system) to perform patient respiratory motion monitoring;

[0118] The specific steps of step S2 are as follows:

[0119] Step S21: defining a processing step for marking the patient's chest contour;

[0120] Get the resolution of the monitoring video, recorded as Lb × Hb; (i.e., on the monitoring image, there are Lb pixels horizontally and Hb pixels vertically)

[0121] Let time be t, and take the image at time t in the monitoring video as the monitoring image;

[0122] Define triple 2-1: (r （x，y） , g （x，y） , b （x，y） ); (numerical representation)

[0123] (r （x，y） , g （x，y） , b （x，y） ) represents the RGB value of the pixel at (x, y) on the monitoring image; (RGB value has 3 specific values)

[0124] r （x，y） , g （x，y） and b （x，y） , respectively represent the color intensity of red (channel), green (channel) and blue (channel) of the pixel at (x, y) on the monitoring image; the value range of x is: 1~Lb, the value range of y is: 1~Hb;

[0125] Step S22: taking the monitoring image as image A;

[0126] The RGB values ​​of all pixels on image A are recorded as (r （1，1） , g （1，1） , b （1，1） )~(r （Lb，Hb） , g （Lb，Hb） , b （Lb，Hb） ); calculate r （1，1） ~r （Lb，Hb） The average value of ar,g （1，1） ~g （Lb，Hb） The average value of ag,b （1，1） ~b （Lb，Hb） The average value ab;

[0127] The number of Gaussian filters is recorded as N, and the scale of the 1st to Nth Gaussian filters is recorded as d （1） ~d （N） , the standard deviation is denoted as σ （1） ~σ （N） ;

[0128] (It should be noted that the value of N is generally 3. The scale of the Gaussian filter represents the window size or kernel size of the Gaussian filter (common scales of Gaussian filters are: 3×3, 5×5, 7×7, etc.); users or relevant technical personnel can adjust the number and scale of Gaussian filters according to actual needs)

[0129] Calculate the weights of the 1st to Nth Gaussian filters and get ω （1） ~ω （N） ;

[0130] Step S221: Calculate r （1，1） ~r（Lb，Hb） The mean square error Sr: ;

[0131] g （1，1） ~g （Lb，Hb） The mean square error Sg: ;

[0132] b （1，1） ~b （Lb，Hb） The mean square error Sb: ;

[0133] Step S222: back up N copies of image A to obtain (N) duplicate images;

[0134] Extract d （1） ~d （N） The minimum value in is denoted as dl; define calculation formula 2-2:

[0135] ;

[0136] Among them, Fr （k）（x，y） 、Fg （k）（x，y） 、Fb （k）（x，y） , respectively represent the color intensity of red (channel), green (channel) and blue (channel) at the monitoring image (x, y) after being processed by the k-th Gaussian filter;

[0137] d （k） and σ （k） , respectively represent the scale and standard deviation of the kth Gaussian filter, and the value range of k is: 1~N;

[0138] (According to formula 2-2) Use the 1st to Nth Gaussian filters to process the heavy image and obtain image A （1） ~Image A （N） ;

[0139] Step S223: Calculate image A （1） The mean square error of the color intensities of red (channel), green (channel) and blue (channel) is obtained by （1） 、sg （1） and sb （1） ;

[0140] Image A （1） The RGB values ​​of all pixels are recorded as (r （1）（1，1） , g （1）（1，1） , b （1）（1，1） )~(r （1）（Lb，Hb） , g （1）（Lb，Hb） , b （1）（Lb，Hb） ); calculate r （1）（1，1） ~r （1）（Lb，Hb） The average value of （1） , g（1）（1，1） ~g （1）（Lb，Hb） The average value of ag （1） , b （1）（1，1） ~b （1）（Lb，Hb） The average value of ab （1） ;

[0141] Calculate r （1）（1，1） ~r （1）（Lb，Hb） The mean square error sr （1） : ;

[0142] g （1）（1，1） ~g （1）（Lb，Hb） The mean square error sg （1） : ;

[0143] b （1）（1，1） ~b （1）（Lb，Hb） The mean square error sb （1） : ;

