Urine detection method, urine detection device and computer readable storage medium

Abstract

The embodiment of the present application discloses a urine detection method, a urine detection device and a computer-readable storage medium. The detection method includes: controlling an image acquisition device to shoot a urine sample located in a counting pool to obtain a first preset number of captured images corresponding to the urine sample; obtaining the counting information of the formed components contained in each target area of ​​the second preset number of target areas from the captured images of the first preset number; determining the third preset number of target areas in the second preset number as tracking areas based on the counting information of the formed components contained in each target area; controlling the image acquisition device to shoot the third preset number of tracking areas to obtain the counting information of the formed components in the urine sample. The embodiment of the present application obtains the tracking area containing the formed components and tracks and shoots to obtain the information of the formed components contained in the urine sample, which is conducive to saving time for urine detection.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a urine testing method, a urine testing device, and a computer-readable storage medium. Background Art

[0002] Urine analysis diagnostic equipment (such as urine formed component analyzer) mainly obtains images of formed components in urine samples by taking pictures under a microscope and performs analysis. Urine analysis diagnostic equipment determines multiple target locations of urine samples and takes pictures and tracks each of the multiple target locations under a microscope. However, when there are too many target areas to be located, tracking takes too long, affecting the real-time nature of sample analysis. Summary of the invention

[0003] The embodiments of the present application provide a urine testing method, a urine testing device, and a computer-readable storage medium, which can save time in determining the counting information of a urine sample.

[0004] A first aspect of an embodiment of the present application provides a urine detection system, wherein the urine detection device includes an image acquisition device, and the urine detection device includes:

[0005] An image acquisition device, used for photographing the urine sample in the counting pool to obtain a first preset number of photographed images corresponding to the urine sample;

[0006] A processor is connected to the image acquisition device, and the processor is used to obtain counting information of formed components contained in each target area of ​​a second preset number of target areas from the captured images of the first preset number; the processor determines that a third preset number of target areas of the second preset number are tracking areas based on the counting information of formed components contained in each target area; the processor is also used to control the image acquisition device to capture the third preset number of tracking areas to obtain statistical information of formed components in the urine sample.

[0007] A second aspect of an embodiment of the present application provides a urine detection method, which is applied to a urine detection device, wherein the urine detection device includes an image acquisition device, and the urine detection method includes:

[0008] Controlling the image acquisition device to photograph the urine sample in the counting pool to obtain a first preset number of photographed images corresponding to the urine sample;

[0009] Acquire counting information of tangible components contained in each of the second preset number of target areas from the first preset number of captured images;

[0010] Determining a third preset number of target areas in the second preset number as tracking areas based on the counting information of the tangible components included in each target area;

[0011] The image acquisition device is controlled to photograph a third preset number of the tracking areas to obtain statistical information of the formed components in the urine sample.

[0012] A third aspect of an embodiment of the present application provides a computer-readable storage medium, which is used to store a computer program for electronic data exchange, wherein the computer program enables a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0013] The urine testing method, urine testing device and computer-readable storage medium obtain the tracking area containing formed components and track and photograph the tracking area to obtain the counting information of the formed components contained in the urine sample, which is helpful to save the time of urine testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of the steps of a urine detection method in one embodiment of the present application.

[0016] Figure 2 It is a hardware structure block diagram of a urine testing device in one embodiment of the present application.

[0017] Figure 3 It is a schematic diagram of capturing an image in one embodiment of the present application.

[0018] Figure 4 It is a schematic diagram of capturing an image in yet another embodiment of the present application.

[0019] Figure 5 It is a schematic diagram of the target area distribution in one embodiment of the present application.

[0020] Figure 6 It is a schematic diagram of the target area distribution in another embodiment of the present application.

[0021] Figure 7 This is a flowchart of the steps for obtaining a tracking area in an embodiment of the present application.

[0022] Figure 8This is a flowchart of the steps for obtaining a tracking area in another embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The embodiments of the present application are described in detail below.

[0027] See also Figure 1 , which is a flow chart of the steps of a urine detection method in an embodiment of the present application. The urine detection method comprises the following steps:

[0028] Step 100: Control the image acquisition device to photograph the urine sample in the counting pool to obtain a first preset number of photographed images corresponding to the urine sample.

