An intelligent triage system for emergency departments based on machine learning

Abstract

The present invention relates to the field of image communication technology, and more specifically to an intelligent emergency department triage system based on machine learning, comprising: acquiring remote consultation images; obtaining the grayscale variance and grayscale variance continuity of each pixel point, thereby obtaining the grayscale value loss degree of each pixel point; obtaining the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point based on the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model; obtaining the grayscale value change threshold of each pixel point; obtaining the difference value of each pixel point; changing the grayscale value of each pixel point based on the difference value of each pixel point and the grayscale value change threshold value, thereby compressing and transmitting the remote consultation image. The present invention discards the grayscale value information of some pixels in the image and then uses the Huffman coding algorithm for compression, thereby ensuring the preservation of detailed information in the image and improving compression efficiency.

Description

Technical Field

[0001] The present invention relates to the field of image communication technology, and in particular to an intelligent triage system for emergency departments based on machine learning. Background Art

[0002] Telemedicine refers to the transmission of medical images from one location to another through remote technology. Doctors can conduct remote consultations by transmitting patients' medical images to remote experts to achieve intelligent triage in the emergency department. This method allows patients to receive diagnosis and treatment recommendations from experts around the world. When transmitting medical images, the images need to be compressed to reduce the size of the image files, thereby reducing the amount of image data that needs to be transmitted and increasing the transmission speed.

[0003] Huffman coding is a commonly used lossless compression method that operates on the grayscale value of each pixel in the remote consultation image. However, since the grayscale values of the pixels in the remote consultation image are relatively dispersed, the Huffman tree obtained according to the frequency of the pixel grayscale values may be deep, resulting in an excessively long code length when encoding the remote consultation image, low image compression efficiency, and reduced transmission speed during image transmission. Summary of the Invention

[0004] The present invention provides an intelligent triage system for emergency departments based on machine learning to solve existing problems.

[0005] The present invention provides an intelligent emergency department triage system based on machine learning, which adopts the following technical solutions:

[0006] One embodiment of the present invention provides an intelligent triage system for emergency departments based on machine learning, which implements the following steps:

[0007] Collect remote consultation images;

[0008] Obtain the grayscale variance of each pixel; obtain the grayscale variance continuity of each pixel based on the grayscale variance of each pixel; obtain the grayscale value loss degree of each pixel based on the grayscale variance and grayscale variance continuity of each pixel;

[0009] Obtaining a grayscale histogram of the remote consultation image; obtaining a mixed Gaussian model based on the grayscale histogram of the remote consultation image; obtaining the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point based on the mixed Gaussian model; obtaining a grayscale value change threshold for each pixel point based on the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model;

[0010] Obtain the difference value of each pixel; change the threshold value according to the difference value of each pixel and the grayscale value, and change the grayscale value of each pixel;

[0011] According to the changed grayscale value of each pixel, the remote consultation image is compressed and transmitted.

[0012] Preferably, the step of obtaining the grayscale variance of each pixel includes the following specific steps:

[0013] The default sliding window size is , traverse any pixel point in the remote consultation image, and the grayscale variance of the pixel point in the corresponding sliding window is used as the grayscale variance of the pixel point.

[0014] Preferably, the grayscale variance continuity of each pixel point is obtained according to the grayscale variance of each pixel point, and the specific steps include the following:

[0015] Traverse any pixel point in the remote consultation image, obtain the grayscale variance of other pixel points in the eight-neighborhood of the pixel point, and sort the grayscale variances in descending order to obtain a sequence, obtain the pixel points corresponding to the first two grayscale variances from the sequence for analysis, record them as the first pixel point and the second pixel point, establish a rectangular coordinate system with the pixel point as the center point, connect the first pixel point and the second pixel point with the origin of the rectangular coordinate system to form two straight lines, obtain the angle between each straight line and the vertical axis of the rectangular coordinate system, record them as the first angle and the second angle, obtain the difference between the first angle and the second angle, record them as the first difference, obtain the difference between the first difference and the vertical axis of the rectangular coordinate system, and obtain the difference between the first difference and the vertical axis of the rectangular coordinate system. The difference between them is taken as the grayscale variance continuity of the pixel.