[0144] Step S224: Repeat the calculation of sr （1） 、sg （1） and sb （1） Steps to calculate image A （2） To Image A （N） The mean square error of the color intensities of red (channel), green (channel) and blue (channel) is obtained by （2） ~sr （N） , sg （2） ~sg （N） and sb （2） ~sb （N） ;

[0145] Let image A （k） The mean square error of the color intensity of red (channel), green (channel) and blue (channel) is sr （k） 、sg （k） and sb （k） ;The weight coefficient of the kth Gaussian filter is zq （k） ;

[0146] Define calculation formula 2-3: ;

[0147] (According to formula 2-3) calculate the weight coefficient zq of the 1st to Nth Gaussian filters （1） ~zq （N） ;

[0148] Step S225: Calculate zq （1） ~zq （N） The sum of is denoted as aq;

[0149] The weight coefficient of the kth Gaussian filter is recorded as zq （k） , the weight is denoted as ω （1） , define calculation formula 2-4: ω （k） =(zq （k） / aq);

[0150] (According to formula 2-4) Calculate the weights ω of the 1st to Nth Gaussian filters （1） ~ω （N） ;

[0151] Step S23: Define calculation formula 2-5:

[0152] ;

[0153] Among them, Rr （x，y） , Rg （x，y） and Rb （x，y） , respectively represent the color intensity of the pixel point (x, y) after red (channel), green (channel) and blue (channel) transformation on the monitoring image;

[0154] Fr （k）（x，y） 、Fr （k）（x，y） 、Fr （k）（x，y） , respectively represent the color intensity of red (channel), green (channel) and blue (channel) at (x, y) of the monitoring image after being processed by the k-th Gaussian filter;

[0155] According to formula 2-5, the monitoring image is transformed once to obtain (Rr （1，1） , Rg （1，1） , Rb （1，1） )~(Rr （Lb，Hb） , Rg （Lb，Hb） , Rb （Lb，Hb） );

[0156] (Using weighted average algorithm) (Rr （1，1） , Rg （1，1） , Rb （1，1） )~(Rr （Lb，Hb） , Rg （Lb，Hb） , Rb （Lb，Hb） ) is converted into grayscale value (grayscale value range: 0 ~ 255), and gr （1，1） ~gr （Lb，Hb） ;

[0157] Step S24: Count the number of pixels with gray value 0 and record it as ga （0） ;

[0158] The number of pixels with a gray value of 1 is denoted as ga （1） ;

[0159] By analogy, the number of pixels with a grayscale value of 255 is recorded as ga （255） ;

[0160] Let pj （z） Indicates the frequency of pixels whose grayscale value is less than or equal to z. The value range of z is: 0 to 255; ga （i） Indicates the number of pixels whose grayscale value is i and whose grayscale value is less than or equal to z. The value range of i is: 0~z;

[0161] Define calculation formula 2-6: ;

[0162] (According to formula 2-6) calculate the frequency pj of pixels with grayscale values ​​less than or equal to 0 to 255 （0） ~pj （255） ;

[0163] Step S25: Define calculation formula 2-7:

[0164] ;

[0165] Among them, gr （x，y） Indicates (Rr （x，y） , Rg （x，y） , Rb （x，y） ), Indicates that the grayscale value is less than or equal to gr （x，y） Pixel frequency, xf （x，y） Indicates gr （x，y） The transformation value of

[0166] Based on gr （1，1） ~gr （Lb，Hb） , (according to formula 2-7) perform adaptive contrast enhancement on the monitoring image and obtain xf （1，1） ~xf （Lb，Hb） ;

[0167] xf （1，1） ~xf （Lb，Hb） The corresponding image is taken as the quasi-image;

[0168] Use the pyrMeanShiftFiltering function in the OpenCV library to align the image for background removal, and then use the adaptivethreshold function to align the image for adaptive threshold segmentation (obtain the patient's body contour; identify the patient's body contour,) and mark the patient's chest contour;

[0169] Step S26: Get the current time, recorded as dh hours dm minutes;

