Fine particle measurement system and fine particle measurement method

Abstract

To easily perform highly accurate measurement related to fine particles using a photographed image.SOLUTION: A microparticle measurement system according to an embodiment includes: a light source configured to emit illumination light to a photographing container on which a liquid containing microparticles to be measured is placed; an objective lens configured to condense the illumination light; an image forming lens configured to form an image of the condensed illumination light; an image sensor configured to capture the illumination light to output a captured image; an adjustment mechanism configured to adjust a photographing distance which is a distance from the image sensor to the photographing container; a calculation unit configured to detect the microparticles for each of a plurality of the captured images having different photographing distances and calculate a predetermined focus index value correlated with a degree of focus on the detected microparticles; and a detection unit configured to detect the microparticles based on one of the plurality of the captured images selected according to the focus index value.SELECTED DRAWING: Figure 14

Description

[Technical Field]

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

[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), but these methods require highly specialized equipment, and have problems such as the time required to transport samples to specialized facilities and the long measurement times.

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

[0005] [Patent Document 1] Japanese Patent Publication No. 2023-79806 Summary of the Invention [Problem to be solved by the invention]

[0006] In the above-mentioned conventional technology, the appearance of microparticles in a captured image varies depending on the focal position of the camera. For example, it is possible to detect microparticles using multiple captured images taken at multiple focal positions, but this takes time for the capture and detection processes. Therefore, it is preferable to be able to detect microparticles from a single captured image taken at a single focal position.

[0007] Furthermore, when detecting microparticles from a single image captured at one focus position, it is preferable to input the image captured at the focus position where the transmitted light intensity of the microparticle is high (preferably maximum). This is because, when the focus position of the microparticle is adjusted to the just-in-focus position (a position where the focus is sufficiently correct), the transmitted light intensity is high compared to other objects that are not microparticles, which is an inherent characteristic of microparticles.

[0008] However, with conventional technology, workers have to judge whether the image is captured at the exact focus position by looking at how the microparticles appear in the captured image, which places a burden on the workers and is not easy.

[0009] 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 easily perform high-precision measurements of microparticles using captured images. [Means for solving the problem]

[0010] The microparticle measurement system of the embodiment includes a light source that emits illumination light toward 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 the photographed image, an adjustment mechanism that adjusts the photographing distance, which is the distance from the image sensor to the photographing container, a calculation unit that detects the microparticles for each of a plurality of photographed images with different photographing distances and calculates a predetermined focus index value that is correlated with the degree to which the detected microparticles are in focus, and a detection unit that detects the microparticles based on one of the photographed images with different photographing distances selected in accordance with the focus index value. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram of a microparticle measuring system according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram of parameters in a ray tracing matrix. [Figure 3] FIG. 3 is an explanatory diagram of the relative transmitted light intensity and the like for Bacillus spores. [Figure 4] FIG. 4 is an explanatory diagram of the relative transmitted light intensity and the like for acrylic particles. [Figure 5] FIG. 5 is a diagram showing an example of a photographed image of sludge. [Figure 6] FIG. 6 is a diagram showing examples of captured images at different shooting distances. [Figure 7] FIG. 7 shows examples of images of Bacillus spores captured at each focus position. [Figure 8] FIG. 8 is an explanatory diagram of a method for calculating the focus index value. [Figure 9] FIG. 9 is a graph showing an example of the calculation result of the focus index value. [Figure 10] FIG. 10 is a diagram showing examples of captured images and teaching images used in deep learning. [Figure 11] FIG. 11 shows an example of the results of Bacillus detection using deep learning. [Figure 12] FIG. 12 is a diagram schematically illustrating a neural network used in deep learning. [Figure 13] FIG. 13 is an explanatory diagram of the process of detecting Bacillus spores by pattern matching. [Figure 14] FIG. 14 is a flowchart showing the processing performed by the microparticle measuring system of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] 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 be taken as an example of microparticles. Bacillus spores will also be simply referred to as bacillus.

[0013] 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.

[0014] 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.

[0015] The stage 13 supports a slide glass 12 that holds a measurement sample SP. Note that a hemocytometer may be used instead of the slide glass 12.

