A method for storing status data of nephropathy patients

Abstract

The present invention relates to the technical field of image data processing, and specifically relates to a method for storing state data of nephropathy patients, including: obtaining valid pixel points and invalid pixel points according to the gray values of pixel points in the difference image of the acquired CT image, iteratively dividing the difference image according to the differences between the valid pixel points and the invalid pixel points, constructing a tree structure according to the division results, and realizing intelligent storage of image data related to the state of nephropathy patients. The present invention utilizes the basic features of CT images to perform differential processing on the images, and then combines the pixel distribution law of the CT difference map to perform compressed storage of the difference map, thereby further improving the compression ratio, greatly saving the storage space, and optimizing the utilization rate of storage space resources.

Description

Technical Field

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

[0002] During the computer tomography imaging process, several frames of images need to be scanned per second and displayed in real time on a monitor. The resolution of CT images is usually 512*512, and the value of each pixel is usually 0-4096. It takes 12 bytes to store one pixel. Therefore, 3145728 bytes are required to store one image, thus occupying a large amount of storage space. Therefore, it is necessary to compress and store CT images.

[0003] The existing lossless compression method based on Huffman coding globally compresses CT images. Although compression processing is achieved to a certain extent, due to the particularity of CT images, except for the lesion parts in several CT images, the similarity of other regions is extremely high. The existing compression method does not consider this factor, resulting in an unsatisfactory compression effect.

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

[0005] The present invention provides a method for storing status data of nephropathy patients to solve the existing problems.

[0006] The method for storing status data of nephropathy patients according to the present invention adopts the following technical solutions:

[0007] The present invention provides a method for storing status data of nephropathy patients, and the method includes the following steps:

[0008] Obtain difference images;

[0009] Mark the pixel points with non-zero gray values in the difference images as effective pixel points; mark the pixel points with gray value of 0 in the difference images as invalid pixel points; construct a sliding window, and obtain an aggregation factor according to the Euclidean distance of the effective pixel points in the sliding window; obtain the effective point aggregation rate of the difference images according to the fusion result of the difference images, the number of effective pixel points in the sliding window, and the aggregation factor; obtain the null value feature of the difference images according to the quantity difference between the effective pixel points and the invalid pixel points in the difference images.

[0010] According to the fusion result of the effective point aggregation rate and the null value feature, obtain the compressibility evaluation degree of the difference image;

[0011] Iteratively divide the difference image according to the size of the compressibility evaluation degree, obtain several sub-images and the corresponding compressibility evaluation degrees of the sub-images, and construct a tree structure denoted as the difference compression tree according to the difference image and the corresponding compressibility evaluation degrees of several sub-images; According to the difference compression tree, realize the intelligent storage of the status data of nephropathy patients.

[0012] Further, the method for obtaining the difference image is as follows:

[0013] First, obtain the CT image of the nephropathy patient, denoted as the first image;

[0014] Then, perform grayscale processing on all the first images, denoted as the second image;

[0015] Finally, record the first second image during the acquisition process as the reference image, subtract the reference image from the remaining second images, and obtain the difference images corresponding to the remaining second images.

[0016] Further, the steps for constructing the sliding window and obtaining the aggregation factor according to the Euclidean distance of the effective pixel points in the sliding window are as follows:

[0017] First, construct a sliding window with a size of 1 / 6 of the difference image and a sliding step of 1, and denote the diagonal length of the sliding window as l; perform sliding window analysis on the difference image to obtain the number of effective pixel points in the sliding window, denoted as Nh;

[0018] Then, use the sliding window to traverse the difference image, and obtain the aggregation factor of the effective pixel points according to the Euclidean distance between any effective pixel point and other effective pixel points in the sliding window. The specific method is as follows:

[0019]

[0020] Among them, D s represents the aggregation factor of the s-th effective pixel point in the sliding window; Nh represents the number of effective pixel points contained in the sliding window; Xe s , Ye s respectively represent the horizontal and vertical coordinates of the s-th effective pixel point in the sliding window; Xe si , Ye si respectively represent the horizontal and vertical coordinates of the i-th effective pixel point other than the s-th effective pixel point in the sliding window.

