Microparticle measuring system and microparticle measuring method

Abstract

A microparticle measuring system (10) according to an embodiment comprises: a light source (11) for emitting illuminating light toward an imaging container in which a liquid containing microparticles to be measured has been placed; an objective lens (16) for condensing the illuminating light; an imaging lens (17) for forming an image of the condensed illuminating light; an image sensor (18) for capturing an image of the imaged illumination light and outputting the captured image; an adjustment mechanism for adjusting an imaging distance, which is the distance from the image sensor (18) to the imaging container; and a detecting unit (22) for detecting the microparticles on the basis of a plurality of the captured images having mutually different imaging distances.

Description

Microparticle measurement system and microparticle measurement method

[0001] FIELD Embodiments of the present invention relate to a microparticle measurement system and a microparticle measurement method.

[0002] Conventionally, organic wastewater treatment has utilized various beneficial microorganisms to decompose organic matter in wastewater and remove nitrogen and phosphorus. In this process, wastewater treatment is carried out based on indicators such as sludge concentration and treated water quality. Therefore, if the concentration of beneficial microorganisms that contribute to organic matter decomposition and nitrogen removal can be measured, stable control can be achieved, and improved wastewater treatment performance can be expected.

[0003] Techniques for measuring the concentration of beneficial microorganisms (microparticles such as Bacillus) include colony counting and PCR (Polymerase Chain Reaction) methods, but these methods have problems such as the need for highly specialized equipment, the time required to transport samples to specialized facilities, and the long measurement times.

[0004] Therefore, a technology has been proposed that utilizes the characteristics of light refraction by the microparticles being measured and detects microparticles (e.g., Bacillus spores) from captured images using image processing based on deep learning. This eliminates the need to transport samples to specialized facilities and enables microparticle concentration measurements to be performed in a short time.

[0005] Japanese Patent Application Laid-Open No. 2023-79806

[0006] However, with the above-mentioned conventional technology, there is a problem in that substances other than the microparticles being measured may have optical properties similar to those of the microparticles being measured under certain conditions, making it difficult to identify them using captured images and resulting in measurement errors.

[0007] Therefore, the present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide a microparticle measurement system and a microparticle measurement method that can improve the accuracy when measuring microparticles using captured images.

[0008] The microparticle measurement system of the embodiment includes a light source that emits illumination light toward a photographing container that contains a liquid containing the microparticles to be measured, an objective lens that focuses the illumination light, an imaging lens that forms an image of the focused illumination light, an image sensor that photographs the imaged illumination light and outputs the photographed image, an adjustment mechanism that adjusts the photographing distance, which is the distance from the image sensor to the photographing container, and a detection unit that detects the microparticles based on a plurality of photographed images that have different photographing distances.

[0009] FIG. 1 is a schematic diagram of a microparticle measurement system according to an embodiment. FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. FIG. 3 is an explanatory diagram of relative transmitted light intensity and the like for Bacillus spores. FIG. 4 is an explanatory diagram of relative transmitted light intensity and the like for acrylic particles. FIG. 5 is a diagram showing an example of a captured image of sludge. FIG. 6 is a diagram showing examples of captured images and teaching images used in deep learning. FIG. 7 is a diagram showing an example of a Bacillus detection result by deep learning. FIG. 8 is a diagram showing examples of captured images for each shooting distance. FIG. 9 is a diagram showing examples of captured images of bacteria in a spore state and bacteria not in a spore state. FIG. 10 is an explanatory diagram of a Bacillus spore detection process by deep learning. FIG. 11 is an explanatory diagram of a Bacillus spore detection process by pattern matching. FIG. 12 is a flowchart showing processing by the microparticle measurement system according to an embodiment.

[0010] Hereinafter, embodiments of a microparticle measurement system and a microparticle measurement method of the present invention will be described with reference to the drawings. Note that, hereinafter, Bacillus spores will also be simply referred to as bacillus.