[0029] Please also read Figure 2, shown is a hardware structure block diagram of a urine detection device in an embodiment of the present application. The urine detection device 50 may include a storage device 502, a display screen 506, a microscope 514, and a processor 500 connected or controlled by a connection 516 to the storage device 502, the display screen 506, and the microscope 514. Among them, the microscope 514 includes a light source device 504, a counting pool 508, an image acquisition device 510, and an objective lens 512. The counting pool 508 can be arranged on the sample stage of the microscope 50, and the position of the counting pool 508 can be adjusted by moving along the first direction, the second direction, or the third direction under the drive of the driving mechanism to form different shooting positions on the counting pool 508, wherein the first direction is perpendicular to the second direction and is located in the same plane. The microscope 514 may include different types of objective lenses 512 (such as high-power lens, low-power lens), and the objective lens 512 is used to enlarge the urine sample loaded in the counting pool 508. The image acquisition device 510 shoots the urine sample in the counting pool 508 through the objective lens 512.

[0030] In this embodiment, when the urine sample loaded or inserted into the counting pool 508 is photographed, the processor 500 can control the light source device 504 to emit light to illuminate the urine sample loaded into the counting pool 508, and the processor 500 can control the driving mechanism to move the counting pool 508 to a preset shooting position, and select the high-power lens or the low-power lens in the objective lens 512 to aim at the counting pool 508. The processor 500 uses the image acquisition device 510 and the objective lens 512 to shoot the preset shooting position to obtain a first preset number of photographed images 530 (shown in FIG. 1 ) corresponding to the urine sample. Figure 3 In one embodiment, the processor 500 may control the objective lens 512 to be at different positions, such as adjusting the position of the objective lens 512 or the counting cell in a third direction for focusing processing, wherein the third direction may be perpendicular to the plane formed by the first direction and the second direction.

[0031] Please also read Figure 3 , which is a schematic diagram of capturing images in one embodiment of the present application. The first preset number of captured images 530 may include one or more captured images, each of which may include urine liquid 534 and one or more formed components 532 located in the urine liquid 534. In this embodiment, the processor 500 may capture the counting pool 508 through a low-power lens to generate the captured image 530. In other embodiments, the captured image 530 may not include any formed component 532.

[0032] Please also read Figure 4, shown is a schematic diagram of capturing an image in another embodiment of the present application. Since the counting pool 508 has a certain size and the field of view of the objective lens 512 of the microscope 514 is limited, it may not be possible to cover the space or shooting area where the entire counting pool 508 is located at one time. Therefore, in order to obtain an image that can cover the entire counting pool 508, the processor 500 can shoot multiple times at different shooting positions in the counting pool 508 through the microscope 514 to obtain multiple captured images 530. For example, the processor 500 can control the action of the driving mechanism to control the counting pool 508 to be located at different shooting positions, and then, when shooting at each shooting position, the captured image 530 of the urine sample at the corresponding shooting position can be obtained. After multiple shootings, the processor 500 can obtain multiple captured images 530 to cover the entire counting pool 508, which is conducive to improving the accuracy of subsequent determination of the counting information of the formed components in the urine sample. In one embodiment, the captured images 530 obtained after being captured at different shooting positions may have some overlapping areas. The processor 500 can obtain the overlapping areas or the same image areas in the different captured images 530 based on image recognition, segmentation and / or registration related methods, and perform splicing processing on the obtained multiple captured images 530 to exclude the parts containing the same image areas in the different captured images 530. The processor 500 can also synthesize the captured images after splicing processing into the same image, so that the user can view the distribution of the formed components of the urine sample in the entire counting pool 508 in the same image.

[0033] Step 102: Obtain, from the first preset number of captured images, counting information of tangible components contained in each target area of ​​a second preset number of target areas.

[0034] In this embodiment, in order to save time for detecting urine samples, the processor 500 can determine the location based on the area containing the formed components in the image 530 captured by the low-power microscope, and then track and capture the determined location through the high-power microscope to obtain the outline of each formed component, and identify the category of the formed component based on the outline of the formed component, and then, the category of each formed component in the urine sample can be analyzed more accurately, and counting information such as the number of each type of formed component can be obtained. Therefore, if the captured image 530 does not contain an area containing formed components, there is no need to perform tracking and capturing, and the purpose of saving detection time is achieved by reducing the tracking and capturing of the area not containing formed components.

[0035] After obtaining a first preset number of captured images 530 through a low-power lens, the processor 500 may capture the images 530 for identification, and determine the position information of the tangible components contained in the first preset number of captured images 530 to achieve the purpose of positioning.