[0021] Further, the method for obtaining the effective point aggregation rate is as follows:

[0022] First, obtain the aggregation factor of all valid points during the traversal of the sliding window, and denote the set formed by the aggregation factors of all valid points as the aggregation factor set {D};

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

[0024]

[0025] where J represents the valid point aggregation rate of the difference image; l represents the diagonal length of the sliding window; Nh represents the number of valid pixel points contained in the sliding window; NT represents the number of valid pixel points in the difference image; {D} represents the aggregation factor set; min() represents obtaining the minimum value.

[0026] Furthermore, obtaining the null value feature of the difference image according to the quantity difference between the valid pixel points and the invalid pixel points in the difference image includes the following specific steps:

[0027] First, denote the number of invalid pixel points in the difference image as Nw, perform connected component detection on the difference image, and obtain the connected component with the largest area in the connected component with a gray value of 0, denoted as the largest blank area;

[0028] Then, denote the ratio between the number of invalid pixel points in the difference image and the number of valid pixel points in the difference image as the first ratio; denote the difference between the number of pixel points in the largest blank area and the number of valid pixel points in the difference image as the first difference, and denote the product result of the first ratio and the first difference as the control feature of the difference image.

[0029] Furthermore, the method for obtaining the compressibility evaluation degree is as follows:

[0030] Obtain the compressibility evaluation degree according to the null value feature and the valid point aggregation rate of the difference image:

[0031]

[0032] where Zip represents the compressibility evaluation degree of the difference image, J represents the valid point aggregation rate of the difference image; K represents the null value feature of the difference image; arctan() represents the arctangent function; | | represents the absolute value symbol, and e represents the natural constant.

[0033] Furthermore, iteratively divide the difference image according to the size of the compressibility evaluation degree to obtain several subgraphs and the corresponding compressibility evaluation degrees of the subgraphs. According to the difference image and the corresponding compressibility evaluation degrees of the several subgraphs, construct a tree structure denoted as the difference compression tree, including the following specific steps:

[0034] First, preset the compressibility evaluation threshold. After obtaining the compressibility evaluation of the difference image, if the compressibility evaluation of the difference image is less than the compressibility evaluation threshold, it is directly stored without segmentation; if the compressibility evaluation is greater than the compressibility evaluation threshold, the difference image is evenly divided into 4 square images of the same size, denoted as the first sub-images;

[0035] Then, use the method for obtaining the compressibility evaluation to obtain the compressibility evaluation of any first sub-image. According to the size of the compressibility evaluation of the first sub-image, if the compressibility evaluation of the first sub-image is less than the compressibility evaluation threshold, it is considered that the first sub-image has reached the compression standard and is directly stored without segmentation; if the compressibility evaluation of the first sub-image is greater than the compressibility evaluation threshold, it indicates that there are a large number of invalid pixel points in the first sub-image, and the first sub-image is evenly divided into 4 square images of the same size, denoted as the second sub-images;

[0036] Finally, by analogy, until the size of the divided image is not less than a square image of the preset size; according to the difference image and all the divided square images, that is, all sub-images, a tree structure is constructed; the node value of the node where the square image composed of pixel points 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 greater than the compressibility evaluation threshold is set to 1, indicating that it has leaf nodes; the node value of the node corresponding to the difference image or square image with a compressibility evaluation less than the compressibility evaluation threshold is set to 0; the tree structure formed by all the nodes with node values is denoted as the difference compression tree corresponding to the difference image.

[0037] Further, the intelligent storage of the status data of nephropathy patients is realized according to the difference compression tree, and the specific steps are as follows:

[0038] First, use Huffman coding to perform lossless compression on the reference image;

[0039] Then, directly store the sub-images with node values of 0 in the difference compression tree, and compress the sub-images with node values of null using Huffman coding;

[0040] Finally, by separately compressing the reference image and the difference image to obtain the compressed image data, uploading the compressed image data to the electronic file of the corresponding nephropathy patient and storing it in the hospital's case database, the intelligent storage of the status data of nephropathy patients is realized, and it is convenient for subsequent reexamination and doctor diagnosis and other related operations.