[0170] Extract the patient's monitoring video within dh hours (dm-1) minutes (from the complete monitoring video) as the target video; use the image per second in the target video as the target image; (the number of target images is 60) calculate the patient's chest motion information in the last 1 minute;

[0171] Step S261: Set the target picture at the dtth second in the target video (after processing in steps S21 to S25) as picture Pr （dt） , the target picture at (dt+1) seconds is picture Pr （dt+1） ;

[0172] Step S262: Mark the image Pr （dt） The chest contour of the patient was counted (Picture Pr （dt） The number of chest contour pixels in patients) is recorded as st;

[0173] Put the picture Pr （dt） The pixel points of the patient's chest contour are used as punctuation points;

[0174] The positions of the 1st to the stth punctuation marks are recorded as (sx （1） ,sy （1） )~(sx （st） ,sy （st） );

[0175] In the picture Pr （dt） Extract the 1st to the stth punctuation marks (i.e. (sx （1） ,sy （1） )~(sx （st） ,sy （st） The gray value of the pixel at ) is recorded as Ii (sx （1） ,sy （1） )~Ii(sx （st） ,sy （st） );

[0176] Step S263: (In the picture Pr （dt） (sx) （1） ,sy （1） )~(sx （st） ,sy （st） ) as punctuation points; (the number of punctuation points is st)

[0177] In the picture Pr （dt+1） In the example, the positions corresponding to the 1st to the stth punctuation marks are extracted and recorded as (tx （1） ,ty （1） )~(tx （st） ,ty （st） );

[0178] (In the picture Pr（dt+1） Extract (tx （1） ,ty （1） )~(tx （st） ,ty （st） ) is the grayscale value of the pixel at tx （1） ,ty （1） )~Io(tx （st） ,ty （st） );

[0179] Step S264: The picture Pr （dt） In the example, the position of the rth punctuation mark is recorded as (sx （r） ,sy （r） ), grayscale is recorded as Ii (sx （r） ,sy （r） );

[0180] Put the picture Pr （dt+1） In the example, the position of the rth punctuation mark is denoted as (tx （r） ,ty （r） ), grayscale is recorded as Io (tx （r） ,ty （r） ); The value range of r is: 1~st;

[0181] Define calculation formula 2-8:

[0182] ;

[0183] Among them, 1x （r） , Iy （r） and Iio （r） , respectively represent the horizontal (position) gradient, vertical (position) gradient and brightness gradient of the rth punctuation point;

[0184] (According to formula 2-8) calculate the horizontal (position) gradient Ix from the 1st to the stth mark （1） ~Ix （st） , longitudinal (position) gradient Iy （1） ~Iy （st） , brightness gradient 110 （1） ~Iio （st） ;

[0185] Step S265: The patient's chest transverse expansion and contraction velocity (i.e., chest expansion and chest contraction) at dt seconds is recorded as uv （dt） , the longitudinal velocity of the thoracic cavity is recorded as vv （dt） ;

[0186] Define calculation formula 2-9:

[0187] ;

[0188] Step S266: (From the complete monitoring video) extract the monitoring image of the patient at dh hours (dm-2) minutes and 59 seconds as the reference image; set the value of dt (in step S261) to 1, and calculate the patient's chest transverse expansion and contraction velocity uv within dh hours (dm-1) minutes （1） ~uv （60） , chest longitudinal velocity vv （1） ~vv （60） ;

[0189] The uv （1） ~uv （60） and vv （1） ~vv （60） As chest movement information;

[0190] Step S27: Based on the patient's chest transverse expansion and contraction velocity uv in the past 1 minute （1） ~uv （60） and the thoracic longitudinal velocity vv （1） ~vv （60） , analyzing the patient’s respiratory movements;

[0191] Step S271: Assume that the patient's chest cavity transverse expansion and contraction velocity at the qth second is uv （q） , the longitudinal velocity of the chest cavity is vv （q） ;

[0192] Obtain the chest cavity transverse expansion and contraction velocity interval of the first to the hoth healthy person within 1 minute, recorded as [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ]; the interval of the longitudinal expansion and contraction velocity of the chest cavity is recorded as [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ]; where ho represents the number of healthy people;