[0011] 1 is a schematic diagram of a microparticle measurement system 10 according to an embodiment. The microparticle measurement system 10 includes a light source 11, a stage 13, a stage driver 14, a laser displacement meter 15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement controller 19, and an information processor 20. The measurement controller 19 and the information processor 20 may be integrated into one unit. Alternatively, the information processor 20 may be divided into two or more units.

[0012] The light source 11 emits illumination light L onto a slide glass 12 (preparation: photographing container) on which a measurement sample SP (liquid; specimen) containing microparticles to be measured is placed.

[0013] The stage 13 supports a slide glass 12 that holds the measurement sample SP. Note that a hemocytometer may be used instead of the slide glass 12.

[0014] The stage driver 14 moves the stage 13 along the optical axis in the vertical direction in Fig. 1. The stage driver 14 is an example of an adjustment mechanism that adjusts the imaging distance, which is the distance from the image sensor 18 to the glass slide 12. The laser displacement meter 15 detects the position of the glass slide 12 by laser.

[0015] The objective lens 16 condenses the illumination light L into parallel light, and the imaging lens 17 condenses the parallel illumination light L to form an image.

[0016] The image sensor 18 captures the illumination light imaged by the imaging lens 17 and outputs the captured image. The measurement control unit 19 controls the stage driving unit 14 and the image sensor 18.

[0017] The information processing device 20 includes an acquisition unit 21 , a detection unit 22 , a calculation unit 23 , a processing unit 24 , a storage unit 25 , a display unit 26 , and an input unit 27 .

[0018] The acquisition unit 21 acquires the captured image from the image sensor 18 .

[0019] The detection unit 22 detects minute particles, impurities, and the like that appear in the captured image.

[0020] For example, the stage driving unit 14 changes the shooting distance at regular intervals. In this case, the image sensor 18 captures the illumination light and outputs the captured image each time the shooting distance is changed at regular intervals by the stage driving unit 14. The detection unit 22 then detects microparticles based on the captured images captured at different shooting distances (details will be described later).

[0021] The detection unit 22 detects microparticles by image processing using deep learning, for example, based on a plurality of captured images taken at different shooting distances.

[0022] Furthermore, the detection unit 22 may detect microparticles by image processing using pattern matching based on a plurality of captured images taken at different shooting distances.

[0023] The calculation unit 23 calculates the concentration of microparticles and the like (hereinafter also referred to as the “measurement result”) from the detection result by the detection unit 22 .

[0024] The processing unit 24 executes various information processes, such as displaying the measurement results on the display unit 26.

[0025] The memory unit 25 stores the operating programs of each unit 21 to 24, various parameters, the captured images acquired by the acquisition unit 21, the detection results by the detection unit 22, the measurement results by the calculation unit 23, and the processing results by the processing unit 24.

[0026] The display unit 26 displays various information in response to instructions from the processing unit 24 .

[0027] The input unit 27 is a means for the user to input information, such as a keyboard, a mouse, or a touch panel.

[0028] All or part of the processing performed by the above-mentioned units 21 to 24 is executed by a single processor (control unit) based on, for example, an operation program and various parameters stored in the storage unit 25.

[0029] Next, the principle of measuring microparticles will be explained. When illumination light is irradiated from the back side of a microparticle in a liquid, the illumination light is focused at a position according to the particle diameter and refractive index of the microparticle due to the lens effect of the microparticle.

[0030] The transmitted light intensity increases as the light approaches the focusing position, reaches a maximum at the focusing position, and decreases again as the light moves away from the focusing position. In other words, the position where the transmitted light intensity is maximum is the focusing position. At this time, the focusing position can be identified by measuring the distance between the objective lens 16 and the position where the transmitted light intensity is maximum.

