Method for storing state data of nephrotic personnel

Abstract

The invention relates to the technical field of image data processing, in particular to a state data storage method for nephropathy personnel, which comprises the following steps of: obtaining effective pixel points and invalid pixel points according to gray values of pixel points in a difference image of a CT (Computed Tomography) image, carrying out iterative division on the difference image according to the difference between the effective pixel points and the invalid pixel points, and obtaining the state data of the nephropathy personnel. And a tree structure is constructed according to a division result, and intelligent storage of image data of related states of the nephropathy personnel is realized. According to the method, the basic features of the CT image are utilized to carry out differential processing on the image, and then the pixel distribution rule of the CT difference image is combined to carry out difference image compression storage, so that the compression ratio is further improved, the storage space is greatly saved, and the utilization rate of storage space resources is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a method for storing status data of kidney disease patients. Background Art

[0002] During the computer tomography imaging process, several frames of images need to be scanned every second and displayed in real time on the monitor. The resolution of CT images is usually 512*512, and the value of each pixel is usually 0-4096. 12 bytes are required to store a pixel, so 3145728 bytes are required to store an image, which takes up a lot of storage space. Therefore, CT images need to be compressed and stored.

[0003] The existing lossless compression method based on Huffman coding performs global compression on CT images. Although compression processing is achieved to a certain extent, due to the particularity of CT images, the similarity of other areas in several CT images except for the lesion part is extremely high. The existing compression method does not take this factor into consideration, resulting in unsatisfactory compression effect.

[0004] The present invention proposes an adaptive CT image compression algorithm. Based on the shooting method and image features of renal CT images, the first CT image is used to obtain a difference image group between the remaining CT images and the first CT. Then, null value calculation is performed on the difference image to screen and obtain the effective area, and the tree structure is used to store it, thereby achieving effective compression of the difference image, further increasing the compression rate, and greatly saving storage space. Summary of the invention

[0005] The invention provides a method for storing status data of a person with kidney disease to solve the existing problems.

[0006] A method for storing status data of a person with kidney disease of the present invention adopts the following technical solution:

[0007] The present invention provides a method for storing status data of a person with kidney disease, the method comprising the following steps:

[0008] Obtaining a difference image;

[0009] The pixels whose grayscale values ​​in the difference image are not 0 are recorded as valid pixels; the pixels whose grayscale values ​​in the difference image are 0 are recorded as invalid pixels; a sliding window is constructed, and a clustering factor is obtained according to the Euclidean distance of the valid pixels in the sliding window; the effective point clustering rate of the difference image is obtained according to the fusion result of the number of valid pixels in the difference image and the sliding window and the clustering factor; the null value feature of the difference image is obtained according to the difference in the number of valid pixels and invalid pixels in the difference image;

[0010] According to the fusion results of effective point aggregation rate and null value features, the compressibility evaluation degree of the difference image is obtained;

[0011] The difference image is iteratively divided according to the size of the compressibility judgment degree to obtain a number of sub-images and the compressibility judgment degrees corresponding to the sub-images. According to the difference image and the compressibility judgment degrees corresponding to the several sub-images, a tree structure is constructed and recorded as a difference compression tree; based on the difference compression tree, intelligent storage of the status data of kidney disease patients is realized.

[0012] Furthermore, the difference image is obtained by the following method:

[0013] First, a CT image of a patient with kidney disease is obtained, which is recorded as the first image;

[0014] Then, all the first images are grayed out and recorded as second images;

[0015] Finally, the first second image in the acquisition process is recorded as a reference image, and the reference image is subtracted from the remaining second images to obtain difference images corresponding to the remaining second images.

[0016] Furthermore, the construction of the sliding window and obtaining the clustering factor according to the Euclidean distance of the valid pixel points in the sliding window include the following specific steps:

[0017] First, a sliding window with a size of 1 / 6 of the difference image and a sliding step of 1 is constructed, and the diagonal length of the sliding window is recorded as l; a sliding window analysis is performed on the difference image to obtain the number of valid pixels in the sliding window, recorded as Nh;

[0018] Then, the difference image is traversed using the sliding window, and the clustering factor of the effective pixel is obtained according to the Euclidean distance between any effective pixel in the sliding window and other effective pixel points. The specific method is as follows:

[0019]

[0020] Among them, D s represents the clustering factor of the sth valid pixel in the sliding window; Nh represents the number of valid pixels in the sliding window; Xe s Ye s Respectively represent the horizontal and vertical coordinates of the sth valid pixel in the sliding window; Xe si Ye si They respectively represent the horizontal and vertical coordinates of the i-th valid pixel point other than the s-th valid pixel point in the sliding window.