[0016] 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 shooting distance, which is the distance from the image sensor 18 to the glass slide 12, by changing the height of the stage on which the glass slide 12 is placed. The laser displacement meter 15 detects the position of the glass slide 12 with a laser.

[0017] The objective lens 16 condenses the illumination light L into parallel light. The imaging lens 17 condenses the illumination light L, which has become parallel light, to form an image.

[0018] The image sensor 18 captures the illumination light imaged by the imaging lens 17 and outputs the captured image. For example, the image sensor 18 captures the illumination light and outputs the captured image every time the shooting distance is changed by the stage driving unit 14. The measurement control unit 19 controls the stage driving unit 14 and the image sensor 18 .

[0019] The information processing device 20 includes an acquisition unit 21, a calculation unit 22, a detection unit 23, a processing unit 24, a storage unit 25, a display unit 26, and an input unit 27. First, an overview of the functions of each unit 21 to 27 will be described, and details will be provided later.

[0020] The acquisition unit 21 acquires the captured image from the image sensor 18 .

[0021] The calculation unit 22 executes various calculation processes. For example, the calculation unit 22 detects microparticles for each of a plurality of captured images captured at different shooting distances, and calculates a predetermined focus index value that correlates with the degree to which the detected microparticles are in focus. Specifically, the calculation unit 22 calculates the focus index value based on the brightness difference between the detected microparticle and its surroundings in the captured image (described later with reference to FIG. 8).

[0022] The detection unit 23 executes various detection processes. The detection unit 23 detects microparticles, impurities, and the like that appear in the captured image. For example, the detection unit 23 detects microparticles based on one captured image selected according to the focus index value from among a plurality of captured images captured at different shooting distances.

[0023] Furthermore, at least one of the calculation unit 22 and the detection unit 23 may detect microparticles by image processing using deep learning based on the captured image.

[0024] Furthermore, at least one of the calculation unit 22 and the detection unit 23 may detect microparticles by image processing using pattern matching based on the captured image.

[0025] Furthermore, the calculation unit 22 may detect microparticles using a detection method that is faster than the detection method of microparticles used by the detection unit 23 .

[0026] Furthermore, when the calculation unit 22 detects multiple microparticles for each of multiple captured images taken at different shooting distances and calculates focus index values ​​for each microparticle, the detection unit 23 may detect the microparticle based on one captured image selected from the multiple captured images taken at different shooting distances according to either the sum, average, or median of the multiple focus index values.

[0027] Furthermore, the detection unit 23 obtains the concentration of microparticles and the like (hereinafter also referred to as "measurement results") from the detection results.

[0028] The processing unit 24 performs various information processing operations, such as displaying the measurement results on the display unit 26.

[0029] 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 calculation results by the calculation unit 22, the detection results (measurement results) by the detection unit 23, the processing results by the processing unit 24, etc.

[0030] The display unit 26 displays various information in response to instructions from the processing unit 24 .

[0031] The input unit 27 is a means for the user to input information, such as a keyboard, a mouse, or a touch panel.

[0032] All or part of the processing performed by the above-mentioned units 21 to 24 is executed by one processor (control unit) based on an operation program and various parameters stored in the storage unit 25, for example.

[0033] 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.

[0034] The transmitted light intensity increases as the focus position is approached, reaches a maximum at the focus position, and decreases again as the focus position is moved away. In other words, the position where the transmitted light intensity is maximum is the focus position. At this time, the focus position can be identified by measuring the distance between the objective lens 16 and the position where the transmitted light intensity is maximum.

[0035] 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.

[0036]

number

[0037] 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 for the target microparticle is maximum is z. Also, the distance from the optical axis when the illumination light L is incident on the microparticle is x0, and the incident angle when the illumination light L is incident on the microparticle is u0. Also, the distance from the optical axis when the illumination light L is incident on the image sensor 18 is x1, and the incident angle of the illumination light L when it is incident on the image sensor 18 is u1.

[0038] Furthermore, the distance between the objective lens 16 and the imaging lens 17 is defined as l1, and the distance between the imaging lens 17 and the image sensor 18 is defined as l2. The focal length of the objective lens is defined as f1, and the focal length of the imaging lens 17 is defined as f2.