[0031] In this case, the optical path of the illumination light can be expressed by the following equation: Therefore, if the particle diameter of the microparticle is known in addition to the distance between the objective lens 16 and the position where the transmitted light intensity is maximum, the refractive index of the microparticle can be determined by solving the equation expressed by the ray tracing matrix below.

[0032]

[0033] 2 is an explanatory diagram of parameters in a ray tracing matrix. In the ray tracing matrix, the radius of the microparticle PC is r, the refractive index of the microparticle is n, and the distance between the microparticle and the objective lens 16 when the transmitted light intensity of the illumination light L is maximum for the target microparticle is z. Also, the distance from the optical axis when the illumination light L is incident on the microparticle is x. ０ The incident angle when the illumination light L is incident on the microparticle is u ０ The distance from the optical axis of the illumination light L incident on the image sensor 18 is defined as x １ and the incident angle of the illumination light L incident on the image sensor 18 is u １ Let's say.

[0034] Furthermore, the distance between the objective lens 16 and the imaging lens 17 is set to l １ and the distance between the imaging lens 17 and the image sensor 18 is l ２ The focal length of the objective lens is f １ and the focal length of the imaging lens 17 is f ２ Let's say.

[0035] As described above, if the refractive index of the microparticle is known, the particle diameter of the microparticle can be calculated by solving the equation expressed by the ray tracing matrix.

[0036] In addition, useful microorganisms used in organic wastewater treatment can be considered as microparticles under certain conditions, such as when the useful microorganisms form spores. When the useful microorganisms form spores, their shape does not change and the shape is almost constant for the useful microorganisms.

[0037] Since spores of useful microorganisms have a specific size (e.g., particle diameter) and a specific refractive index, by treating them in the same way as microparticles, it becomes possible to detect such useful microorganisms and measure their number per observation field (and thus their concentration).

[0038] When measuring the concentration, the observation position (image capture position) is scanned along the optical axis direction, and the concentration can be measured by measuring the number of useful microorganisms in a volume corresponding to the observation field of view x scanning distance.

[0039] Incidentally, for spores of Bacillus strains (Bacillus spores) in sludge with a known refractive index and a particle size of 1 μm or less, the position corresponding to the distance z at which the transmitted light intensity is maximized is the focal length f １ Therefore, based on a preset transmitted light intensity threshold, the area with a light intensity equal to or greater than the threshold can be regarded as a Bacillus spore.

[0040] In this case, the intensity of transmitted light through the liquid containing Bacillus spores is greater than the intensity of transmitted light through the liquid not containing Bacillus spores. Therefore, by setting the threshold value of the transmitted light intensity for determining whether or not Bacillus spores are present to a value slightly greater than the intensity of transmitted light through the liquid not containing spores, Bacillus spores can be reliably detected.

[0041] Furthermore, by using a set threshold value, the sample is continuously moved in the optical axis direction while sequentially capturing images, and by combining this with deep learning (machine learning) that uses the transmitted light intensity at each location (each pixel) obtained from the captured images and the size (particle diameter) of the Bacillus spores as microparticles as judgment criteria, it is possible to detect and measure the number of Bacillus spores, and ultimately to measure the concentration of Bacillus spores with high accuracy.

[0042] When performing deep learning, for example, multiple samples with different concentrations of microparticles are prepared in advance, and supervised learning is performed so that the manual detection results by the user (measurement worker) for each sample are equivalent to the detection results obtained by deep learning, thereby obtaining microparticle detection results that correspond to the particle diameter and refractive index of the microparticles being learned.

[0043] Next, Fig. 3 is an explanatory diagram of the relative transmitted light intensity for Bacillus spores, etc. In detail, Fig. 3 is a diagram illustrating the relationship between the difference in the actual position of the objective lens 16 relative to the distance z between the Bacillus spore and the objective lens 16 when the transmitted light intensity for the Bacillus spore is at its maximum, and the relative transmitted light intensity.