[0036] In this embodiment, the formed elements 532 may include, but are not limited to, red blood cells, white blood cells, bacteria, yeast, crystals, casts, and epithelial cells. The processor 150 determines each formed element 532 included in the first preset number of captured images 530 through image recognition, and obtains position information P of each formed element 532. n,i =(n,x i ,y i ,w i ,h i ), where n represents the nth captured image 530 in the first preset number, x i ,y i ,w i ,h i Respectively represent the vertical and horizontal coordinates of the center point of the i-th formed element 532 on the image and the width and height of the smallest rectangle circumscribing the formed element 532. Since the shapes of different formed elements may be different, such as the shapes of red blood cells and white blood cells are approximately ellipsoidal (such as Figure 3 Formed ingredients P 1,1 ), tubular cells are generally long and thin (such as Figure 3 Formed ingredients P 1,2 ), therefore, the processor 150 can determine the minimum circumscribed rectangle 536 of each shaped component, and define the position information of the shaped component by the pixel position of the center of the minimum circumscribed rectangle 536 in the current captured image and the width and height of the minimum circumscribed rectangle. Figure 3 As shown, when the first preset number is 1, the position information of the tangible component 532 in the captured image 530 can be expressed as P 1,i or P i ;like Figure 4 As shown, when the first preset number is 9, the position information of the tangible component 532 in the captured image 530 can be expressed as P n,i , where n is an integer between 1 and 9.

[0037] Please also read Figure 5 , shown is a schematic diagram of the target area distribution in one embodiment of the present application. Since the field of view of the high-power scope has a certain size, the processor 150 can also obtain an image within a certain position range in the counting pool 508 in one shooting. When the processor 150 tracks and shoots the captured image 530 through the high-power scope, the processor 150 can perform block processing on a first preset number of captured images 530 to obtain a second preset number of target areas 540. Among them, the size and shape of the target area 540 can correspond to the range of the field of view of the high-power scope. For example, the processor 150 can block each captured image 530 in the form of several rows and columns, so that the size of each target area obtained can be equal. Figure 5As shown, the target area 540 obtained after the first preset number of captured images 530 are processed by block processing may contain formed components 532 or may not contain formed components 532. The number of formed components 532 in the target area 540 containing formed components 532 may be the same or different.

[0038] Please refer to Figure 3 , the processor 150 can perform block processing while performing the process of identifying the shaped component 532. For example, when the processor 150 identifies that there is a shaped component 532 at the current position of the captured image 530, the processor 150 can block the position where the shaped component 532 is located, such as determining a block of a preset size based on a vertex of the minimum circumscribed rectangle 536 of the identified shaped component 532, and can use the block of the preset size as the target area 540. For example, when the processor 150 identifies that the shaped component P 1,1 When the processor 150 uses the tangible component P 1,1 The vertex of the upper left corner of the minimum circumscribed rectangle of the target area 540 is used as the vertex of the upper left corner, and a region of a preset size is determined to include the tangible component in the set target area. 1,1 The vertex of the upper right corner of the minimum circumscribed rectangle of the target area 540 is used as the vertex of the upper right corner of the target area 540, or the target area is determined in other ways. In this embodiment, after determining the target area 540, the processor 150 can also identify the image in the target area 540 to determine whether the target area 540 also includes other tangible components. Figure 3 As shown, when the tangible component P is identified 1,1 After determining the target area 540, the tangible component P 1,2 It may also be located within the target area 540 .

[0039] Please also read Figure 6 , which is a schematic diagram of target area distribution in another embodiment of the present application. Since the target area is determined when the shaped component is identified, after the processor 150 identifies the shaped component on the first preset number of captured images 530, the target area 540 containing the shaped component can be determined, while the area in the captured image 530 that also contains the shaped component does not need to be determined as the target area.

[0040] The processor 150 may calculate the count information K of each target partition based on the position information of each tangible component. n,t Since each target partition has a corresponding position range, the processor 150 can calculate the number of shaped components whose position information is located in the position range of the target partition, and use the calculated number as the counting information of the target partition. For example, the processor 150 can calculate the center coordinate value (xi ,y i ) to determine whether it is within the location range of the corresponding target partition. If so, it is counted into the target partition, and the counting information of the target partition is confirmed. Figure 5 As shown, the counting information K of each target area 540 n,t Can be 0 or greater than 0; Figure 6 As shown, the counting information K of each target area 540 n,t Can be a value not less than 1.

