Urinary tract infection identification method based on ultrasound imaging analysis

Abstract

This invention discloses a method for identifying urinary tract infections based on ultrasound imaging analysis, relating to the field of image analysis. The method includes: extracting the transparency of colony locations; identifying the transparency of impurities and air bubbles in non-urine areas; analyzing the transparency of colonies to determine transparency fluctuation errors; dividing the image to be tested into a non-detection area and a detection area; adjusting the detection area to obtain a corrected area; forming at least one colony composition scheme at each sampling point; and, based on a reference sampling point, obtaining a target colony composition scheme at non-reference sampling points, and analyzing at least one target symptom and its severity present in the image to be tested. By identifying the transparency of colonies, impurities, and air bubbles, obtaining the corrected area, forming the colony composition scheme at the sampling point, and obtaining the target colony composition scheme at non-reference sampling points, the type and scale of colonies can be determined more accurately.

Description

Technical Field

[0001] This invention relates to the field of image analysis, and more specifically to a method for identifying urinary tract infections based on ultrasound imaging analysis. Background Technology

[0002] Urinary tract infection (UTI), also known as UT1, is an infectious disease caused by pathogens such as bacteria, mycoplasma, chlamydia, and viruses growing and multiplying in the urinary tract. UTIs can be divided into upper and lower urinary tract infections. Upper urinary tract infections mainly involve pyelonephritis and ureteritis, while lower urinary tract infections mainly involve cystitis and urethritis. The symptoms of UTIs vary depending on the bacterial colonies present, and the effects of different colonies can be cumulative. Furthermore, the location of bacterial colonies is often difficult to pinpoint, making accurate identification of the bacterial composition in a UTI difficult to manage, thus hindering targeted treatment. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method for identifying urinary tract infections based on ultrasound imaging analysis. This technical solution resolves the issues raised in the background section.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Urinary tract infection identification methods based on ultrasound imaging analysis include: Based on big data, at least one bacterial colony causing urinary tract infection is obtained, and the symptoms of urinary tract infection caused by the bacterial colony are obtained. Ultrasound imaging is used to obtain sample images of the bacterial colony. Based on the sample images, the transparency of the location of the bacterial colony is extracted. The urine carrying the bacterial colony in the sample image is the sample urine. The sample urine was produced without urinary tract infection. The sample urine image was divided into urine and non-urine parts. The transparency of the urine part was obtained, and the transparency of impurities and air bubbles in the non-urine part was identified. Based on the transparency of the urine sample, the transparency of the bacterial colonies is analyzed, resulting in a fluctuation error in transparency. Ultrasound imaging is used to obtain images of the urinary tract to be tested. Based on the transparency of the urine portion, the images are divided into non-detection portions and detection portions. Based on the transparency of impurities and bubbles, the part to be detected is adjusted to obtain the corrected part; In the correction section, at least one sampling point is uniformly set, and based on the transparency of the sampling point, at least one colony composition scheme is formed at the sampling point; Sampling points with a colony composition scheme of 1 were used as reference sampling points, and sampling points with a colony composition scheme of more than 1 were used as non-reference sampling points. Based on the reference sampling points, the colony composition schemes of non-reference sampling points are screened to obtain the target colony composition schemes of non-reference sampling points. The analysis reveals at least one target symptom and its severity in the image to be detected.

[0005] Preferably, extracting the transparency of the colony location based on the sample image includes the following steps: In the sample image, the average pixel value of the pixels at the location of the colony is taken to obtain the transparency of the location of the colony.

[0006] Preferably, dividing the sample urine image into a urine portion and a non-urine portion includes the following steps: At least one sample point is uniformly selected from the sample urine image, and the pixel value of the pixel at the sample point is obtained as the feature value of the sample point. Classify the sample points to obtain at least one set of sample points, such that the sample points in the set have the same feature values; The set of sample points with the largest number of elements is taken as the target sample point set. The area covered by the sample points in the target sample point set is taken as the urine part. The part of the sample urine other than the urine part is taken as the non-urine part.