[0193] Step S272: Assume that the transverse expansion and contraction speed of the chest cavity of the patient (or healthy person) at the uth second in 1 minute is uvr （u） , the longitudinal velocity of the chest cavity is vvr （u） , respiratory frequency is ff; the value range of u is: 1~60;

[0194] Define the relation S:

[0195] ;

[0196] Among them, uvr （s） and vvr （s）, respectively represent the transverse expansion and contraction velocity and longitudinal velocity of the chest cavity of a patient (or healthy person) at the sth second within 1 minute, and the value range of s is: 1~u;

[0197] ε represents the (error) judgment value; (The value of ε is 0.1, and users or relevant technical personnel can adjust the value of ε according to actual needs)

[0198] j represents the imaginary unit, and e represents the base of the natural logarithm;

[0199] Step S273: [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ] and [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ]Substitute into the relation S (uvr （u） and vvr （u） and uvr （s） and vvr （s） ), calculate the respiratory frequency range of a healthy person and get [fh （1） , fh （ho） ];

[0200] The uv （1） ~uv （60） and vv （1） ~vv （60） Substitute into the relation S (uvr （u） and vvr （u） and uvr （s） and vvr （s） ), calculate the patient's respiratory rate, recorded as hf;

[0201] Step S274: Determine whether hf is in [fh （1） , fh （ho） ] interval;

[0202] If yes, it means the patient is breathing normally and no treatment is required (and step S3 is skipped);

[0203] If not, an alarm is immediately issued, and the medical records of all target hospitals are obtained, the patient's pathological condition is analyzed, and step S3 is entered.

[0204] Step S3: extract the medical record information with the same name as the patient's illness as the pathological information; construct a lesion index change equation based on the pathological information; predict the patient's condition change based on the lesion index change equation, obtain diagnostic information, and provide feedback; the specific steps of step S3 are as follows:

[0205] Step S31: extracting the medical records with the same disease name as the patient from all the medical records of the target hospital as pathological information; and counting the number of patients in the pathological information, which is recorded as mu;

[0206] The first patient in the pathology information is designated as patient C. The treatment time of patient C is recorded as td (unit: day).

[0207] The pathogen content of patient C from day 1 to day td is recorded as: sy （1） 、sy （2） ~sy （td） ;

[0208] Immune cell content is recorded as: im （1） 、im （2） ~im （td） ;

[0209] Non-immune cell content is recorded as: ni （1） 、ni （2） ~ni （td） ;

[0210] The content of liver and kidney secretions is recorded as: lk （1） lk （2） ~lk （td） ;

[0211] Obtain the content of disease markers in healthy people as syy, immune cell content as imm, non-immune cell content as nii, liver and kidney secretion content as lkk, and construct the pathological equation SS （1） ;

[0212] Step S311: Analyze the changes in the pathological index of patient C from the first to the second day and calculate the Kalman gain Kdk （1-2） ;

[0213] Step S3111: Construct the pathological index matrix of patient C on the first day, denoted as B （1） , the lesion index matrix on the second day is recorded as B （2） ;

[0214] Matrix B （1） The mathematical representation of is: ;

[0215] Matrix B （2） The mathematical representation of is: ;

[0216] Step S3112: Calculate matrix B （1） Becomes matrix B （2） The state transfer matrix of （1-2） Matrix B （1-2） The calculation formula is:

[0217] ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0218] Step S3113: Calculate matrix B （1） The difference matrix of （1） ; Matrix RB （1） The calculation formula is:

[0219] ; Where - represents matrix subtraction;

[0220] Calculate matrix B （2） The difference matrix of （2） ; Matrix RB （2） The calculation formula is:

[0221] ;

[0222] Calculate matrix B （1） Becomes matrix B （2） The error transition matrix is ​​denoted as matrix QB （1-2） ; Matrix QB （1-2） The mathematical formula is:

[0223] QB （1-2） =RB （2） -RB （1） ;

[0224] Step S3114: Calculate sy （1） 、im （1） 、ni （1） and lk （1） The average value is denoted as ah;