[0041] Step 104 : determining a third preset number of target areas in the second preset number as tracking areas based on the counting information of the tangible components included in each target area.

[0042] In one embodiment, after obtaining a second preset number of target areas 540 in the first preset number of captured images 530, the processor 150 may select the target area 540 containing the tangible component 532 as the tracking area to reduce the time waste caused by tracking the target area that does not contain the tangible component.

[0043] Please also read Figure 7 , shown is a flowchart of the steps of obtaining a tracking area in an embodiment of the present application, which includes:

[0044] Step 700, determining frequency information of each counting category.

[0045] In this embodiment, the processor 150 determines the tracking area based on statistical principles.

[0046] Since the counting information K corresponding to each target area n,t With corresponding values, where the count information K n,t The value range is at the minimum value min(K n,t ) and the maximum value max(K n,t ), the minimum value min(K n,t ) may be 0, so the minimum value min(K n,t ) and the maximum value max(K n,t ) may have 0 target areas, 1 target area, or multiple target areas (two or more target areas). In this embodiment, different counting information is defined as different counting categories, and the ratio of the number of target areas corresponding to each counting category to the second preset number is defined as the frequency information f of the corresponding counting category. k It can be expressed as:

[0047]

[0048] When the number of target areas in each captured image is the same (e.g. Figure 5 As shown in FIG. 1 , N is a first preset number of captured images, T is the number of target areas obtained after each captured image is processed by block division, N×T is a second preset number of target areas, and K n,t represents the tth target area in the nth captured image, and k represents the count category with a value of [0, max(K n,t )] or [min(K n,t ),max(K n,t )], so the frequency information f in the above formula k It can be expressed as traversing a second preset number of target areas N×T, and determining the counting information K of the current target area n,t is the counting information corresponding to the counting category k. After the counting category k is increased by 1 and the ratio is calculated with the second preset number, the frequency information corresponding to each counting category k can be obtained after the traversal is completed.

[0049] When different captured images have different numbers of target areas (such as Figure 6 As shown), the ratio of the number of target areas corresponding to each counting category to the second preset number is defined as the frequency information f corresponding to the counting category. k By traversing a second preset number of target areas, the counting information K of the current target area can be determined. n,t is the counting information corresponding to the counting category k, and the frequency information corresponding to each counting category k can be obtained after the traversal is completed by adding 1 to the ratio of the counting category k to the second preset number. In this embodiment, the frequency information corresponding to each counting category is accumulated to 1.

[0050] Step 702: Determine a third preset number of tracking areas according to the frequency information of each counting category.

[0051] In this embodiment, the processor 150 obtains a target counting category that meets a first preset condition in the frequency information of each counting category, and determines that a target area corresponding to the target counting category is a tracking area, wherein the target counting category includes one or more counting categories.

[0052] The processor 150 may select a fourth preset number of counting categories from the frequency information of the counting categories in a preset order, wherein the preset order is a size order, and the sum of the frequency information corresponding to the fourth preset number of counting categories is not less than a preset threshold. For example, the processor 150 may arrange the frequency information of each counting category in a descending order or a descending order, and select the fourth preset number of counting categories from the largest order as the target counting category; or the processor 150 may arrange the frequency information of each counting category in a descending order or a descending order, and accumulate the frequency information of each counting category from the largest order until it is not less than a preset threshold (such as 0.95), at which time, the processor 150 may use the selected counting category as the target counting category. For example, when the target counting category is 0.1, 0.2, and 0.3, the processor 150 may select all target areas A1 with a frequency information of 0.1, all target areas A2 with a frequency information of 0.2, and all target areas A3 with a frequency information of 0.3 from the second preset number, thereby obtaining a third preset number (i.e., A1+A2+A3) of target areas. The processor 150 uses the obtained third preset number of target areas as the tracking area.

[0053] like Figure 5 As shown, there is a target area 540 that does not include a tangible component 532 in the captured image 530 , and therefore, the processor 150 may exclude the target area 540 that does not include a tangible component 532 .

[0054] In one embodiment, since there may be more areas in the urine sample that are target areas 540 that do not contain formed components 532, when calculating each counting category, the frequency information obtained for the counting category of 0 (i.e., the counting category that does not contain formed components) is larger. Therefore, the processor 150 can exclude the target area that does not contain formed components based on the frequency information of the counting category. For example, the processor 150 obtains the first remaining counting category whose frequency information does not exceed the preset frequency information in each counting category to exclude the target area corresponding to the counting category of 0. The processor 150 also selects a fourth preset number of counting categories from the frequency information of the first remaining counting category in a preset order, and obtains a third preset number of tracking areas.