[0041] The beneficial effects of the technical solution of the present invention are as follows: When compressing CT images, instead of performing a one-time compression process on the entire image, the basic features of the CT images are utilized to perform differential processing on the images. Subsequently, in combination with the pixel distribution law of the CT difference map, the difference map is compressed and stored, thereby further improving the compression ratio, greatly saving the storage space, and optimizing the utilization rate of the storage space resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a flowchart of the steps of a method for storing the status data of nephropathy patients according to the present invention;

[0044] Figure 2 It is a schematic diagram of a differential compression tree;

[0045] Figure 3 It is a schematic diagram of the division of the difference image. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method for storing the status data of nephropathy patients proposed according to the present invention. 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 can be combined in any suitable form.

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

[0048] The following specifically describes the specific solution of a method for storing the status data of nephropathy patients provided by the present invention with reference to the drawings.

[0049] Please refer to Figure 1 , which shows a flowchart of the steps of a method for storing the status data of nephropathy patients provided by an embodiment of the present invention. The method includes the following steps:

[0050] Step S001, obtaining relevant image data of nephropathy patients.

[0051] Obtain the CT images of nephropathy patients that need to be compressed and stored, denoted as the first images.

[0052] Step S002: Obtain the difference images based on the differences between the first images, and obtain the effective point aggregation rate and null values based on the relevant features of the difference images.

[0053] Since when nephropathy patients undergo CT examinations, several first images of different frames will be generated. The lesion parts in the first images are different due to physiological activities, and the remaining parts are almost exactly the same. Therefore, the differences between the first images can be utilized to perform difference processing on the remaining pictures. The information carried by the obtained difference pictures is less, and the storage occupied is also less. Moreover, the first images can be restored using the difference pictures.

[0054] In this embodiment, the first image of the first frame is selected to perform difference operations with the remaining images to obtain a group of difference images. Subsequently, for the group of difference images, combined with its pixel distribution law and matrix characteristics, the difference images are subjected to region division and determination of compression effectiveness, the effective regions are retained, and storage is performed in combination with a tree structure, thereby achieving effective compression of the CT image group.

[0055] The specific process is as follows:

[0056] Step (1): Perform grayscale processing on all the first images, denoted as the second images; denote the first second image in the acquisition process as the reference image, and subtract the reference image from the remaining second images to obtain the corresponding difference images of the remaining second images.

[0057] It should be noted that during the examination process, the image information between multiple first images in the same group is close, and there are some differences caused by organ movement and human body position movement. Therefore, the obtained difference images are approximately sparse matrices, and most regions in the difference images are regions without effective information composed of pixel points with a grayscale value of 0. Therefore, they have extremely high compressibility, and compressing the regions without effective information will not lose any detailed features of the images, and lossless compression of the difference images can be achieved, greatly saving storage space.

[0058] Step (2): Denote the pixel points with non-zero grayscale values in the difference images as effective pixel points, and denote the pixel points with a grayscale value of 0 in the difference images as invalid pixel points; obtain the effective point aggregation rate:

[0059] First, construct a sliding window with a size of 1 / 6 of the difference image and a sliding step of 1, and denote the diagonal length of the sliding window as l; perform sliding window analysis on the difference pictures to obtain the number of effective pixel points in the sliding window, denoted as Nh.

[0060] Then, a sliding window is used to traverse the difference image, and according to the Euclidean distance between any valid pixel point and other valid pixel points in the sliding window, the aggregation factor of the valid pixel points is obtained. The specific method is as follows:

[0061]

[0062] Among them, D s represents the aggregation factor of the s-th valid pixel point in the sliding window; Nh represents the number of valid pixel points contained in the sliding window; Xe s , Ye s respectively represent the horizontal and vertical coordinates of the s-th valid pixel point 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, obtain the aggregation factors of all valid pixel points in the difference image during the traversal of the sliding window. Denote the set formed by the aggregation factors of all valid pixel points as the aggregation factor set {D}. According to the obtained valid point aggregation rate of any difference image:

[0064]

[0065] Among them, J represents the valid point aggregation rate of the difference image; Nh represents the number of valid pixel points contained in the sliding window; NT represents the number of valid pixel points in the difference image; D s represents the aggregation factor of the s-th valid pixel point in the sliding window; {D} represents the aggregation factor set {D}; 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; Xe s , Ye s respectively represent the horizontal and vertical coordinates of the s-th valid pixel point in the sliding window; 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 artificially preset parameters and can be adjusted according to the application situation.