[0021] Furthermore, the effective point aggregation rate is obtained by the following method:

[0022] First, the clustering factors of all valid points in the sliding window during the traversal process are obtained, and the set formed by the clustering factors of all valid points is recorded as the clustering factor set {D};

[0023] Then, obtain the effective point aggregation rate of any difference image:

[0024]

[0025] Among them, J represents the effective point aggregation rate of the difference image; l represents the diagonal length of the sliding window; Nh represents the number of effective pixels contained in the sliding window; NT represents the number of effective pixels in the difference image; {D} represents the aggregation factor set; min() means obtaining the minimum value.

[0026] Furthermore, the method of obtaining the null value feature of the difference image according to the difference in the number of valid pixels and invalid pixels in the difference image includes the following specific steps:

[0027] First, the number of invalid pixels in the difference image is recorded as Nw, and the difference image is detected for connected domains to obtain the connected domain with the largest area among the connected domains with a gray value of 0, which is recorded as the largest blank area;

[0028] Then, the ratio of the number of invalid pixels in the difference image to the number of valid pixels in the difference image is recorded as the first ratio; the difference between the number of pixels in the maximum blank area and the number of valid pixels in the difference image is recorded as the first difference, and the product of the first ratio and the first difference is recorded as the control feature of the difference image.

[0029] Furthermore, the compressibility evaluation degree is obtained by the following method:

[0030] The compressibility evaluation degree is obtained based on the null value characteristics and effective point aggregation rate of the difference image:

[0031]

[0032] Among them, Zip represents the compressibility evaluation degree of the difference image, J represents the effective point aggregation rate of the difference image; K represents the null value feature of the difference image; arctan() represents the inverse tangent function; | | represents the absolute value symbol, and e represents a natural constant.

[0033] Furthermore, the difference image is iteratively divided according to the size of the compressibility judgment degree to obtain a plurality of sub-images and the compressibility judgment degrees corresponding to the sub-images, and a tree structure is constructed according to the difference image and the compressibility judgment degrees corresponding to the plurality of sub-images, which is recorded as a difference compression tree, and the specific steps included are as follows:

[0034] First, a compressibility judgment degree threshold is preset. After obtaining the compressibility judgment degree of the difference image, if the compressibility judgment degree of the difference image is less than the compressibility judgment degree threshold, it is directly stored without segmentation; if the compressibility judgment degree is greater than the compressibility judgment degree threshold, the difference image is equally divided into 4 square images of the same size, which are recorded as the first sub-image;

[0035] Then, the compressibility judgment degree of any first sub-image is obtained by using the method for obtaining the compressibility judgment degree, and according to the size of the compressibility judgment degree of the first sub-image, if the compressibility judgment degree of the first sub-image is less than the compressibility judgment degree threshold, it is considered that the first sub-image has reached the compression standard and is directly stored without segmentation; if the compressibility judgment degree of the first sub-image is greater than the compressibility judgment degree threshold, it means that there are a large number of invalid pixels in the first sub-image, and the first sub-image is equally divided into 4 square images of the same size, which are recorded as the second sub-image;

[0036] Finally, this process is repeated until the size of the divided image is no smaller than a square image of a preset size; a tree structure is constructed based on the difference image and all the divided square images, i.e., all sub-images; the node value of the node where the square image composed of pixels with all grayscale values ​​of 0 is located is set to null; the node value of the node corresponding to the difference image or square image with a compressibility greater than the compressibility threshold is set to 1, indicating that there is a leaf node; the node value of the node corresponding to the difference image or square image with a compressibility less than the compressibility threshold is set to 0; the tree structure formed by all nodes containing node values ​​is recorded as the difference compression tree corresponding to the difference image.

[0037] Furthermore, the intelligent storage of the status data of kidney disease patients is realized according to the difference compression tree, and the specific steps include the following:

[0038] First, the reference image is losslessly compressed using Huffman coding;

[0039] Then, the subgraphs with node values ​​of 0 in the difference compression tree are directly stored, and the subgraphs with node values ​​of null are compressed using Huffman coding;

[0040] Finally, the baseline image and the difference image are compressed to obtain compressed image data, which is then uploaded to the electronic file of the corresponding kidney patient and stored in the hospital's case database, thus realizing intelligent storage of kidney disease patient status data and facilitating subsequent review and doctor's diagnosis and other related operations.