[0055] In one embodiment, the processor 150 may exclude the target area that does not contain a tangible component directly based on the counting categories. For example, the processor 150 obtains the second remaining counting categories whose counting categories do not include a preset counting category (such as the counting category corresponding to the target area whose counting information is 0), and selects a fourth preset number of counting categories from the frequency information of the second remaining counting categories in a preset order, and the third preset number of tracking areas can also be obtained.

[0056] Please also read Figure 8, shown is a flow chart of the steps of obtaining the tracking area in another embodiment of the present application.

[0057] In one embodiment, since the urine testing device 50 has a tracking upper limit value, the processor 150 further determines the tracking area based on the tracking upper limit value.

[0058] Step 800 , controlling the normalization process to be performed on the frequency information of each target counting category, and obtaining the normalized information corresponding to each target counting category.

[0059] In this embodiment, the processor 150 obtains the minimum frequency information in the target count category, and controls the ratio of the frequency information of each target count category to the minimum frequency information as the normalized information of the corresponding target count category. In one embodiment, the normalized information of each target count category is the value obtained by rounding down the ratio of the frequency information of the corresponding target count category to the minimum frequency information. For example,

[0060]

[0061] Among them, k′ is the determined target counting category, k′ i ∈k′, symbol In other embodiments, the normalized information of each target count category may not be rounded down.

[0062] Step 802, calculating the accumulated value of the normalized information corresponding to each target counting category.

[0063] The processor 150 calculates the accumulated value S as:

[0064]

[0065] Step 804 , determining whether the accumulated value is greater than the tracking upper limit value, if so, executing step 806 ; if not, executing step 808 .

[0066] In this embodiment, when the accumulated value S is greater than the tracking upper limit value L, the processor 150 selects a corresponding number of target areas corresponding to each target counting category as tracking areas; when the accumulated value S is not greater than the tracking upper limit value L, the processor 150 selects the corresponding target areas as tracking areas from the third preset number of target areas in the aforementioned step 702 according to a preset order.

[0067] Step 806, calculate the ratio information of the tracking upper limit value to the accumulated value, and determine the third preset number of tracking areas based on the ratio information and the normalization information corresponding to each target count category. For example, the processor 150 calculates the ratio information Q of the tracking upper limit value to the accumulated value as:

[0068]

[0069] Thus, for the target counting category k′ i , the number of target areas selected by the processor 150 as tracking areas in the corresponding target counting category is:

[0070]

[0071] Then, the third preset number of tracking areas can be expressed as In one embodiment, the processor 150 may select a target counting category k′ from i Randomly select the corresponding target area The target area is used as the tracking area.

[0072] Step 808: Determine the target areas corresponding to the second preset number as the tracking areas in descending order according to the frequency information corresponding to each target counting category, and the number of the determined target areas does not exceed the tracking upper limit.

[0073] When the accumulated value S is not greater than the tracking upper limit value L, the processor 150 controls the frequency information corresponding to each target count category to be arranged in descending order, and determines the tracking area. For example, the processor 150 may first select all target areas in the target count category with the largest frequency information as the tracking area, and then select all target areas in the target count category with the second largest frequency information as the tracking area, until all tracking areas reach the tracking upper limit value.

[0074] Step 106 , controlling the image acquisition device to photograph a third preset number of the tracking areas to obtain statistical information of the formed components in the urine sample.

[0075] In this embodiment, when the third preset number of tracking areas in the second preset number of target areas is determined, the processor 150 may obtain the position range of each tracking area and the captured image where it is located to determine the tracking position. For example, the tracking position may be represented as the jth position range in the nth captured image. Figure 6 As shown, since the tracking area has a regular shape, the processor 150 may use the centroid of the tracking area as the tracking position, such as using the center of a rectangular tracking area as the tracking position.

[0076] When it is necessary to determine the specific category information of the formed components 532 in the tracking area 540, the processor 150 can track and shoot through a high-power microscope to obtain statistical information of the formed components in the urine sample. In this embodiment, after determining the tracking position of each tracking area, the processor 150 can control the drive mechanism to drive the counting pool 508 to a position corresponding to the tracking position. In this way, when the processor 150 moves to a position corresponding to the tracking position through a high-power microscope to shoot, it can be obtained that the formed components contained in the tracking area have an outline. The processor 150 can distinguish different categories of formed components based on the outline features of the formed components, and count the formed components of different categories to obtain statistical information of each formed component. After the tracking shooting of each tracking area is completed, the processor 150 can also determine the number of each type of formed components according to other related formed component identification methods, and can output statistical information of each formed component.