[0225] Calculate matrix B （1） The decentralized matrix of （1） ;APd （1） The calculation formula is: APd （1） =B （1） -[ah×(I （4） ) T ]; where - represents matrix subtraction, T represents the transpose of the matrix, and I （4） represents a (4×1) matrix of all 1s;

[0226] Calculate matrix B （1） The error covariance matrix of （1） Matrix Pd （1） The calculation formula is:

[0227] Pd （1）= (1 / 4) × (APd （1） T ×APd （1） );

[0228] Step S3115: Calculate matrix B （2） The pre-error covariance matrix of （2） ;Pk （2） The calculation formula is:

[0229] ;

[0230] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0231] Calculate the Kalman gain Kdk （1-2） ,Kdk （1-2） The calculation formula is:

[0232] ;

[0233] Step S312: Repeat the calculation of the Kalman gain Kdk （1-2） The same process is used to analyze the changes in the lesion indicators of patient C from the 2nd to the 3rd day to the (td-1) to the td day, and calculate the Kalman gain Kdk （2-3） ~Kdk （（td-1）-td） ;

[0234] Calculate Kdk （1-2） ~Kdk （（td-1）-td） The average of , denoted as Kkk;

[0235] Step S313: Assume that the disease marker content corresponding to patient C on day k is sy （k） , immune cell content is im （k） , non-immune cell content is ni （k） , liver and kidney secretion content is lk （k） ;

[0236] Assume that the symptom marker content of patient C on day (k+1) is sy （k+1） , immune cell content is im （k+1） , non-immune cell content is ni （k+1） , liver and kidney secretion content is lk （k+1） ;

[0237] Construct Equation SS （1） The initial mathematical expression is:

[0238] ;

[0239] Wherein, QC represents the (4×1) error matrix, and all initial parameters in the matrix QC are 0;

[0240] Step S314: Set sy （1） ~sy （td） 、im （1） ~im （td） 、ni （1） ~ni （td） and lk （1） ~lk （td） Substitute into equation SS (1,1) and calculate the value of QC using MATLAB software to obtain CC;

[0241] Substitute CC into the equation SS （1） From the initial mathematical expression, we get the equation SS(1,1):

[0242] ;

[0243] Process A315: Repeated construction of equation SS （1） The same process is used to construct the lesion equations corresponding to the 2nd to muth patients in the pathological information, and the equation SS is obtained. （2） ~ Equation SS （mu） ;

[0244] Using MATLAB software, the equation SS （1） ~ Equation SS （mu） Perform fitting to obtain the equation for the change of lesion index;

[0245] Process A32: Obtain the current pathogen content in the patient's medical examination report, recorded as ym, immune cell content as mu, non-immune cell content as nu, and liver and kidney secretion content as li;

[0246] Substitute ym, mu, nu, and li into the lesion index change equation to calculate the patient's lesion index change on the second day as diagnostic information.

[0247] Step S4: Continuously update and analyze the monitoring video until the patient recovers.

[0248] Example 2

[0249] See also Figure 2 , a respiratory motion-based ICU image processing system comprises:

[0250] Image acquisition module: used to obtain the patient's name and medical examination report, and obtain the patient's monitoring video in the intensive care unit;

[0251] Image analysis module: used to enhance the monitoring video and mark the patient's chest contour through background modeling technology (such as Gaussian mixture model); detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis of the patient's respiratory movement (using Fourier transform) to obtain periodic information of the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no processing is performed (and step S3 is skipped); if not, an alarm is immediately issued, and medical records of all target hospitals are obtained to analyze the patient's pathological condition;

[0252] Patient monitoring module: used to extract medical records with the same name as the patient's disease as pathological information; construct a lesion index change equation based on the pathological information; predict the patient's condition changes based on the lesion index change equation, obtain diagnostic information, and provide feedback;

[0253] Continuous monitoring module: used to continuously update and analyze monitoring videos until the patient recovers.

[0254] The above formulas are all dimensionless and calculated by taking their numerical values. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, the size of the settings is to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the quantized value, it is fine.