[0007] Preferably, the step of obtaining the transparency of the urine portion and identifying the transparency of impurities and bubbles in the non-urine portion includes the following steps: The transparency of the urine portion is obtained by averaging the pixel values ​​of the pixels in the urine portion. At least one contour line is identified in the non-urine portion. The average pixel value of the pixels in the region within the contour line is taken to obtain a first value. The average pixel value of the pixels on the contour line is taken to obtain a second value. If the first value and the second value are not equal, the outline is taken as the target outline, the area enclosed by the target outline is taken as the bubble part, and the area in the non-urine part other than the bubble part is taken as the impurity part. The transparency of the bubble is obtained by averaging the pixel values ​​of the pixels within the bubble portion, and the transparency of the impurity is obtained by averaging the pixel values ​​of the pixels within the impurity portion.

[0008] Preferably, the analysis yields the colony transparency, and the resulting fluctuation error in transparency includes the following steps: The transparency of the colony is obtained by subtracting the transparency of the urine portion from the transparency of the location of the colony. Using ultrasound imaging, at least one image of the urine sample is acquired at different times to obtain at least one sampled image. The average pixel value of each pixel in the sampled image is taken to obtain the recognition value of the sampled image. The difference between the maximum and minimum recognition values ​​of the sampled image is used to obtain the floating error of transparency.

[0009] Preferably, dividing the image to be detected into a non-detection portion and a detection portion includes the following steps: At least one detection point is uniformly selected in the image to be detected, and the detection points are classified to obtain at least one set of detection points, satisfying that the difference in pixel values ​​of the detection points in the set of detection points does not exceed the floating error of transparency. The region covered by the detection points in the set of detection points with the largest number of elements is taken as the first part; The area covered by the detection point whose transparency difference with impurities does not exceed that of the transparency detection point is designated as the second part, and the area covered by the detection point whose transparency difference with bubbles does not exceed that of the transparency detection point is designated as the third part. The first, second, and third parts are merged into the non-detection part, and the part of the image to be detected other than the non-detection part is taken as the detection part.

[0010] Preferably, adjusting the part to be detected to obtain the corrected part includes the following steps: The average pixel value of the pixels in the non-detection part is taken to obtain the correction value. The pixel value of the pixels in the part to be detected is subtracted from the correction value to obtain the adjustment value of the pixel. The pixel value of the pixel point equal to the adjustment value is taken as the associated pixel point of the pixel point corresponding to the adjustment value. In the part to be detected, the pixels are replaced by the associated pixels of the pixels, and the resulting image is used as the correction part.

[0011] Preferably, the method for forming at least one colony composition at a sampling point based on the transparency of the sampling point includes the following steps: The pixel value of the pixel at the sampling point in the correction part is used as the transparency of the sampling point. At least one colony is summarized to form a colony set. A subset of the colony set is used as the colony subset. The transparency of colonies in the colony subset is superimposed to obtain the overall transparency of the colony subset. The lower transparency is obtained by subtracting the fluctuation error from the overall transparency. The upper transparency is obtained by adding the overall transparency to the fluctuation error. Using the lower and upper transparency as endpoints, the transparency range of the colony subset is formed. If the transparency of the sampling point belongs to the transparency range of the colony subset, then the colonies in the colony subset are used as the colony composition scheme.

[0012] Preferably, the process of screening the colony composition schemes of non-reference sampling points to obtain the target colony composition schemes for non-reference sampling points includes the following steps: Based on the direction of urine movement in the urinary tract, the reference sampling point located downstream of the non-reference sampling point and closest to it is designated as the first sampling point, and the reference sampling point located upstream of the non-reference sampling point and closest to it is designated as the second sampling point. The colonies in the colony composition schemes of the first and second sampling points are summarized to obtain a reference composition scheme; The number of colonies that are the same in the colony composition scheme and the reference composition scheme is taken as the test value. The test value is divided by the number of colonies in the colony composition scheme to obtain the overlap ratio between the colony composition scheme and the reference composition scheme. The colony composition scheme with the highest overlap with the reference composition scheme among the non-reference sampling points is taken as the target colony composition scheme for the non-reference sampling points.