[0077] In one embodiment, the processor 500 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The storage device 502 is used to store the computer program and / or module, and the processor 500 implements various functions of the liquid pump control method by running or executing the computer program and / or module stored in the storage device 502, and calling the data stored in the storage device 502. The storage device 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc. In addition, the storage device 502 may include a high-speed random access storage device, and may also include a non-volatile storage device, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0078] The display screen 506 can display a user interface (UI) or a graphical user interface (GUI). The display screen 506 can also be used as an input device and an output device. The display screen 506 may include at least one of a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), an organic light emitting diode (OLED) touch display, a flexible touch display, a three-dimensional (3D) touch display, etc.

[0079] The processor 500 runs a program corresponding to the executable program code by reading the executable program code stored in the storage device 502, so as to execute the steps of the urine detection method in any of the above embodiments.

[0080] The above-mentioned urine detection method obtains the target area related to the formed components of the urine sample through a low-power microscope, calculates the counting information of the formed components contained in each target area, and determines the tracking area containing the formed components based on statistical analysis, and tracks and photographs the tracking area through a high-power microscope to achieve urine detection. By excluding the target area that does not contain the formed components, the urine detection is targeted at the target area containing the formed components, which is helpful to save time for urine detection.

[0081] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A urine testing device, characterized in that: The urine detection device includes an image acquisition device, and the urine detection device includes: An image acquisition device, used for photographing the urine sample in the counting pool to obtain a first preset number of photographed images corresponding to the urine sample; A processor connected to the image acquisition device, the processor being used to obtain counting information of tangible components contained in each of a second preset number of target areas from the first preset number of captured images; The processor determines, based on the counting information of the tangible components included in each target area, a third preset number of target areas in the second preset number as tracking areas, and different counting information corresponds to different counting categories; wherein the processor determines frequency information of the counting category, and the frequency information of the counting category is a ratio of the number of target areas of the counting category to the second preset number, and the processor further determines the third preset number of tracking areas according to the frequency information of the counting category; The processor is further used to control the image acquisition device to photograph a third preset number of the tracking areas to obtain statistical information of formed components in the urine sample; When determining the third preset number of tracking areas according to the frequency information of the counting categories, the processor controls the normalization processing to be performed on the frequency information of each target counting category to obtain the normalized information corresponding to each target counting category, and the processor further determines the third preset number of tracking areas based on the normalized information corresponding to each target counting category; When determining the third preset number of tracking areas based on the normalized information corresponding to each target counting category, the processor obtains the tracking upper limit value when the image acquisition device shoots the tracking area, and calculates the cumulative value of the normalized information corresponding to each target counting category; the processor calculates the ratio information of the tracking upper limit value and the cumulative value; the processor also determines the third preset number of tracking areas based on the ratio information and the normalized information corresponding to each target counting category.

2. The urine testing device according to claim 1, characterized in that: When obtaining the counting information of the tangible components contained in each target area of ​​the second preset number of target areas from the captured images of the first preset number, the processor is used to determine the second preset number of target areas located in the captured images of the first preset number, and control the identification of the tangible components of the second preset number of target areas to obtain the counting information of each target area.

3. The urine testing device according to claim 2, characterized in that: When determining a second preset number of target areas located in the first preset number of captured images, the processor controls the first preset number of captured images to be divided into blocks to obtain the second preset number of target areas; When controlling the identification of shaped components for the second preset number of target areas and obtaining the counting information of each target area, the processor is used to obtain the position information of each shaped component and calculate the counting information of each target partition according to the position information of the shaped component.

4. The urine testing device according to claim 3, characterized in that: When calculating the counting information of each target partition based on the position information of the formed components, the processor is used to determine the position range of each target partition in the corresponding target image; the processor also calculates the number of formed components whose position information is located in the position range of the target partition, and uses the calculated number as the counting information of the target partition.

5. The urine testing device according to claim 1, characterized in that: The urine testing equipment includes a microscope, which includes a low-power microscope and a high-power microscope. The processor controls the image acquisition device to photograph the urine sample through the low-power microscope; the processor also controls the image acquisition device to photograph a third preset number of tracking areas through the high-power microscope.