[0044] 4 is a diagram illustrating the relative transmitted light intensity for acrylic particles, etc. Specifically, this diagram illustrates the relationship between the relative transmitted light intensity and the difference in the actual position of the objective lens 16 relative to the distance z between the acrylic particle (microparticle) and the objective lens 16 when the transmitted light intensity is at its maximum for an acrylic particle having a particle diameter of 30 μm.

[0045] First, images of the Bacillus spores and acrylic particles at the focal position were acquired by the image sensor 18. Then, the stage 13 was moved up and down along the optical axis direction by the stage driving unit 14, and the position difference Δz between the position of the objective lens 16 when the relative transmitted light intensity of each microparticle on the image sensor 18 was at its maximum and the actual position of the objective lens 16 was measured by the laser displacement meter 15.

[0046] 3A shows a photographed image of a liquid containing Bacillus spores, in which the relative transmitted light intensity is at its maximum. As shown in FIG. 3A, it can be seen that the relative transmitted light intensity is at its maximum at the center of the photographed area. Furthermore, as shown in FIG. 3B, it was calculated that the relative transmitted light intensity is at its maximum when the position difference Δz is 0 μm in the liquid containing Bacillus spores.

[0047] In contrast, as shown in Figure 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity has a negative value at a position difference of Δ0 μm, where the relative transmitted light intensity for Bacillus spores is maximized. In other words, it can be seen that the transmitted light intensity is lower than the background light intensity. Furthermore, as shown in Figure 4(A), it can be seen that the relative transmitted light intensity is minimized around the acrylic particles. Furthermore, as shown in Figure 4(B), in a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity is maximized outside a position difference of Δz = ±15 μm.

[0048] 4C shows a captured image of a liquid containing acrylic particles with a particle diameter of 30 μm, where the relative transmitted light intensity is at its maximum. As shown in FIG. 4C, the relative transmitted light intensity is at its maximum at the center of the captured area. As shown in FIG. 4D, the relative transmitted light intensity was calculated to be at its maximum at a position difference Δz of 26 μm for a liquid containing acrylic particles with a particle diameter of 30 μm.

[0049] Based on these measurement results, the position difference Δz corresponding to the difference between the distance z from the microparticles Bacillus spores and acrylic particles with a particle diameter of 30 μm to the objective lens 16 when the transmitted light intensity is at its maximum and the focal length of the objective lens was calculated using the above-mentioned ray tracing matrix for Bacillus spores and acrylic particles with a particle diameter of 30 μm.The position difference Δz was found to be Δz = 0.9 μm in the liquid containing Bacillus spores and Δz = 22.5 μm in the liquid containing acrylic particles with a particle diameter of 30 μm, which was found to be almost consistent with the measurement results using the laser displacement meter 15. The distance between the objective lens 16 and the imaging lens 17 at this time was calculated as 1 １ = 130 mm, and the distance between the imaging lens 17 and the image sensor 18 is l ２ = 164.5 mm, the focal length of the objective lens is f １ = 4.1125 mm, and the focal length of the imaging lens 17 is f ２ The Bacillus spores had r = 1 μm and n = 1.4, and the acrylic particles had n = 1.5.

[0050] In particular, it was found that the position difference Δz = 0.9 μm in the liquid containing Bacillus spores is effectively equal to the focal length of the objective lens 16 (within the depth of field), and the relative transmitted light intensity is maximized at the focal position. This shows that in measuring Bacillus spores, it is possible to detect Bacillus spores by measuring the transmitted light intensity at the focal length.

[0051] In this way, the characteristic of Bacillus spores that the center glows brightly when the transmitted light intensity is at its maximum can be utilized to detect Bacillus spores from the captured image.

[0052] Next, Fig. 5 shows examples of photographed images of sludge. As shown in Fig. 5(a) and (b), in addition to Bacillus spores B, impurities C may also appear in the photographed images.