[0255] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for processing images in a ward based on respiratory motion, characterized in that: The method comprises: Step S1: Obtaining the patient's monitoring video; Step S2: Enhance the monitoring video and mark the patient's chest contour using background modeling technology; detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis on the patient's respiratory movement to obtain periodic information of the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no treatment is performed; if not, the patient's pathological condition is analyzed; Get the picture in the monitoring video as the target picture, and the picture at the dt second is picture Pr （dt） , the (dt+1) second picture is picture Pr （dt+1） ; Picture Pr （dt） In the example, the position of the rth punctuation mark (sx （r） ,sy （r） ), grayscale Ii (sx （r） ,sy （r） );Picture Pr （dt+1） In the , the position of the rth punctuation mark (tx （r） ,ty （r） ), Grayscale Io (tx （r） ,ty （r） ); Define calculation formula 2-8: ； Among them, Ix （r） , Iy （r） and Iio （r） , respectively represent the horizontal gradient, vertical gradient and brightness gradient of the rth punctuation point; calculate the horizontal gradient Ix from the 1st to the stth punctuation point （1） ~Ix （st） , longitudinal gradient Iy （1） ~Iy （st） , brightness gradient 110 （1） ~Iio （st） ; The patient's chest transverse expansion and contraction velocity at dt seconds is recorded as uv （dt） , the longitudinal velocity of the thoracic cavity is recorded as vv （dt） ; Define calculation formula 2-9: ； Extract the patient's monitoring image at dh hour (dm-2) minute 59 seconds as the baseline image; set the value of dt to 1 and calculate the patient's chest transverse expansion and contraction velocity uv within dh hour (dm-1 minute) （1） ~uv （60） and the thoracic longitudinal velocity vv （1） ~vv （60） As chest movement information; Analyze the patient's respiratory motion based on the transverse expansion and contraction velocity of the chest cavity and the longitudinal velocity of the chest cavity; Assume that the patient's chest cavity transverse expansion and contraction velocity in the qth second is uv （q） , the longitudinal velocity of the chest cavity is vv （q） ; Obtain the chest cavity transverse expansion and contraction velocity interval of the first to the hoth healthy person within 1 minute, recorded as [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ]; the interval of the longitudinal expansion and contraction velocity of the chest cavity is recorded as [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ]; Assume that the patient's chest cavity transverse expansion and contraction speed at the uth second in 1 minute is uvr （u） , the longitudinal velocity of the chest cavity is vvr （u） , the respiratory frequency is ff; define the relationship S: ； Among them, uvr （s） and vvr （s） , respectively, represent the patient's chest transverse expansion and contraction velocity and chest longitudinal velocity at the sth second within 1 minute; ε represents the judgment value; j represents the imaginary unit; [uvl （1，ho） ~uvl （60，ho） , uvm （1，ho） ~uvm （60，ho） ] and [vvl （1，ho） ~vvl （60，ho） , vvm （1，ho） ~vvm （60，ho） ] is substituted into the relation S to calculate the respiratory frequency range of a healthy person, and we get [fh （1） , fh （ho） ]; The uv （1） ~uv （60） and vv （1） ~vv （60） Substitute into the relationship S and calculate the patient's respiratory rate, recorded as hf; determine whether hf is within [fh （1） , fh （ho） ] interval; If the patient is present, no action will be taken; if the patient is not present, an alarm will be immediately issued and the patient's pathological condition will be analyzed; Step S3: constructing a lesion index change equation; predicting the patient's condition change and obtaining diagnostic information; Step S4: Continuously update and analyze the monitoring video according to the lesion condition and diagnostic information until the patient recovers.