6. The urine testing device according to claim 5, characterized in that: The urine sample includes a plurality of viewing areas, and the image acquisition device obtains a first preset number of photographed images after photographing the plurality of viewing areas through the low-power lens of the microscope.

7. The urine testing device according to claim 1, characterized in that: When determining the third preset number of tracking areas according to the frequency information of each counting category, the processor obtains a target counting category that meets a first preset condition in the frequency information of each counting category, and determines the target area corresponding to the target counting category as the tracking area.

8. The urine testing device according to claim 7, characterized in that: When acquiring a target counting category that satisfies a first preset condition from the frequency information of each counting category, the processor selects a fourth preset number of counting categories from the frequency information of the counting categories in a preset order, wherein the preset order is a size order, and the sum of the frequency information corresponding to the fourth preset number of counting categories is not less than a preset threshold; the processor also determines that the fourth preset number of counting categories are the target counting categories.

9. The urine testing device according to claim 8, characterized in that: When selecting a fourth preset number of counting categories from the frequency information of the counting categories in a preset order, the processor obtains the first remaining counting categories whose frequency information does not exceed the preset frequency information in each counting category, and selects the fourth preset number of counting categories from the frequency information of the first remaining counting categories in a preset order; or, the processor obtains the second remaining counting categories whose counting categories do not include the preset counting category, and selects the fourth preset number of counting categories from the frequency information of the second remaining counting categories in a preset order.

10. The urine testing device according to claim 8, characterized in that: When determining that the target area corresponding to the target counting category is the tracking area, the processor is further configured to determine that a target area that satisfies a second preset condition among the target areas corresponding to a fourth preset number of the counting categories is the tracking area.

11. The urine testing device according to claim 10, characterized in that: When determining that the target area that meets the second preset condition among the target areas corresponding to the fourth preset number of counting categories is the tracking area, the processor obtains the tracking upper limit value when the image acquisition device shoots the tracking area, and accumulatively selects the target areas that do not exceed the tracking upper limit value from the target areas corresponding to the fourth preset number of counting categories in accordance with the preset order as the tracking area.

12. The urine testing device according to claim 1, characterized in that: The processor further determines whether the accumulated value is greater than the tracking upper limit value; when the accumulated value is greater than the tracking upper limit value, the processor calculates ratio information of the tracking upper limit value to the accumulated value.

13. The urine testing device according to claim 12, characterized in that: When determining the third preset number of tracking areas based on the ratio information and the normalized information corresponding to each target counting category, the processor determines the tracking number of the corresponding counting category according to the product of the ratio information and the normalized information corresponding to each target counting category, and randomly selects the tracking number of target areas corresponding to the target counting category from the second preset number as the tracking areas.

14. The urine testing device according to claim 12, characterized in that: After determining whether the accumulated value is greater than the tracking upper limit value, if the accumulated value is not greater than the tracking upper limit value, the processor determines the corresponding target areas in the second preset number as the tracking areas in descending order according to the frequency information corresponding to each target counting category, and the number of the determined target areas does not exceed the tracking upper limit value.

15. The urine testing device according to claim 5, characterized in that: When controlling the image acquisition device to photograph a third preset number of tracking areas through the high-power scope, the processor determines a target position of the counting pool relative to the high-power scope according to position information of the tracking areas; The processor is used to drive the counting pool to the target position, and control the image acquisition device to photograph the tracking area through the high-power lens.

16. A urine testing method, applied to a urine testing device, characterized in that: The urine detection device includes an image acquisition device, and the urine detection method includes: Controlling the image acquisition device to photograph the urine sample in the counting pool to obtain a first preset number of photographed images corresponding to the urine sample; Acquire counting information of tangible components contained in each of the second preset number of target areas from the first preset number of captured images; Based on the counting information of the tangible components included in each target area, a third preset number of target areas in the second preset number are determined as tracking areas, and different counting information corresponds to different counting categories; wherein frequency information of the counting category is determined, and the frequency information of the counting category is the ratio of the number of target areas of the counting category to the second preset number, and the third preset number of tracking areas is determined according to the frequency information of the counting category; Controlling the image acquisition device to photograph a third preset number of the tracking areas to obtain statistical information of formed components in the urine sample; The step of determining the third preset number of tracking areas according to the frequency information of the counting categories includes: controlling the normalization processing to be performed on the frequency information of each target counting category to obtain the normalized information corresponding to each target counting category; and determining the third preset number of tracking areas based on the normalized information corresponding to each target counting category; The method of determining the third preset number of tracking areas based on the normalized information corresponding to each target counting category includes: obtaining the tracking upper limit value when the image acquisition device shoots the tracking area; calculating the accumulated value of the normalized information corresponding to each target counting category; calculating the ratio information of the tracking upper limit value and the accumulated value; and determining the third preset number of tracking areas based on the ratio information and the normalized information corresponding to each target counting category.