[0013] Preferably, the analysis to obtain at least one target symptom and its severity in the image to be detected includes the following steps: The first count is the number of times the colony appears in the colony composition schemes of all reference sampling points, and the second count is the number of times the colony appears in the target colony composition schemes of all non-reference sampling points. The first and second counts of colonies are summed to obtain the total count of colonies. The total number of sampling points is used as the preset value. Colonies with a total count that is not zero are designated as target colonies. Symptoms of urinary tract infection caused by target colonies are designated as target symptoms. The severity of the target symptoms is determined by dividing the total count of colonies by a preset value.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: By identifying the transparency of colonies, impurities, and bubbles, obtaining corrections, forming a colony composition scheme at the sampling point, and obtaining a target colony composition scheme at non-reference sampling points, ultrasound imaging of urine in the urinary tract can be performed. Based on the acquired images, the colonies present in the urine can be analyzed. During the analysis, the superposition of colonies is taken into account, and areas without colonies are excluded, thereby enabling a more accurate determination of the colony types. Furthermore, based on the type and size of the colonies, the severity of the symptoms they cause can be estimated, thus enabling more accurate treatment. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the urinary tract infection identification method based on ultrasound imaging analysis of the present invention. Figure 2 This is a schematic diagram of the process of dividing a sample urine image into a urine part and a non-urine part according to the present invention; Figure 3This is a schematic diagram illustrating the process of obtaining the transparency of urine portion and identifying the transparency of impurities and bubbles in non-urine portion according to the present invention. Figure 4 This is a schematic diagram illustrating the process of analyzing the transparency of bacterial colonies to obtain the transparency fluctuation error in this invention. Figure 5 This is a schematic diagram of the process of dividing an image to be detected into a non-detection part and a detection part according to the present invention; Figure 6 This is a schematic diagram of the process of adjusting the part to be detected to obtain the corrected part according to the present invention; Figure 7 This is a flowchart illustrating the process of forming at least one colony composition scheme at a sampling point based on the transparency of the sampling point according to the present invention. Figure 8 This is a flowchart illustrating the process of screening colony composition schemes for non-reference sampling points to obtain target colony composition schemes for non-reference sampling points according to the present invention. Figure 9 This is a schematic diagram illustrating the process of analyzing and obtaining at least one target symptom and its severity in an image to be detected, as described in this invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] Reference Figure 1 As shown, the method for identifying urinary tract infections based on ultrasound imaging analysis includes: Based on big data, at least one bacterial colony causing urinary tract infection is obtained, and the symptoms of urinary tract infection caused by the bacterial colony are obtained. Ultrasound imaging is used to obtain sample images of the bacterial colony. Based on the sample images, the transparency of the location of the bacterial colony is extracted. The urine carrying the bacterial colony in the sample image is the sample urine. The sample urine was produced without urinary tract infection. The sample urine image was divided into urine and non-urine parts. The transparency of the urine part was obtained, and the transparency of impurities and air bubbles in the non-urine part was identified. Based on the transparency of the urine sample, the transparency of the bacterial colonies is analyzed, resulting in a fluctuation error in transparency. Ultrasound imaging is used to obtain images of the urinary tract to be tested. Based on the transparency of the urine portion, the images are divided into non-detection portions and detection portions. Based on the transparency of impurities and bubbles, the part to be detected is adjusted to obtain the corrected part; In the correction section, at least one sampling point is uniformly set, and based on the transparency of the sampling point, at least one colony composition scheme is formed at the sampling point; Sampling points with a colony composition scheme of 1 were used as reference sampling points, and sampling points with a colony composition scheme of more than 1 were used as non-reference sampling points. Based on the reference sampling points, the colony composition schemes of non-reference sampling points are screened to obtain the target colony composition schemes of non-reference sampling points. The analysis reveals at least one target symptom and its severity in the image to be detected.

[0018] In this approach, urinary tract infections (UTIs) are primarily identified through the analysis of ultrasound imaging. Different substances exhibit different characteristics under ultrasound, resulting in varying pixel values. This allows for identification, making UTI identification relatively straightforward—detecting bacterial colonies is sufficient. However, the actual causes of UTIs are diverse, with different bacterial colonies causing different infections, leading to varying treatment plans. To achieve better treatment outcomes, it is necessary to identify the specific bacterial species present for targeted treatment. Impurities and air bubbles in urine, along with the urine itself, can be visualized, necessitating the exclusion of these factors during identification. Furthermore, the small size of bacterial colonies and their potential overlap further complicate identification. Therefore, a series of subsequent steps address these issues.