[0053] Next, Figure 6 shows examples of captured images and training images used in deep learning. (a) is a captured image showing Bacillus spores B and impurities C. A user provides the center position P of Bacillus spores B as correct answer data for this captured image, which is used as the training image shown in (b). Deep learning can be performed by training a network to detect the center position P of Bacillus spores B in the captured image using these images.

[0054] 7 is a diagram showing an example of a Bacillus detection result using deep learning. The detection unit 22 calculates the likelihood of the Bacillus spore detection result using image processing using deep learning (the possibility (probability) that each pixel is the center position of the Bacillus spore).

[0055] Fig. 7(a) shows an input image (photographed image). The detection unit 22 calculates, for example, a likelihood map as shown in Fig. 7(b). In this likelihood map, brighter areas indicate higher likelihood, and darker areas indicate lower likelihood. The symbol Q indicates a portion with high likelihood corresponding to Bacillus spore B (Fig. 7(a)).

[0056] The detection unit 22 then performs threshold processing on the likelihood and determines that the pixel with a likelihood equal to or greater than a certain level is the center position of the bacillus, thereby obtaining the detection result shown in Fig. 7(c), in which the symbol S indicates the center position of the detected bacillus spore.

[0057] However, when the above detection is performed using a single captured image as in conventional technology, bacteria that are not in a spore state (substances that are not the microparticles being measured) may have optical characteristics similar to those of bacteria in a spore state (microparticles being measured) under certain conditions, making it difficult to distinguish them using the captured image and resulting in measurement errors.

[0058] Therefore, in the following, taking such situations into consideration, a technology that can improve the accuracy when measuring microparticles using captured images will be described in detail.

[0059] 8A and 8B are diagrams showing examples of images captured at different shooting distances. In Fig. 8A, the focal position becomes deeper in the order (a4) → (a1), i.e., the shooting distance becomes shorter. The brightness of the Bacillus spores is approximately the same in (a3) ​​and (a4), while (a2) is darker than them and (a1) is even darker.

[0060] In Figure 8(b), the focus position becomes closer in the order of (b1) → (b4), i.e., the shooting distance becomes longer. The brightness of the Bacillus spores is almost the same in (b1) and (b2), but (b3) is darker than them, and (b4) is even darker.

[0061] Next, FIG. 9 shows examples of captured images of bacteria B (Bacillus spores) in a spore state and bacteria D in a non-spore state. As shown in (a), bacteria B and D are photographed at focus positions (1), (2), and (3). In this case, bacteria B and D appear differently, as shown in (b). Specifically, for bacteria B, the brightness is "dark," "light," and "dark" at focus positions (1), (2), and (3). On the other hand, for bacteria D, the brightness is "light" at focus positions (1), (2), and (3). In other words, when bacteria D has a shape resembling a vertically elongated sphere as shown in (a) and is positioned as shown, its optical characteristics are similar to those of bacteria B. Note that when bacteria D is rotated 90 degrees on the page, its optical characteristics are not similar to those of bacteria B.

[0062] Therefore, for example, if one attempts to detect spores using only the image captured at focus position (2), it is difficult to distinguish between spore-forming bacteria B and non-spore-forming bacteria D, resulting in reduced detection accuracy. However, if one attempts to detect spores using all of the images captured at focus positions (1), (2), and (3), the difference in how the bacteria appear at each focus position makes it easier to distinguish between spore-forming bacteria B and non-spore-forming bacteria D, resulting in higher detection accuracy. Therefore, by acquiring multiple images while shifting the focus position (photographing distance) at regular intervals, etc., and using these multiple images, it becomes possible to accurately identify whether the bacteria shown in the captured images are in spore form.

[0063] Next, FIG. 10 is an explanatory diagram of the Bacillus spore detection process using deep learning. (a) shows examples of captured images and training images used when training a deep learning model. (a1) shows multiple captured images of sludge taken while moving the focus position at regular intervals, with each captured image having a different focus position. The user provides the center position of the Bacillus spore as correct answer data for this captured image, which is used as the training image shown in (a2). A deep learning model can be generated by training a network to detect the center position of the Bacillus spore from the captured image using these captured images.