[0016] Preferably, the grayscale value loss degree of each pixel point is obtained according to the grayscale variance and grayscale variance continuity of each pixel point, and the specific steps include the following:

[0017]

[0018] Where, For the Grayscale variance of pixels; For the The first angle of the pixel point; For the The second angle of the pixel point; Representative Continuity of grayscale variance of pixels; For the The degree of gray value loss of each pixel; Represents an exponential function with a natural constant as its base.

[0019] Preferably, the step of obtaining the grayscale histogram of the remote consultation image includes the following specific steps:

[0020] The grayscale value of the pixel in the remote consultation image is used as the horizontal coordinate, and the number of pixels corresponding to the grayscale value is used as the vertical coordinate to draw the grayscale histogram of the remote consultation image.

[0021] Preferably, the method of obtaining a mixed Gaussian model based on the grayscale histogram of the remote consultation image; and obtaining the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point based on the mixed Gaussian model includes the following specific steps:

[0022] Use the grayscale histogram of remote consultation images The algorithm fits the mixed Gaussian model, obtains the mixed Gaussian model, and obtains the standard deviation of each sub-Gaussian model. The standard deviation of the sub-Gaussian model to which each pixel belongs is the standard deviation of the sub-Gaussian distribution model corresponding to each pixel.

[0023] Preferably, the grayscale value change threshold of each pixel point is obtained according to the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model, and the specific steps include the following:

[0024]

[0025] Where, For the The degree of gray value loss of each pixel; For the The standard deviation of the sub-Gaussian distribution model corresponding to each pixel; For the The gray value of each pixel changes the threshold.

[0026] Preferably, the step of obtaining the difference value of each pixel includes the following specific steps:

[0027] Obtain the difference between the grayscale value of each pixel and the grayscale mean of other pixels in the eight neighborhoods, and record it as the difference value.

[0028] Preferably, the step of changing the threshold value according to the difference value and the gray value of each pixel to change the gray value of each pixel includes the following specific steps:

[0029] Traverse any pixel point in the remote consultation image. When the difference value of the pixel point is greater than the grayscale value change threshold, the grayscale value of the pixel point does not change. When the difference value of the pixel point is less than the grayscale value change threshold and the grayscale value of the pixel point is less than the grayscale mean of other pixels in the eight neighborhoods, the grayscale value of the pixel point is increased according to the difference value. When the difference value of the pixel point is greater than the grayscale value change threshold and the grayscale value of the pixel point is greater than the grayscale mean of other pixels in the eight neighborhoods, the grayscale value of the pixel point is reduced according to the difference value.

[0030] Preferably, the method of compressing and transmitting the remote consultation image according to the changed grayscale value of each pixel point includes the following specific steps:

[0031] The frequencies of the changed gray values are counted to construct a Huffman tree, the remote consultation image is compressed, and the compressed remote consultation image is remotely transmitted.

[0032] The technical solution of the present invention has the following beneficial effects: based on the characteristics of the remote consultation image, the present invention obtains the grayscale variance and grayscale variance continuity of each pixel point, and then obtains the grayscale value loss degree of each pixel point based on the grayscale variance and grayscale variance continuity of each pixel point; obtains a mixed Gaussian model based on the grayscale histogram of the remote consultation image, and then obtains the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point; obtains the grayscale value change threshold of each pixel point based on the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model, and obtains the difference value of each pixel point; changes the grayscale value of each pixel point based on the difference value of each pixel point and the grayscale value change threshold value, and compresses and transmits the remote consultation image based on the changed grayscale value of each pixel point. The present invention discards the grayscale value information of the tissue area in the image to make the grayscale value range more concentrated, and the Huffman tree obtained based on the changed grayscale value is shallower, while ensuring the preservation of important information in the remote consultation image and improving compression efficiency and transmission speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0034] Figure 1 This is a flowchart of the steps for implementing an intelligent triage system for emergency departments based on machine learning according to the present invention. DETAILED DESCRIPTION

[0035] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of an intelligent triage system for emergency departments based on machine learning proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0036] 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.

[0037] The following describes in detail a specific solution of an intelligent triage system for emergency departments based on machine learning provided by the present invention with reference to the accompanying drawings.

[0038] See also Figure 1 , which shows a flowchart of the steps of implementing an intelligent triage system for emergency departments based on machine learning provided by one embodiment of the present invention. The system implements the following steps:

[0039] S001. Collect images for remote consultation.