[0041] The beneficial effect of the technical solution of the present invention is that when compressing a CT image, instead of performing a one-time compression process on the entire image, the basic characteristics of the CT image are used to perform differential processing on the image, and then the difference image is compressed and stored in combination with the pixel distribution law of the CT difference image, thereby further improving the compression rate, greatly saving storage space, and optimizing the utilization rate of storage space resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of the steps of a method for storing status data of a person with kidney disease according to the present invention;

[0044] Figure 2 This is a schematic diagram of the difference compression tree;

[0045] Figure 3 Schematic diagram of difference image partitioning. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method for storing data of a person with kidney disease status according to the present invention, its specific implementation, structure, features and effects thereof, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The following describes in detail a specific solution of a method for storing status data of kidney disease patients provided by the present invention in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows a flowchart of a method for storing status data of a kidney disease patient provided by an embodiment of the present invention, the method comprising the following steps:

[0050] Step S001, obtaining relevant image data of a kidney disease patient.

[0051] A CT image of a kidney disease patient that needs to be compressed and stored is obtained and recorded as a first image.

[0052] Step S002, obtaining a difference image according to the difference between the first images, and obtaining an effective point aggregation rate and a null value according to relevant features of the difference image.

[0053] When a kidney patient undergoes a CT scan, several first images of different frames are generated. The lesion parts in the first images are different due to physiological activities, and the remaining parts are almost identical. Therefore, the differences between the first images can be used to perform difference processing on the remaining images. The obtained difference images carry less information and occupy less storage. In addition, the first image can be restored using the difference images.

[0054] This embodiment selects the first image of the first frame, performs a difference operation with the remaining images, and obtains a difference image group. Then, for the difference image group, combined with its pixel distribution law and matrix characteristics, the difference image is divided into regions and the compression effectiveness is determined. The effective region is retained and stored in a tree structure, thereby achieving effective compression of the CT image group.

[0055] The specific process is:

[0056] Step (1), grayscale all first images and record them as second images; record the first second image in the acquisition process as a reference image, and subtract the reference image from the remaining second images to obtain difference images corresponding to the remaining second images;

[0057] It should be noted that, during the inspection process, the image information between multiple first images of the same group is close, and there are some differences caused by organ movement and human body position movement, so the obtained difference image is similar to a sparse matrix, and most of the areas in the difference image are areas without valid information composed of pixels with a gray value of 0, so it has extremely high compressibility, and compressing the area without valid information will not lose any image details, so lossless compression of the difference image can be achieved, greatly saving storage space;

[0058] Step (2), record the pixels whose grayscale value in the difference image is not 0 as valid pixels, and record the pixels whose grayscale value in the difference image is 0 as invalid pixels; obtain the effective point aggregation rate:

[0059] First, a sliding window with a size of 1 / 6 of the difference image and a sliding step of 1 is constructed, and the diagonal length of the sliding window is recorded as l; a sliding window analysis is performed on the difference image to obtain the number of valid pixels in the sliding window, recorded as Nh;

[0060] Then, the difference image is traversed using the sliding window, and the clustering factor of the effective pixel is obtained according to the Euclidean distance between any effective pixel in the sliding window and other effective pixel points. The specific method is as follows:

[0061]

[0062] Among them, D s represents the clustering factor of the sth valid pixel in the sliding window; Nh represents the number of valid pixels in the sliding window; Xe s Ye s Respectively represent the horizontal and vertical coordinates of the sth valid pixel in the sliding window; Xe si Ye si Respectively represent the horizontal and vertical coordinates of the i-th valid pixel point other than the s-th valid pixel point in the sliding window;

[0063] Finally, the clustering factors of all valid pixels in the difference image during the traversal of the sliding window are obtained, and the set formed by the clustering factors of all valid pixels is recorded as the clustering factor set {D}. The effective point clustering rate of any difference image is obtained according to:

[0064]

[0065] Where J represents the effective point aggregation rate of the difference image, Nh represents the number of effective pixels contained in the sliding window; NT represents the number of effective pixels in the difference image; D s represents the clustering factor of the sth valid pixel in the sliding window; {D} represents the clustering factor set {D}; Xe si Ye si Respectively represent the horizontal and vertical coordinates of the ith valid pixel point other than the sth valid pixel point in the sliding window; Xe s Ye s They represent the horizontal and vertical coordinates of the sth valid pixel in the sliding window respectively; l represents the diagonal length of the sliding window, and min() represents obtaining the minimum value.