[0064] (b) is a diagram showing an example of Bacillus detection processing using deep learning. When multiple images (b1) captured while moving the focus position at regular intervals are used as input data for deep learning, a likelihood map (b2) of the Bacillus spore detection result is calculated. This likelihood is subjected to threshold processing, and pixels with a likelihood above a certain level are determined to be the center position of the Bacillus spore, resulting in a Bacillus spore detection result (b3).

[0065] As described above, in addition to deep learning, bacillus spore detection processing can also be performed by pattern matching. Figure 11 is an explanatory diagram of bacillus spore detection processing using pattern matching. (a) shows multiple images captured while moving the focus position at regular intervals when photographing sludge, with each captured image having a different focus position. (b) shows multiple images captured separately while moving the focus position at regular intervals, with representative images of bacillus spores at each focus position used as template images. The similarity between the input image (a) and the template image (b) is calculated, and coordinates with a similarity level above a certain level are designated as the bacillus spore detection result (c).

[0066] 12 is a flowchart showing processing by the microparticle measurement system 10 of this embodiment. The measurement operator collects water from a water treatment device containing the microorganism (bacillus) to be measured, performs predetermined pretreatment (filtering, heating, etc.) on the collected sample, and sets the pretreated sample in the microparticle measurement system 10. Next, the measurement operator adjusts the height of the stage 13 of the microparticle measurement system 10 to its initial position. The initial height position of the stage 13 is, for example, a height at which the focal position is aligned with the bottom of the stage 13.

[0067] Generally, the specific gravity of the microorganisms (bacillus) in the measurement sample SP is greater than 1. Therefore, for example, after preparing the measurement sample SP, the microorganisms (bacillus) are allowed to settle and become stable, and then the following steps S1 and subsequent steps are carried out.

[0068] In step S1 , the acquisition unit 21 acquires a captured image from the image sensor 18 and stores it in the storage unit 25 .

[0069] Next, in step S2, the processing unit 24 determines whether the number of times of imaging has reached a predetermined number (for example, 20 times), and if Yes, proceeds to step S4, and if No, proceeds to step S3.

[0070] In step S3, the stage driving unit 14 changes the height of the stage 13 at regular intervals in response to an instruction from the processing unit 24, and the process returns to step S1. The height of the stage 13 may be changed manually by a measurement operator. The regular interval at which the stage 13 is moved may be, for example, about 1.0 μm. This length is an example of a length appropriate for distinguishing between spore-state Bacillus bacteria (microparticles with a diameter of approximately 3.4 μm) and non-spore-state bacteria, but is not limited to this. This length may be empirically obtained, for example, through experiments.

[0071] In step S4, the detection unit 22 detects Bacillus spores appearing in the captured images based on a plurality of captured images taken at different shooting distances (FIGS. 10 and 11).

[0072] Next, in step S5, the calculation unit 23 calculates the number of detected Bacillus spores and the concentration from the detection result in step S4.

[0073] Next, in step S6, the processing unit 24 causes the display unit 26 to display the measurement results.

[0074] In this way, according to the microparticle measuring system 10 of this embodiment, by using a plurality of captured images taken at different shooting distances, it is possible to improve the accuracy of measurements relating to microparticles.

[0075] Furthermore, if the intervals at which the photographing distance is changed are constant, the photographing distance can be easily changed.

[0076] Furthermore, if the microparticle measuring system 10 has a configuration in which a stage driving unit 14 for adjusting the height of the stage 13 is provided, the imaging distance can be easily changed by controlling the stage driving unit 14 .

[0077] In addition, if the microparticle measurement system 10 is configured to include an imaging device drive unit (adjustment mechanism) that adjusts the shooting distance by moving the objective lens 16, the imaging lens 17, and the image sensor 18 together, the shooting distance can be easily changed by controlling the imaging device drive unit.