2. The method for processing ICU images based on respiratory motion according to claim 1, characterized in that: The specific steps of step S2 are as follows: Step S21: defining a processing step for marking the patient's chest contour; obtaining a resolution of the monitoring video of Lb×Hb; and using the image at time t in the monitoring video as the monitoring image; Define triple 2-1: (r （x，y） , g （x，y） , b （x，y） );(r （x，y） , g （x，y） , b （x，y） ) represents the RGB value of the pixel at (x, y) on the monitoring image; Step S22: The monitoring image is taken as image A; the RGB values ​​of all pixels on image A are recorded as (r （1，1） , g （1，1） , b （1，1） )~(r （Lb，Hb） , g （Lb，Hb） , b （Lb，Hb） ); calculate r （1，1） ~r （Lb，Hb） The average value of ar,g （1，1） ~g （Lb，Hb） The average value of ag,b （1，1） ~b （Lb，Hb） The average value ab; The number of Gaussian filters is recorded as N, and the scale of the 1st to Nth Gaussian filters is recorded as d （1） ~d （N） , the standard deviation is denoted as σ （1） ~σ （N） ; Calculate the weights of the 1st to Nth Gaussian filters and get ω （1） ~ω （N） ; Step S23: Perform a transformation on the monitoring image to obtain the grayscale value gr （1，1） ~gr （Lb，Hb） .

3. The method for processing ICU images based on respiratory motion according to claim 2, characterized in that: The step S2 further includes: Step S24: Count the number of pixels with gray value 0 and record it as ga （0） ; Similarly, the number of pixels with a grayscale value of 255 is recorded as ga （255） ; Let pj （z） Indicates the frequency of pixels whose grayscale value is less than or equal to z; ga （i） Represents the number of pixels whose grayscale value is i and whose grayscale value is less than or equal to z; Define calculation formula 2-6: ; Calculate the frequency pj of pixels with grayscale values ​​less than or equal to 0 to 255 （0） ~pj （255） ; Step S25: Define calculation formula 2-7: ； Among them, gr （x，y） represents the grayscale value, Indicates that the grayscale value is less than or equal to gr （x，y） Pixel frequency, xf （x，y） Indicates gr （x，y） The transformation value of Perform adaptive contrast enhancement on the monitoring image to obtain xf （1，1） ~xf （Lb，Hb） ; xf （1，1） ~xf （Lb，Hb） The corresponding image is used as a quasi-image; background removal is performed on the aligned image, adaptive threshold segmentation is performed on the aligned image, and the chest contour of the patient is marked; Step S26: obtaining the current time dh hours and dm minutes; extracting the patient's monitoring video within dh hours (dm-1) minutes as the target video; using the picture per second in the target video as the target picture; and calculating the patient's chest movement information in 1 minute; Step S27: Based on the patient's chest transverse expansion and contraction velocity uv within 1 minute （1） ~uv （60） and the thoracic longitudinal velocity vv （1） ~vv （60） , analyzing the patient's respiratory movements.

4. The method for processing images in a ICU based on respiratory motion according to claim 2, wherein: The specific steps of step S22 are as follows: Step S221: Calculate r （1，1） ~r （Lb，Hb） The mean square error Sr;g （1，1） ~g （Lb，Hb） The mean square error Sg; b （1，1） ~b （Lb，Hb） The mean square error Sb is as follows: ； ； ； Step S222: Back up image A to obtain a duplicate image; extract d （1） ~d （N） The minimum value dl in Define calculation formula 2-2: ； Among them, Fr （k）（x，y） 、Fg （k）（x，y） 、Fb （k）（x，y） , respectively represent the color intensity of red, green and blue at (x, y) of the monitoring image after being processed by the k-th Gaussian filter; d （k） and σ （k） , respectively represent the scale and standard deviation of the kth Gaussian filter; use the 1st to Nth Gaussian filters to process the heavy image and obtain image A （1） ~Image A （N） ; Step S223: Calculate image A （1） The mean square deviation of the color intensities of red, green, and blue is obtained as sr （1） 、sg （1） and sb （1） ; Image A （1） The RGB values ​​of all pixels are recorded as (r （1）（1，1） , g （1）（1，1） , b （1）（1，1） )~(r （1）（Lb，Hb） , g （1）（Lb，Hb） , b （1）（Lb，Hb） ); Calculate r （1）（1，1） ~r （1）（Lb，Hb） The average value of （1） , g （1）（1，1） ~g （1）（Lb，Hb） The average value of ag （1） , b （1）（1，1） ~b （1）（Lb，Hb） The average value of ab （1） .