[0066] It should be noted that the size and step length of the sliding window are manually preset parameters and can be adjusted according to the application situation.

[0067] In this embodiment, since it is necessary to perform multiple iterations of compression on the difference image to achieve the best compression effect, it is necessary to consider the degree of aggregation of effective pixels. When quantifying the degree of aggregation of effective pixels, it is necessary to consider the proportion of the number of effective pixels in the region. The greater the proportion of the number of effective pixels, the higher the degree of aggregation of effective pixels in the corresponding region. It also indicates that the more invalid pixels in other regions, the higher the compressibility of the difference image.

[0068] For the pixels in the sliding window, it is necessary to obtain the degree of aggregation of effective pixels in the sliding window area, that is, the effective point aggregation rate. The effective point aggregation rate is obtained according to the ratio of the Euclidean distance between effective pixels and the diagonal angle of the sliding window. The larger the value of the effective point aggregation rate, the lower the degree of aggregation of effective pixels, and the lower the compressibility of the remaining area.

[0069] It should be noted that the compressibility described in this embodiment means that when the difference image is compressed using a tree structure, it can be compressed at a higher compression rate, and the compressibility corresponds to the compressibility evaluation degree in the subsequent steps.

[0070] Step (3), record the number of invalid pixels in the difference image as Nw, perform connected domain detection on the difference image, and obtain the connected domain with the largest area among the connected domains with a gray value of 0, which is recorded as the largest blank area;

[0071] According to the number of invalid pixels in the difference image and the number of pixels in the maximum blank area, the null value feature of the difference image is obtained:

[0072]

[0073] Among them, K represents the null value feature of the difference image, Nw represents the number of invalid pixels in the difference image; NT represents the number of valid pixels in the difference image; Nb represents the number of pixels in the maximum blank area.

[0074] It should be noted that since the difference image corresponding to the grayscale pathology image contains a large number of areas composed of pixels with grayscale values ​​of 0, when the difference image is subsequently compressed using the tree structure, the area composed of pixels with grayscale values ​​of 0 is mainly compressed. Therefore, the number of invalid pixels can be used to obtain the compressibility of the difference image. In addition, These are preset parameters and can be adjusted according to actual applications.

[0075] Step S003, obtaining the compressibility evaluation degree according to the effective point aggregation rate and the null value feature.

[0076] The compressibility evaluation degree is obtained based on the null value characteristics and effective point aggregation rate of the difference image:

[0077]

[0078] Among them, Zip represents the compressibility evaluation degree of the difference image, J represents the effective point aggregation rate of the difference image; K represents the null value feature of the difference image; arctan() represents the inverse tangent function; | | represents the absolute value symbol, and e represents a natural constant.

[0079] It should be noted that since the value of the null feature K can be positive or negative, when the K value is non-zero, the impact on the compression evaluation degree is lower than the effective point aggregation rate, while the negative value has a higher impact on the compression evaluation degree than the effective point aggregation rate. Therefore, when K ≥ 0, an exponential function with a natural constant as the base is used to reduce its impact, and the compressibility evaluation degree is obtained after further normalization.

[0080] When K<0, the larger its absolute value is, the smaller the area formed by the invalid pixels in the difference image is, and the smaller the compression rate when compressing the difference image is, that is, the lower the compressibility is. Therefore, the power function is used to amplify the influence of the null value feature K, and then the inverse trigonometric function is used to normalize it to obtain the compressibility judgment degree.

[0081] Step S004: compress and store the image data according to the compressibility evaluation degree.

[0082] Step (1) processes the difference image according to the compressibility evaluation degree to obtain the corresponding tree structure. The specific process is as follows:

[0083] First, the compressibility threshold is preset to 35%. After obtaining the compressibility of the difference image, if the compressibility of the difference image is less than 35%, it is considered that the difference image has reached the compression standard and is directly stored without segmentation. If the compressibility is greater than 35%, it means that there are a large number of invalid pixels in the difference image, and the difference image is equally divided into 4 square images of the same size, which are recorded as the first sub-image.