[0067] In this embodiment, since the difference image needs to be iteratively compressed multiple times to achieve the best compression effect, it is necessary to consider the aggregation degree of valid pixel points. When quantifying the aggregation degree of valid points, it is necessary to consider the proportion of the number of valid pixel points in the region. The larger the proportion of the number of valid pixel points, the higher the aggregation degree of valid pixel points in the corresponding region, and at the same time, it also indicates that there are more invalid pixel points in other regions, and the higher the compressibility of the difference image;

[0068] For the pixel points within the sliding window, it is necessary to obtain the aggregation degree of the valid pixel points within the sliding window area, that is, the valid point aggregation rate. The valid point aggregation rate is obtained according to the ratio of the Euclidean distance between the valid pixel points to the diagonal of the sliding window. The larger the value of the valid point aggregation rate, the lower the aggregation degree of the valid pixel points, and the lower the compressibility of the remaining area;

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

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

[0071] According to the number of invalid pixel points in the difference image and the number of pixel points in the largest blank area, obtain the null value feature of the difference image:

[0072]

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

[0074] It should be noted that since there are a large number of areas composed of pixel points with a gray value of 0 in the difference image corresponding to the gray-scale pathological image, when the difference image is compressed using a tree structure later, it is mainly the areas composed of pixel points with a gray value of 0 that are compressed. Therefore, the compressibility of the difference image can be obtained by using the number of invalid pixel points; in addition, is a preset parameter that can be adjusted according to actual applications.

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

[0076] Obtain the compressibility evaluation degree according to the null value feature and the valid point aggregation rate of the difference image:

[0077]

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

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

[0080] For K<0, the larger its absolute value indicates that the area formed by invalid pixel points in the difference image is smaller, and the compression rate during the compression of the difference image is smaller, that is, the compressibility is lower. Therefore, the power function is used to amplify the influence degree of the null feature K, and then the inverse trigonometric function is used for normalization to obtain the compressibility evaluation degree.

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

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

[0083] First, preset the compressibility evaluation degree threshold to 35%. After obtaining the compressibility evaluation degree of the difference image, if the compressibility evaluation degree 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 evaluation degree is greater than 35%, it means that there are a large number of invalid pixel points in the difference image, and the difference image is equally divided into 4 square images of the same size, denoted as the first sub-image;

[0084] Then, use the methods in step S002 and step S003 to obtain the compressibility evaluation degree of any first sub-image, 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 pixel points in the first sub-image, and the first sub-image is equally divided into 4 square images of the same size, denoted as the second sub-image;

[0085] Finally, and so on, until the size of the divided image is not less than a square image of 8*8; construct a tree structure according to the difference image and all the divided square images, that is, all sub-images;

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

[0087] During the partitioning process, the schematic diagram of partitioning the difference image is as Figure 3 shown, where null represents a sub-image composed of pixel points with all gray values being 0; 0 represents a sub-image with a compressibility evaluation degree less than 35%.

[0088] It should be noted that when partitioning the difference image, the first sub-image, the second sub-image, and the Nth sub-image, the obtained square images can all be called sub-images, and the square image is an image with the lengths of two sides being equal.

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

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

[0091] Then, directly store the sub-images with node values of 0 in the difference compression tree, and compress the sub-images with node values of null using Huffman coding;

[0092] Finally, obtain the compressed image data by compressing the reference image and the difference image respectively, and upload and store the compressed image data into the electronic file of the corresponding nephropathy patient and deposit it into the hospital's case database for subsequent review and doctor diagnosis and other related operations.

[0093] It should be noted that the exp(--x) model used in this embodiment is only used to represent the negative correlation relationship and constrain the result of the model output to be within the interval [0, 1). During specific implementation, it can be replaced with other models with the same purpose. This embodiment only takes the exp(-x) model as an example for description and does not make specific limitations on it, where x refers to the input of the model.

[0094] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall 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