[0040] It should be noted that when an in-hospital patient requires a remote consultation, the hospital will collect various patient data, such as routine clinical examinations, X-rays, CT scans, and ultrasound scans. For patients requiring a remote consultation, the hospital collects the patient's medical imaging data and inputs it into a computer system. The medical images of the patient in the computer system are converted to grayscale, and the resulting images are recorded as remote consultation images.

[0041] S002. Obtain the grayscale value loss degree of each pixel in the remote consultation image.

[0042] It should be noted that Huffman coding is a lossless coding algorithm for data compression, which uses shorter codes for data with higher frequency and longer codes for data with lower frequency, so that the overall code length can be reduced, thereby achieving data compression. However, since the grayscale values of pixels in the acquired neurosurgery grayscale image are numerous and scattered, when Huffman coding is used, the depth of the constructed Huffman tree is deeper due to the presence of more grayscale values with lower frequency. Therefore, the code length obtained after encoding each pixel of the remote consultation image is longer, and the image compression effect is not good, resulting in a lower speed of the image transmission process. It is known that in remote consultation images, Different tissues or structures usually show different grayscale values. This feature enables doctors and radiologists to distinguish and identify different tissue types based on the grayscale levels of the image. Therefore, the grayscale value information of the edge points between tissues needs to be retained, and the grayscale values of the pixels within each tissue can be changed to make their grayscale values consistent, so as to concentrate the distribution range of the grayscale values in the image. Therefore, an embodiment of the present invention proposes a lossy coding algorithm, which concentrates the grayscale value range by discarding the grayscale value information of certain pixels in the remote consultation image, and then compresses the data in the remote consultation image through the Huffman coding algorithm to achieve a good compression effect.

[0043] It should be further explained that since the grayscale value information of the pixels inside each tissue area in the remote consultation image can be discarded, and the grayscale value information of the pixels at the edge between tissues is kept unchanged as much as possible, it is necessary to obtain the degree of grayscale value loss of each pixel in the remote consultation image. For the pixels inside the tissue area, the degree of grayscale value loss is large, and for the edge points between tissues, the degree of grayscale value loss is small. Because there are large grayscale value differences between each tissue area, the grayscale variance of the pixels at the edge between tissue areas is large in their local areas, and the continuity of the grayscale variance of the pixels at the edge is large. Therefore, according to the grayscale variance of the local area of each pixel and the continuity of the grayscale variance, the degree of grayscale value loss of each pixel in the remote consultation image is obtained.

[0044] In the embodiment of the present invention, setting The sliding window size of traverses all pixels in the remote consultation image and obtains the grayscale variance of each pixel in its sliding window. In this embodiment of the present invention, set In other embodiments, the implementer can set size.

[0045] It should be noted that for the pixel points at the edges between tissue regions, their grayscale variances are continuous, so it is necessary to obtain the continuity of the grayscale variance of each pixel point. Therefore, in an embodiment of the present invention, any pixel point in the remote consultation image is traversed and recorded as the current pixel point, and the grayscale variances of other pixel points in the eight neighborhoods of the current pixel point are obtained, and the grayscale variances are sorted in order from large to small to obtain a sequence. The pixel points corresponding to the first two grayscale variances in the sequence are obtained for analysis, and recorded as the first pixel point and the second pixel point. With the current pixel point as the center point, a rectangular coordinate system is established, and the first pixel point and the second pixel point are connected to the origin of the rectangular coordinate system to form two straight lines. The angle between each straight line and the vertical axis of the rectangular coordinate system is obtained, and recorded as the first angle and the second angle. It should be noted that when the difference between the first angle and the second angle is close to 180, it means that the grayscale variance continuity of the current pixel point is large.

[0046] Get the grayscale value loss degree of each pixel in the remote consultation image:

[0047]

[0048] Where, For the Grayscale variance of pixels; For the The first angle of the pixel point; For the The second angle of the pixel point; Representative The grayscale variance continuity of pixels is The closer the difference between the first angle and the second angle of a pixel is to 180, the The more pixels there are, the more likely they are edge points between tissue regions; For the The gray value loss degree of each pixel; The larger the grayscale variance of each pixel and the greater the continuity of the grayscale variance, the The more likely a pixel point is an edge point between tissues, the smaller the loss of its gray value. When the grayscale variance of each pixel is smaller and the continuity of grayscale variance is smaller, the The more likely a pixel is to be inside the tissue area, the greater the loss of its grayscale value.

[0049] At this point, the grayscale value loss degree of each pixel in the remote consultation image is obtained.