[0019] Extracting the transparency of the location of bacterial colonies based on sample images includes the following steps: In the sample image, the average pixel value of the pixels at the location of the colony is taken to obtain the transparency of the location of the colony.

[0020] Here, we get the transparency of the location of the colony. Since the colony is contained in the urine, we do not get the transparency of the colony itself.

[0021] Reference Figure 2 As shown, dividing a sample urine image into a urine portion and a non-urine portion involves the following steps: At least one sample point is uniformly selected from the sample urine image, and the pixel value of the pixel at the sample point is obtained as the feature value of the sample point. Classify the sample points to obtain at least one set of sample points, such that the sample points in the set have the same feature values; The set of sample points with the largest number of elements is taken as the target sample point set. The area covered by the sample points in the target sample point set is taken as the urine part. The part of the sample urine other than the urine part is taken as the non-urine part.

[0022] Urine contains colonies, impurities, and bubbles, but the proportion of these is highest in urine. Therefore, a target sample point set is selected based on this. The sample points in the target sample point set are all outside the range of colonies, impurities, and bubbles, and thus they are all pure urine. Therefore, the urine part and the non-urine part can be distinguished.

[0023] Reference Figure 3 As shown, obtaining the transparency of the urine portion and identifying the transparency of impurities and bubbles in the non-urine portion includes the following steps: The transparency of the urine portion is obtained by averaging the pixel values ​​of the pixels in the urine portion. At least one contour line is identified in the non-urine portion. The average pixel value of the pixels in the region within the contour line is taken to obtain a first value. The average pixel value of the pixels on the contour line is taken to obtain a second value. If the first value and the second value are not equal, the outline is taken as the target outline, the area enclosed by the target outline is taken as the bubble part, and the area in the non-urine part other than the bubble part is taken as the impurity part. The transparency of the bubble is obtained by averaging the pixel values ​​of the pixels within the bubble portion, and the transparency of the impurity is obtained by averaging the pixel values ​​of the pixels within the impurity portion.

[0024] The urine portion is pure urine without any other interference, so its transparency can be obtained. However, the non-urine portion contains impurities and air bubbles. The outline of the air bubbles is dark, while the inside is transparent. Due to their surface tension and relatively large size, the air bubbles will push away the impurities, meaning that the two will hardly overlap. Therefore, the air bubbles can be identified first, and the remaining part can be regarded as the impurity part, thus obtaining the corresponding transparency.

[0025] Reference Figure 4 As shown, the analysis of colony transparency and the resulting fluctuation error in transparency include the following steps: The transparency of the colony is obtained by subtracting the transparency of the urine portion from the transparency of the location of the colony. Using ultrasound imaging, at least one image of the urine sample is acquired at different times to obtain at least one sampled image. The average pixel value of each pixel in the sampled image is taken to obtain the recognition value of the sampled image. The difference between the maximum and minimum recognition values ​​of the sampled image is used to obtain the floating error of transparency.

[0026] Since urine and bacterial colonies overlap here, in order to obtain the transparency of the bacterial colonies, it is necessary to remove the transparency of the urine portion. Since the urine used for both is the same type of urine, this can be done. During image acquisition, factors in the acquisition environment, mainly different lighting conditions, can cause variations in the acquired images. To avoid these variations affecting recognition, a transparency fluctuation error is introduced. This transparency fluctuation error is then used to implement redundant settings for recognition.

[0027] Reference Figure 5 As shown, dividing the image to be detected into a non-detection part and a detection part includes the following steps: At least one detection point is uniformly selected in the image to be detected, and the detection points are classified to obtain at least one set of detection points, satisfying that the difference in pixel values ​​of the detection points in the set of detection points does not exceed the floating error of transparency. The region covered by the detection points in the set of detection points with the largest number of elements is taken as the first part; The area covered by the detection point whose transparency difference with impurities does not exceed that of the transparency detection point is designated as the second part, and the area covered by the detection point whose transparency difference with bubbles does not exceed that of the transparency detection point is designated as the third part. The first, second, and third parts are merged into the non-detection part, and the part of the image to be detected other than the non-detection part is taken as the detection part.