17. The urine detection method according to claim 16, characterized in that: The step of acquiring the counting information of the tangible components contained in each of the second preset number of target areas from the first preset number of captured images includes: determining a second preset number of target areas located in the first preset number of captured images; Controlling the identification of the tangible components of the second preset number of target areas to obtain counting information of each target area.

18. The urine detection method according to claim 16, characterized in that: The urine testing device includes a microscope, and the microscope includes a low-power microscope and a high-power microscope. The image acquisition device is controlled to take a picture of the urine sample in the counting pool, including: Controlling the image acquisition device to photograph the urine sample through the low-power microscope; The controlling the image acquisition device to photograph a third preset number of the tracking areas comprises: The image acquisition device is controlled to photograph a third preset number of the tracking areas through the high-power lens.

19. The urine detection method according to claim 16, characterized in that: The determining a third preset number of tracking areas according to the frequency information of each counting category includes: Acquire a target counting category that meets a first preset condition in the frequency information of each counting category; A target area corresponding to the target counting category is determined as the tracking area.

20. The urine detection method according to claim 19, characterized in that: The step of obtaining the target counting category that meets the first preset condition in the frequency information of each counting category includes: Selecting a fourth preset number of counting categories from the frequency information of the counting categories in a preset order, wherein the preset order is a size order, and a sum of the frequency information corresponding to the fourth preset number of counting categories is not less than a preset threshold; A fourth preset number of the counting categories is determined as the target counting category.

21. The urine detection method according to claim 20, characterized in that: The selecting a fourth preset number of counting categories from the frequency information of the counting categories in a preset order includes: Obtain the first remaining counting category whose frequency information does not exceed the preset frequency information in each counting category; selecting a fourth preset number of counting categories from the frequency information of the first remaining counting categories in a preset order; or, Obtain the second remaining counting categories of each counting category that does not include the preset counting category; A fourth preset number of counting categories are selected from the frequency information of the second remaining counting categories in a preset order.

22. The urine detection method according to claim 19, characterized in that: The determining the target area corresponding to the target counting category as the tracking area includes: It is determined that a target area that meets a second preset condition among the target areas corresponding to the counting categories of a fourth preset number is the tracking area.

23. The urine detection method according to claim 22, characterized in that: The step of determining that the target area that meets the second preset condition among the target areas corresponding to the fourth preset number of counting categories is the tracking area comprises: Acquire a tracking upper limit value when the image acquisition device photographs the tracking area; Target areas not exceeding the tracking upper limit value are cumulatively selected from the target areas corresponding to the fourth preset number of the counting categories in accordance with the preset order as the tracking areas.

24. The urine detection method according to claim 16, characterized in that: Before calculating the ratio information of the tracking upper limit value to the accumulated value, the method further includes: Determining whether the accumulated value is greater than the tracking upper limit value; The calculating the ratio information of the tracking upper limit value and the accumulated value includes: When the accumulated value is greater than the tracking upper limit value, the step of calculating the ratio information of the tracking upper limit value and the accumulated value is performed.

25. The urine detection method according to claim 24, characterized in that: The determining a third preset number of tracking areas based on the ratio information and normalized information corresponding to each target counting category includes: Determining the tracking quantity of the corresponding counting category according to the product of the ratio information and the normalized information corresponding to each target counting category; The target areas of the tracking number corresponding to the target counting category are randomly selected from a second preset number as the tracking areas.

26. The urine detection method according to claim 24, characterized in that: After determining whether the accumulated value is greater than the tracking upper limit value, the method further comprises: When the accumulated value is not greater than the tracking upper limit, the target areas corresponding to the second preset number are determined as the tracking areas in descending order according to the frequency information corresponding to each target counting category, and the number of the determined target areas does not exceed the tracking upper limit.

27. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the urine detection method according to any one of claims 16 to 26 is implemented.

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