[0050] S003. Obtain a grayscale value change threshold for each pixel based on the grayscale value loss level of each pixel.

[0051] It should be noted that, since the grayscale values of various tissue regions vary greatly, the grayscale value change range of a pixel in each tissue region cannot exceed the grayscale value characteristics of the tissue region to which it belongs.

[0052] In the embodiment of the present invention, the grayscale histogram of the remote consultation image is obtained, and the grayscale histogram of the remote consultation image is used The algorithm fits a mixed Gaussian model and obtains multiple sub-Gaussian distribution models and the standard deviation of each sub-Gaussian model. It should be noted that each tissue area corresponds to a sub-Gaussian model. Therefore, for the pixel point of any tissue area, the change range of its grayscale value shall not exceed the standard deviation of the sub-Gaussian model corresponding to the tissue area.

[0053] It should be noted that for the pixels inside the tissue area, their grayscale value information can be lost, and the degree of loss of the pixel grayscale value is large. The grayscale value information of the edge points between tissue areas cannot be lost, and the degree of loss of the pixel grayscale value is small. Therefore, the grayscale value change threshold of each pixel is obtained by combining the degree of loss of the grayscale value of each pixel and the standard deviation of the sub-Gaussian distribution model corresponding to each pixel.

[0054] In an embodiment of the present invention, the grayscale value change threshold of each pixel is obtained:

[0055]

[0056] Where, For the The degree of gray value loss of each pixel; For the The standard deviation of the sub-Gaussian distribution model corresponding to each pixel; For the The threshold for changing the gray value of each pixel is larger for the pixels inside the tissue area, and smaller for the edge points between tissue areas.

[0057] At this point, the grayscale value change threshold of each pixel point is obtained according to the grayscale value loss degree of each pixel point.

[0058] S004. Changing the threshold value according to the grayscale value of each pixel to change the grayscale value of each pixel in the remote consultation image.

[0059] It should be noted that the grayscale value change threshold of each pixel is the maximum grayscale value that each pixel can increase or decrease. However, if the grayscale value change threshold is directly used to change the grayscale values of all pixels in the remote consultation image, it may cause the loss of detail information in the remote consultation image. Therefore, the grayscale value of each pixel is changed according to the grayscale value change threshold of each pixel and the difference between the grayscale value of each pixel and the grayscale value of the surrounding pixels. When the grayscale value difference between the pixel and the surrounding pixels is greater than the grayscale value change threshold, it is considered that the loss of the pixel grayscale value is too large and may destroy the original image features. Therefore, the grayscale value of the pixel is not changed. When the grayscale value difference between the pixel and the surrounding pixels is less than the grayscale value change threshold, it is considered that the loss of the pixel grayscale value is within an acceptable range and the grayscale value of the pixel is changed according to the grayscale value difference.

[0060] In an embodiment of the present invention, the difference between the grayscale value of each pixel point and the grayscale mean of the pixels in its eight neighborhoods is obtained, recorded as the difference value, and the difference value is compared with the grayscale value change threshold of the pixel point. When the difference value is greater than the grayscale value change threshold, the grayscale value of the pixel point does not change. When the difference value is less than the grayscale value change threshold and the grayscale value of the pixel point is less than the grayscale mean of the pixels in the eight neighborhoods, the grayscale value of the pixel point is increased according to the difference value. When the difference value is greater than the grayscale value change threshold and the grayscale value of the pixel point is greater than the grayscale mean of the pixels in the eight neighborhoods, the grayscale value of the pixel point is reduced according to the difference value.

[0061] So far, the grayscale value of each pixel in the remote consultation image has been changed.

[0062] S005. Compress and transmit remote consultation images.

[0063] The frequencies of the changed gray values are counted to construct a Huffman tree, and the remote consultation images are compressed. The compressed remote consultation images are then transmitted remotely to facilitate remote experts to diagnose the patient's condition.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent triage system for emergency departments based on machine learning, characterized in that: The system implements the following steps: Collect remote consultation images; Obtain the grayscale variance of each pixel; obtain the grayscale variance continuity of each pixel based on the grayscale variance of each pixel; obtain the grayscale value loss degree of each pixel based on the grayscale variance and grayscale variance continuity of each pixel; Obtaining a grayscale histogram of the remote consultation image; obtaining a mixed Gaussian model based on the grayscale histogram of the remote consultation image; obtaining the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point based on the mixed Gaussian model; obtaining a grayscale value change threshold for each pixel point based on the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model; Obtain the difference value of each pixel; change the threshold value according to the difference value of each pixel and the grayscale value, and change the grayscale value of each pixel; According to the changed grayscale value of each pixel, the remote consultation image is compressed and transmitted.

2. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The specific steps of obtaining the grayscale variance of each pixel are as follows: The default sliding window size is , traverse any pixel point in the remote consultation image, and the grayscale variance of the pixel point in the corresponding sliding window is used as the grayscale variance of the pixel point.

3. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The grayscale variance continuity of each pixel point is obtained according to the grayscale variance of each pixel point, and the specific steps include the following: Traverse any pixel point in the remote consultation image, obtain the grayscale variance of other pixel points in the eight-neighborhood of the pixel point, and sort the grayscale variances in descending order to obtain a sequence, obtain the pixel points corresponding to the first two grayscale variances from the sequence for analysis, record them as the first pixel point and the second pixel point, establish a rectangular coordinate system with the pixel point as the center point, connect the first pixel point and the second pixel point with the origin of the rectangular coordinate system to form two straight lines, obtain the angle between each straight line and the vertical axis of the rectangular coordinate system, record them as the first angle and the second angle, obtain the difference between the first angle and the second angle, record them as the first difference, obtain the difference between the first difference and the vertical axis of the rectangular coordinate system, and obtain the difference between the first difference and the vertical axis of the rectangular coordinate system. The difference between them is taken as the grayscale variance continuity of the pixel.

4. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The grayscale value loss degree of each pixel point is obtained according to the grayscale variance and grayscale variance continuity of each pixel point, and the specific steps include the following: Where, For the Grayscale variance of pixels; For the The first angle of the pixel point; For the The second angle of the pixel point; Representative Continuity of grayscale variance of pixels; For the The degree of gray value loss of each pixel; Represents an exponential function with a natural constant as its base.

5. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The specific steps of obtaining the grayscale histogram of the remote consultation image are as follows: The grayscale value of the pixel in the remote consultation image is used as the horizontal coordinate, and the number of pixels corresponding to the grayscale value is used as the vertical coordinate to draw the grayscale histogram of the remote consultation image.

6. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The method of obtaining a mixed Gaussian model based on the grayscale histogram of the remote consultation image and obtaining the standard deviation of the sub-Gaussian distribution model corresponding to each pixel point based on the mixed Gaussian model includes the following specific steps: Use the grayscale histogram of remote consultation images The algorithm fits the mixed Gaussian model, obtains the mixed Gaussian model, and obtains the standard deviation of each sub-Gaussian model. The standard deviation of the sub-Gaussian model to which each pixel belongs is the standard deviation of the sub-Gaussian distribution model corresponding to each pixel.

7. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The grayscale value change threshold of each pixel point is obtained according to the grayscale value loss degree of each pixel point and the standard deviation of the corresponding sub-Gaussian distribution model, and the specific steps include the following: Where, For the The degree of gray value loss of each pixel; For the The standard deviation of the sub-Gaussian distribution model corresponding to each pixel; For the The gray value of each pixel changes the threshold.

8. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The specific steps of obtaining the difference value of each pixel are as follows: Obtain the difference between the grayscale value of each pixel and the grayscale mean of other pixels in the eight neighborhoods, and record it as the difference value.

9. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The specific steps of changing the threshold value according to the difference value and the gray value of each pixel and changing the gray value of each pixel are as follows: Traverse any pixel point in the remote consultation image. When the difference value of the pixel point is greater than the grayscale value change threshold, the grayscale value of the pixel point does not change. When the difference value of the pixel point is less than the grayscale value change threshold and the grayscale value of the pixel point is less than the grayscale mean of other pixels in the eight neighborhoods, the grayscale value of the pixel point is increased according to the difference value. When the difference value of the pixel point is greater than the grayscale value change threshold and the grayscale value of the pixel point is greater than the grayscale mean of other pixels in the eight neighborhoods, the grayscale value of the pixel point is reduced according to the difference value.

10. The intelligent triage system for emergency department based on machine learning according to claim 1, characterized in that: The specific steps of compressing and transmitting the remote consultation image according to the changed grayscale value of each pixel are as follows: The frequencies of the changed gray values are counted to construct a Huffman tree, the remote consultation image is compressed, and the compressed remote consultation image is remotely transmitted.

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