[0028] When performing colony identification, it is necessary to first determine the range in which the colony is located, i.e. the part to be tested. First, the pure urine part needs to be removed. Second, the impurities and air bubbles need to be removed because the impurities and air bubbles are relatively large, so they will push the colonies apart and make it difficult for them to overlap.

[0029] Reference Figure 6 As shown, adjusting the part to be detected to obtain the corrected part includes the following steps: The average pixel value of the pixels in the non-detection part is taken to obtain the correction value. The pixel value of the pixels in the part to be detected is subtracted from the correction value to obtain the adjustment value of the pixel. The pixel value of the pixel point equal to the adjustment value is taken as the associated pixel point of the pixel point corresponding to the adjustment value. In the part to be detected, the pixels are replaced by the associated pixels of the pixels, and the resulting image is used as the correction part.

[0030] Since different types of urine have varying transparency, they are removed to avoid affecting recognition. This results in an adjustment value for each pixel. The pixels corresponding to these adjustment values ​​are then used to replace the pixels in the part to be detected, thus obtaining the corrected part, which is the part that does not contain the influence of urine and only contains bacterial colonies.

[0031] Reference Figure 7As shown, based on the transparency of the sampling point, forming at least one colony composition scheme at the sampling point includes the following steps: The pixel value of the pixel at the sampling point in the correction part is used as the transparency of the sampling point. At least one colony is summarized to form a colony set. A subset of the colony set is used as the colony subset. The transparency of colonies in the colony subset is superimposed to obtain the overall transparency of the colony subset. The lower transparency is obtained by subtracting the fluctuation error from the overall transparency. The upper transparency is obtained by adding the overall transparency to the fluctuation error. Using the lower and upper transparency as endpoints, the transparency range of the colony subset is formed. If the transparency of the sampling point belongs to the transparency range of the colony subset, then the colonies in the colony subset are used as the colony composition scheme.

[0032] Since the overlap of colonies at each location is unknown—that is, whether there is only one colony or multiple colonies overlapping—a combined approach is used to estimate the colony composition scheme at each location. Depending on the actual situation, some sampling points can determine a unique colony composition scheme, while others cannot. In the subsequent process, the sampling points with a unique colony composition scheme are used to filter the colony composition schemes of the sampling points with an undetermined unique colony composition scheme.

[0033] Reference Figure 8 As shown, the process of screening colony composition schemes for non-reference sampling points to obtain target colony composition schemes for non-reference sampling points includes the following steps: Based on the direction of urine movement in the urinary tract, the reference sampling point located downstream of the non-reference sampling point and closest to it is designated as the first sampling point, and the reference sampling point located upstream of the non-reference sampling point and closest to it is designated as the second sampling point. The colonies in the colony composition schemes of the first and second sampling points are summarized to obtain a reference composition scheme; The number of colonies that are the same in the colony composition scheme and the reference composition scheme is taken as the test value. The test value is divided by the number of colonies in the colony composition scheme to obtain the overlap ratio between the colony composition scheme and the reference composition scheme. The colony composition scheme with the highest overlap with the reference composition scheme among the non-reference sampling points is taken as the target colony composition scheme for the non-reference sampling points.

[0034] Since urine flows in one direction only, non-reference sampling points must lie between the first and second sampling points. The urine at these points originates from the second sampling point, but may become infected, resulting in new colonies. These colonies will inevitably flow to the first sampling point. Therefore, in an ideal scenario, the sum of the colony composition schemes at the first and second sampling points is consistent with the target colony composition scheme at the non-reference sampling point. However, since there are multiple colony composition schemes at the non-reference sampling point, there will inevitably be one that has the smallest difference from the sum of the colony composition schemes at the first and second sampling points. Therefore, this one can be used as the target colony composition scheme for the non-reference sampling point.

[0035] Reference Figure 9 As shown, the analysis to obtain at least one target symptom and its severity in the image to be detected includes the following steps: The first count is the number of times the colony appears in the colony composition schemes of all reference sampling points, and the second count is the number of times the colony appears in the target colony composition schemes of all non-reference sampling points. The first and second counts of colonies are summed to obtain the total count of colonies. The total number of sampling points is used as the preset value. Colonies with a total count that is not zero are designated as target colonies. Symptoms of urinary tract infection caused by target colonies are designated as target symptoms. The severity of the target symptoms is determined by dividing the total count of colonies by a preset value.