[0078] Furthermore, by detecting microparticles through image processing using deep learning based on multiple captured images taken at different shooting distances, higher detection accuracy can be achieved.

[0079] Furthermore, by detecting microparticles through image processing using pattern matching based on a plurality of captured images taken at different shooting distances, it is possible to achieve a certain level of detection accuracy while shortening the calculation time.

[0080] In addition, the information processing device 20 of this embodiment is equipped with a control device such as a CPU (Central Processing Unit), a storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), an external storage device such as an HDD (Hard Disk Drive), a display device such as a display device, and input devices such as a keyboard and a mouse, and has a hardware configuration that utilizes a normal computer.

[0081] In addition, the program executed by the information processing device 20 of this embodiment is provided as a file in an installable or executable format, recorded on a computer-readable recording medium such as a semiconductor storage device such as a DVD (Digital Versatile Disk), a USB (Universal Serial Bus) memory, or an SSD (Solid State Drive).

[0082] The program may also be configured to be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network, or to be provided or distributed via a network such as the Internet, or to be provided by being pre-installed in a ROM or the like.

[0083] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims.

[0084] For example, the predetermined number used as the threshold for the number of times of photographing in step S2 of Fig. 12 is not limited to 20 times, and may be another number. In other words, the threshold for the number of times of photographing and the interval at which the photographing distance is changed can be set to any appropriate value based on experiments, simulations, etc.

[0085] 10...microparticle measurement system, 11...light source, 12...slide glass, 13...stage, 14...stage driving unit, 15...laser displacement meter, 16...objective lens, 17...imaging lens, 18...image sensor, 19...measurement control unit, 20...information processing device, 21...acquisition unit, 22...detection unit, 23...calculation unit, 24...processing unit, 25...storage unit, 26...display unit, 27...input unit

Claims

1. A microparticle measuring system comprising: a light source that emits illumination light to a photographing container in which a liquid containing microparticles to be measured is placed; an objective lens that collects the illumination light; an imaging lens that forms an image of the collected illumination light; an image sensor that captures the image of the illuminated light and outputs a captured image; an adjustment mechanism that adjusts a shooting distance, which is the distance from the image sensor to the photographing container; and a detection unit that detects the microparticles based on a plurality of captured images in which the shooting distances are different from one another.

2. The microparticle measuring system of claim 1, wherein the adjustment mechanism changes the shooting distance at regular intervals, and the image sensor captures the illumination light and outputs the captured image each time the shooting distance is changed at the regular intervals by the adjustment mechanism.

3. The microparticle measuring system according to claim 1, wherein the adjustment mechanism is a stage drive unit that adjusts the height of a stage on which the imaging container is placed.

4. The microparticle measuring system according to claim 1, wherein the adjustment mechanism is an imaging device drive section that adjusts the shooting distance by moving the objective lens, the imaging lens and the image sensor together.

5. The microparticle measuring system according to claim 1, wherein the detection unit detects the microparticles by image processing using deep learning based on a plurality of the captured images having different shooting distances.

6. The microparticle measuring system according to claim 1, wherein the detection section detects the microparticles by image processing using pattern matching based on a plurality of the captured images having different shooting distances.

7. A microparticle measuring method using a microparticle measuring system including a light source that emits illumination light to a photographing container in which a liquid containing microparticles to be measured is placed, an objective lens that collects the illumination light, an imaging lens that forms an image of the collected illumination light, an image sensor that photographs the imaged illumination light and outputs a photographed image, an adjustment mechanism, and a detection unit, the microparticle measuring method including: a step in which the adjustment mechanism adjusts a photographing distance that is the distance from the image sensor to the photographing container; and a step in which the detection unit detects the microparticles based on a plurality of photographed images in which the photographing distances are different from one another.

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