5. The method for processing ICU images based on respiratory motion according to claim 4, characterized in that: The subsequent steps of step S223 are as follows: Step S224: Calculate r （1）（1，1） ~r （1）（Lb，Hb） The mean square error sr （1） ;g （1）（1，1） ~g （1）（Lb，Hb） The mean square error sg （1） ; b （1）（1，1） ~b （1）（Lb，Hb） The mean square error sb （1） ; ； ； ； Step S225: Calculate image A （2） To Image A （N） The mean square deviation of the color intensities of red, green, and blue is obtained as sr （2） ~sr （N） , sg （2） ~sg （N） and sb （2） ~sb （N） ; Let image A （k） The mean square deviation of the color intensity of red, green and blue is sr （k） 、sg （k） and sb （k） ;The weight coefficient of the kth Gaussian filter is zq （k） ; Define calculation formula 2-3: ; Calculate the weight coefficients zq of the 1st to Nth Gaussian filters （1） ~zq （N） ; Step S226: Calculate zq （1） ~zq （N） The sum is recorded as aq; the weight coefficient of the k-th Gaussian filter is recorded as zq （k） , the weight is denoted as ω （k） , define calculation formula 2-4: ω （k） =(zq （k） / aq); Calculate the weights ω of the 1st to Nth Gaussian filters （1） ~ω （N） .

6. The method for processing ICU images based on respiratory motion according to claim 2, characterized in that: The specific steps of step S23 are as follows: Step S231: Define calculation formula 2-5: ； Among them, Rr （x，y） , Rg （x，y） and Rb （x，y） , respectively represent the color intensity of the pixel point (x, y) on the monitoring image after red, green and blue transformation; Fr （k）（x，y） 、Fg （k）（x，y） 、Fb （k）（x，y） , respectively represent the color intensity of red, green and blue at (x, y) of the monitoring image after being processed by the k-th Gaussian filter; Step S232: Perform a transformation on the monitoring image to obtain (Rr （1，1） , Rg （1，1） , Rb （1，1） )~(Rr （Lb，Hb） , Rg （Lb，Hb） , Rb （Lb，Hb） ), converted into grayscale value, and get gr （1，1） ~gr （Lb，Hb） .

7. The method for processing ICU images based on respiratory motion according to claim 3, characterized in that: The specific steps of step S26 are as follows: Step S261: Set the picture at the dtth second in the target video as picture Pr （dt） , the (dt+1) second picture is picture Pr （dt+1） ; Step S262: Count the pictures Pr （dt） The number of pixels of the patient's chest contour in st; Put the picture Pr （dt） The pixel points of the patient's chest contour are taken as punctuation points; the positions of the 1st to the stth punctuation points are recorded as (sx （1） ,sy （1） )~(sx （st） ,sy （st） ); In the picture Pr （dt） Extract the grayscale values ​​of the 1st to stth punctuation points and record them as Ii (sx （1） ,sy （1） )~Ii(sx （st） ,sy （st） ); Step S263: In the picture Pr （dt+1） Extract the positions corresponding to the 1st to stth punctuation marks (tx （1） ,ty （1） )~(tx （st） ,ty （st） ); extract(tx （1） ,ty （1） )~(tx （st） ,ty （st） ) is the gray value of the pixel Io (tx （1） ,ty （1） )~Io(tx （st） ,ty （st） ).

8. A respiratory motion-based ICU image processing system, applicable to the respiratory motion-based ICU image processing method according to any one of claims 1 to 7, characterized in that: The system comprises: Image acquisition module: used to obtain patient monitoring videos; Image analysis module: used to enhance monitoring videos and mark the patient's chest contour through background modeling technology; detect subtle movements of the patient's chest contour in the monitoring video to determine the patient's respiratory movement; perform frequency domain analysis on the patient's respiratory movement to obtain periodic information of the patient's breathing and determine whether the patient's respiratory rate is normal; if so, no action is taken; if not, an alarm is immediately issued, and medical records of all target hospitals are obtained to analyze the patient's pathological condition; Patient monitoring module: used to construct the lesion index change equation; predict the patient's condition change based on the lesion index change equation and obtain diagnostic information; Continuous monitoring module: used to continuously update and analyze monitoring videos based on the pathological condition and diagnostic information until the patient recovers.

Images