[0084] Then, the compressibility evaluation degree of any first sub-image is obtained by using the method in step S002 and step S003, and according to the size of the compressibility evaluation degree of the first sub-image, if the compressibility evaluation degree of the first sub-image is less than 35%, it is considered that the first sub-image has reached the compression standard and is directly stored without segmentation; if the compressibility evaluation degree of the first sub-image is greater than 35%, it means that there are a large number of invalid pixels in the first sub-image, and the first sub-image is equally divided into 4 square images of the same size, which are recorded as second sub-images;

[0085] Finally, this process is repeated until the size of the divided image is no less than a square image of size 8*8. A tree structure is constructed based on the difference image and all the divided square images, that is, all the sub-images.

[0086] The node value of the node where the square image composed of pixels with all gray values ​​of 0 is located is set to null; the node value of the node corresponding to the difference image or square image with a compressibility evaluation degree greater than 35% is set to 1, indicating that it has a leaf node; the node value of the node corresponding to the difference image or square image with a compressibility evaluation degree less than 35% is set to 0; the tree structure formed by all nodes containing node values ​​is recorded as the difference compression tree corresponding to the difference image, such as Figure 2shown.

[0087] During the division process, the schematic diagram of dividing the difference image is as follows Figure 3 As shown, null represents a sub-image composed of pixels with grayscale values ​​all equal to 0; 0 represents a sub-image with a compressibility rating less than 35%.

[0088] It should be noted that the square images obtained by dividing the difference image, the first sub-image, the second sub-image and the Nth sub-image can be called sub-images, and the square image is an image with two sides of equal length.

[0089] Step (2), compressing the reference image using Huffman coding, and compressing all difference images according to the tree structure, the specific method is as follows:

[0090] First, the reference image is losslessly compressed using Huffman coding;

[0091] Then, the subgraphs with node values ​​of 0 in the difference compression tree are directly stored, and the subgraphs with node values ​​of null are compressed using Huffman coding;

[0092] Finally, the baseline image and the difference image are compressed to obtain compressed image data, which is then uploaded and stored in the electronic file of the corresponding kidney disease patient and stored in the hospital's case database to facilitate subsequent review and doctor's diagnosis and other related operations.

[0093] It should be noted that the exp(--x) model used in this embodiment is only used to indicate that the negative correlation and the result of the constraint model output are in the interval [0, 1). In specific implementation, it can be replaced with other models with the same purpose. This embodiment only uses the exp(-x) model as an example for description without making specific limitations on it, where x refers to the input of the model.

[0094] 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for storing status data of a person with kidney disease, characterized in that: The method comprises the following steps: Obtaining a difference image; The pixels whose grayscale values ​​in the difference image are not 0 are recorded as valid pixels; the pixels whose grayscale values ​​in the difference image are 0 are recorded as invalid pixels; a sliding window is constructed, and a clustering factor is obtained according to the Euclidean distance of the valid pixels in the sliding window; the effective point clustering rate of the difference image is obtained according to the fusion result of the number of valid pixels in the difference image and the sliding window and the clustering factor; the null value feature of the difference image is obtained according to the difference in the number of valid pixels and invalid pixels in the difference image; According to the fusion results of effective point aggregation rate and null value features, the compressibility evaluation degree of the difference image is obtained; The difference image is iteratively divided according to the size of the compressibility judgment degree to obtain a number of sub-images and the compressibility judgment degrees corresponding to the sub-images. According to the difference image and the compressibility judgment degrees corresponding to the several sub-images, a tree structure is constructed and recorded as a difference compression tree; based on the difference compression tree, intelligent storage of the status data of kidney disease patients is realized.

2. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The difference image is obtained by the following method: First, a CT image of a patient with kidney disease is obtained, which is recorded as the first image; Then, all the first images are grayed out and recorded as second images; Finally, the first second image in the acquisition process is recorded as a reference image, and the reference image is subtracted from the remaining second images to obtain difference images corresponding to the remaining second images.

3. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The construction of the sliding window and obtaining the clustering factor according to the Euclidean distance of the valid pixel points in the sliding window include the following specific steps: First, a sliding window with a size of 1 / 6 of the difference image and a sliding step of 1 is constructed, and the diagonal length of the sliding window is recorded as l; a sliding window analysis is performed on the difference image to obtain the number of valid pixels in the sliding window, recorded as Nh; Then, the difference image is traversed using the sliding window, and the clustering factor of the effective pixel is obtained according to the Euclidean distance between any effective pixel in the sliding window and other effective pixel points. The specific method is as follows: Among them, D s represents the clustering factor of the sth valid pixel in the sliding window; Nh represents the number of valid pixels in the sliding window; Xe s Ye s Respectively represent the horizontal and vertical coordinates of the sth valid pixel in the sliding window; Xe si Ye si They respectively represent the horizontal and vertical coordinates of the i-th valid pixel point other than the s-th valid pixel point in the sliding window.

4. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The effective point aggregation rate is obtained as follows: First, the clustering factors of all valid points in the sliding window during the traversal process are obtained, and the set formed by the clustering factors of all valid points is recorded as the clustering factor set {D}; Then, obtain the effective point aggregation rate of any difference image: Wherein, J represents the effective point aggregation rate of the difference image; l represents the diagonal length of the sliding window; Nh represents the number of effective pixels contained in the sliding window; NT represents the number of effective pixels in the difference image; {D} represents a set of aggregation factors; min() represents obtaining the minimum value.

5. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The method of obtaining the null value feature of the difference image according to the difference in the number of valid pixels and invalid pixels in the difference image includes the following specific steps: First, the number of invalid pixels in the difference image is recorded as Nw, and the difference image is detected for connected domains to obtain the connected domain with the largest area among the connected domains with a gray value of 0, which is recorded as the largest blank area; Then, the ratio between the number of invalid pixels in the difference image and the number of valid pixels in the difference image is recorded as a first ratio; The difference between the number of pixels in the maximum blank area and the number of valid pixels in the difference image is recorded as a first difference, and the product of the first ratio and the first difference is recorded as a control feature of the difference image.

6. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The compressibility evaluation degree is obtained as follows: The compressibility evaluation degree is obtained based on the null value characteristics and effective point aggregation rate of the difference image: Among them, Zip represents the compressibility evaluation degree of the difference image, J represents the effective point aggregation rate of the difference image; K represents the null value feature of the difference image; arctan() represents the inverse tangent function; | | represents the absolute value symbol, and e represents a natural constant.

7. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The method of iteratively dividing the difference image according to the size of the compressibility evaluation degree to obtain a plurality of sub-images and the compressibility evaluation degrees corresponding to the sub-images, and constructing a tree structure recorded as a difference compression tree according to the compressibility evaluation degrees corresponding to the difference image and the plurality of sub-images, includes the following specific steps: First, a compressibility judgment degree threshold is preset. After obtaining the compressibility judgment degree of the difference image, if the compressibility judgment degree of the difference image is less than the compressibility judgment degree threshold, it is directly stored without segmentation; if the compressibility judgment degree is greater than the compressibility judgment degree threshold, the difference image is equally divided into 4 square images of the same size, which are recorded as the first sub-image; Then, the compressibility judgment degree of any first sub-image is obtained by using the method for obtaining the compressibility judgment degree, and according to the size of the compressibility judgment degree of the first sub-image, if the compressibility judgment degree of the first sub-image is less than the compressibility judgment degree threshold, it is considered that the first sub-image has reached the compression standard and is directly stored without segmentation; if the compressibility judgment degree of the first sub-image is greater than the compressibility judgment degree threshold, it means that there are a large number of invalid pixels in the first sub-image, and the first sub-image is equally divided into 4 square images of the same size, which are recorded as the second sub-image; Finally, this process is repeated until the size of the divided image is no smaller than a square image of a preset size; a tree structure is constructed based on the difference image and all the divided square images, i.e., all sub-images; the node value of the node where the square image composed of pixels with all grayscale values ​​of 0 is located is set to null; the node value of the node corresponding to the difference image or square image with a compressibility greater than the compressibility threshold is set to 1, indicating that there is a leaf node; the node value of the node corresponding to the difference image or square image with a compressibility less than the compressibility threshold is set to 0; the tree structure formed by all nodes containing node values ​​is recorded as the difference compression tree corresponding to the difference image.

8. A method for storing status data of a person with kidney disease according to claim 1, characterized in that: The intelligent storage of the status data of kidney disease patients according to the difference compression tree includes the following specific steps: First, the reference image is losslessly compressed using Huffman coding; Then, the subgraphs with node values ​​of 0 in the difference compression tree are directly stored, and the subgraphs with node values ​​of null are compressed using Huffman coding; Finally, the baseline image and the difference image are compressed to obtain compressed image data, which is then uploaded to the electronic file of the corresponding kidney patient and stored in the hospital's case database, thus realizing intelligent storage of kidney disease patient status data and facilitating subsequent review and doctor's diagnosis and other related operations.

Images