[0036] When the colony composition at each sampling point is determined, the overall situation of colony appearance can be determined. The more locations where it appears, the more severe the symptoms it causes. Since the symptoms it causes are definite, the type of medication can be determined, and the dosage can be set according to the severity of the symptoms.

[0037] Furthermore, this solution also proposes a storage medium on which a computer-readable program is stored, which, when invoked, executes the aforementioned method for identifying urinary tract infections based on ultrasound imaging analysis.

[0038] It is understandable that the storage medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state drive (SSD).

[0039] In summary, the advantages of this invention are as follows: by identifying the transparency of colonies, impurities, and bubbles, obtaining the corrected portion, forming a colony composition scheme at the sampling point, and obtaining a target colony composition scheme at a non-reference sampling point, ultrasound imaging of urine in the urinary tract can be performed. Based on the acquired images, the colonies present in the urine can be analyzed. During the analysis, the superposition of colonies is taken into account, and areas without colonies are excluded, thereby enabling a more accurate determination of the colony types. Furthermore, based on the type and scale of the colonies, the severity of the symptoms caused can be estimated, leading to more accurate treatment.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for identifying urinary tract infections based on ultrasound imaging analysis, characterized in that, include: Based on big data, at least one bacterial colony causing urinary tract infection is obtained, and the symptoms of urinary tract infection caused by the bacterial colony are obtained. Ultrasound imaging is used to obtain sample images of the bacterial colony. Based on the sample images, the transparency of the location of the bacterial colony is extracted. The urine carrying the bacterial colony in the sample image is the sample urine. The sample urine was produced without urinary tract infection. The sample urine image was divided into urine and non-urine parts. The transparency of the urine part was obtained, and the transparency of impurities and air bubbles in the non-urine part was identified. Based on the transparency of the urine sample, the transparency of the bacterial colonies is analyzed, resulting in a fluctuation error in transparency. Ultrasound imaging is used to obtain images of the urinary tract to be tested. Based on the transparency of the urine portion, the images are divided into non-detection portions and detection portions. Based on the transparency of impurities and bubbles, the part to be detected is adjusted to obtain the corrected part; In the correction section, at least one sampling point is uniformly set, and based on the transparency of the sampling point, at least one colony composition scheme is formed at the sampling point; Sampling points with a colony composition scheme of 1 were used as reference sampling points, and sampling points with a colony composition scheme of more than 1 were used as non-reference sampling points. Based on the reference sampling points, the colony composition schemes of non-reference sampling points are screened to obtain the target colony composition schemes of non-reference sampling points. The analysis reveals at least one target symptom and its severity in the image to be detected.

2. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 1, characterized in that, The process of extracting the transparency of the location of bacterial colonies based on sample images includes the following steps: In the sample image, the average pixel value of the pixels at the location of the colony is taken to obtain the transparency of the location of the colony.

3. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 2, characterized in that, The process of dividing the sample urine image into a urine portion and a non-urine portion includes the following steps: At least one sample point is uniformly selected from the sample urine image, and the pixel value of the pixel at the sample point is obtained as the feature value of the sample point. Classify the sample points to obtain at least one set of sample points, such that the sample points in the set have the same feature values; The set of sample points with the largest number of elements is taken as the target sample point set. The area covered by the sample points in the target sample point set is taken as the urine part. The part of the sample urine other than the urine part is taken as the non-urine part.

4. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 3, characterized in that, The process of obtaining the transparency of the urine portion and identifying the transparency of impurities and bubbles in the non-urine portion includes the following steps: The transparency of the urine portion is obtained by averaging the pixel values ​​of the pixels in the urine portion. At least one contour line is identified in the non-urine portion. The average pixel value of the pixels in the region within the contour line is taken to obtain a first value. The average pixel value of the pixels on the contour line is taken to obtain a second value. If the first value and the second value are not equal, the outline is taken as the target outline, the area enclosed by the target outline is taken as the bubble part, and the area in the non-urine part other than the bubble part is taken as the impurity part. The transparency of the bubble is obtained by averaging the pixel values ​​of the pixels within the bubble portion, and the transparency of the impurity is obtained by averaging the pixel values ​​of the pixels within the impurity portion.

5. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 4, characterized in that, The analysis yields the transparency of the colonies, and the fluctuation error in transparency is determined by the following steps: The transparency of the colony is obtained by subtracting the transparency of the urine portion from the transparency of the location of the colony. Using ultrasound imaging, at least one image of the urine sample is acquired at different times to obtain at least one sampled image. The average pixel value of each pixel in the sampled image is taken to obtain the recognition value of the sampled image. The difference between the maximum and minimum recognition values ​​of the sampled image is used to obtain the floating error of transparency.

6. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 5, characterized in that, The process of dividing the image to be detected into a non-detection portion and a detection portion includes the following steps: At least one detection point is uniformly selected in the image to be detected, and the detection points are classified to obtain at least one set of detection points, satisfying that the difference in pixel values ​​of the detection points in the set of detection points does not exceed the floating error of transparency. The region covered by the detection points in the set of detection points with the largest number of elements is taken as the first part; The area covered by the detection point whose transparency difference with impurities does not exceed that of the transparency detection point is designated as the second part, and the area covered by the detection point whose transparency difference with bubbles does not exceed that of the transparency detection point is designated as the third part. The first, second, and third parts are merged into the non-detection part, and the part of the image to be detected other than the non-detection part is taken as the detection part.

7. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 6, characterized in that, The process of adjusting the part to be detected to obtain the corrected part includes the following steps: The average pixel value of the pixels in the non-detection part is taken to obtain the correction value. The pixel value of the pixels in the part to be detected is subtracted from the correction value to obtain the adjustment value of the pixel. The pixel value of the pixel point equal to the adjustment value is taken as the associated pixel point of the pixel point corresponding to the adjustment value. In the part to be detected, the pixels are replaced by the associated pixels of the pixels, and the resulting image is used as the correction part.

8. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 7, characterized in that, The scheme for forming at least one colony composition at a sampling point based on the transparency of the sampling point includes the following steps: The pixel value of the pixel at the sampling point in the correction part is used as the transparency of the sampling point. At least one colony is summarized to form a colony set. A subset of the colony set is used as the colony subset. The transparency of colonies in the colony subset is superimposed to obtain the overall transparency of the colony subset. The lower transparency is obtained by subtracting the fluctuation error from the overall transparency. The upper transparency is obtained by adding the overall transparency to the fluctuation error. Using the lower and upper transparency as endpoints, the transparency range of the colony subset is formed. If the transparency of the sampling point belongs to the transparency range of the colony subset, then the colonies in the colony subset are used as the colony composition scheme.

9. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 8, characterized in that, The process of screening colony composition schemes for non-reference sampling points to obtain target colony composition schemes for non-reference sampling points includes the following steps: Based on the direction of urine movement in the urinary tract, the reference sampling point located downstream of the non-reference sampling point and closest to it is designated as the first sampling point, and the reference sampling point located upstream of the non-reference sampling point and closest to it is designated as the second sampling point. The colonies in the colony composition schemes of the first and second sampling points are summarized to obtain a reference composition scheme; The number of colonies that are the same in the colony composition scheme and the reference composition scheme is taken as the test value. The test value is divided by the number of colonies in the colony composition scheme to obtain the overlap ratio between the colony composition scheme and the reference composition scheme. The colony composition scheme with the highest overlap with the reference composition scheme among the non-reference sampling points is taken as the target colony composition scheme for the non-reference sampling points.

10. The method for identifying urinary tract infections based on ultrasound imaging analysis according to claim 9, characterized in that, The analysis to obtain at least one target symptom and its severity in the image to be detected includes the following steps: The first count is the number of times the colony appears in the colony composition schemes of all reference sampling points, and the second count is the number of times the colony appears in the target colony composition schemes of all non-reference sampling points. The first and second counts of colonies are summed to obtain the total count of colonies. The total number of sampling points is used as the preset value. Colonies with a total count that is not zero are designated as target colonies. Symptoms of urinary tract infection caused by target colonies are designated as target symptoms. The severity of the target symptoms is determined by dividing the total count of colonies by a preset value.

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