[0039] 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.

[0040] 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.

[0041] Since spores of beneficial 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 beneficial microorganisms and measure their number per observation field (and thus their concentration).

[0042] When measuring the concentration, the observation position (image capture position) is scanned along the optical axis, and the concentration can be measured by measuring the number of beneficial microorganisms in a volume corresponding to the observation field of view x scanning distance.

[0043] Incidentally, for spores of Bacillus strains (Bacillus spores) in sludge with a known refractive index and particle size of 1 μm or less, it is known that the position corresponding to the distance z at which the transmitted light intensity is at its maximum is located within the depth of field (effective focal position) corresponding to the focal length f1 in image acquisition. Therefore, based on a preset transmitted light intensity threshold, parts with light intensity above the threshold can be considered Bacillus spores.

[0044] In this case, the intensity of light transmitted through the liquid containing the Bacillus spores is greater than the intensity of light transmitted through the liquid not containing the Bacillus spores. Therefore, by setting the threshold value of the transmitted light intensity for determining whether or not a liquid contains Bacillus spores to a value slightly higher than the transmitted light intensity in a liquid that does not contain spores, Bacillus spores can be reliably detected.

[0045] 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.

[0046] 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.

[0047] 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 relative transmitted light intensity and the difference in the actual position of objective lens 16 relative to the distance z between the Bacillus spore and objective lens 16 when the transmitted light intensity for the Bacillus spore is at its maximum.

[0048] Fig. 4 is an explanatory diagram of the relative transmitted light intensity for acrylic particles, etc. In detail, Fig. 4 is a diagram illustrating 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.

[0049] First, images of the Bacillus spores and acrylic particles were acquired at the focal position 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.

[0050] Figure 3(A) shows an image of a liquid containing Bacillus spores when the relative transmitted light intensity is at its maximum. As shown in Figure 3(A), it can be seen that the relative transmitted light intensity is at its maximum at the center of the imaged area. Furthermore, as shown in Figure 3(B), 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.

[0051] 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 for Bacillus spores is maximum at a position difference of Δ0 μm, and the relative transmitted light intensity is negative. In other words, the transmitted light intensity is lower than the background light intensity. Furthermore, as shown in Figure 4(A), the relative transmitted light intensity is minimum around the acrylic particles. Furthermore, as shown in Figure 4(B), in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was calculated to be maximum outside a position difference of Δz = ±15 μm.

[0052] Figure 4(C) shows an image captured when the relative transmitted light intensity was at its maximum in a liquid containing acrylic particles with a particle diameter of 30 μm. As shown in Figure 4(C), it can be seen that the relative transmitted light intensity was at its maximum at the center of the captured area. As shown in Figure 4(D), it was calculated that the relative transmitted light intensity was at its maximum at a position difference Δz of 26 μm in a liquid containing acrylic particles with a particle diameter of 30 μm.

[0053] Based on these measurement results, the position difference Δz corresponding to the difference between the distance z from the microparticles, i.e., the bacillus spores and the 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 ray tracing matrix described above for the bacillus spores and the acrylic particles with a particle diameter of 30 μm. The position difference Δz was Δz = 0.9 μm in the liquid containing the bacillus spores, and Δz = 22.5 μm in the liquid containing the 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. In this case, the distance between the objective lens 16 and the imaging lens 17 was l1 = 130 mm, the distance between the imaging lens 17 and the image sensor 18 was l2 = 164.5 mm, the focal length of the objective lens was f1 = 4.1125 mm, and the focal length of the imaging lens 17 was f2 = 164.5 mm. In addition, the Bacillus spores had r = 1 μm and n = 1.4, and the acrylic particles had n = 1.5.

[0054] 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 maximum at the focal position. This indicates that in measuring Bacillus spores, it is possible to detect Bacillus spores by measuring the intensity of transmitted light at the focal length.

[0055] 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.

[0056] 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.

[0057] Figure 6 shows examples of images captured at different shooting distances. In Figure 6(a), the focal position moves further back in the order (a4) → (a1), i.e., the shooting distance decreases. The brightness of the Bacillus spores is almost the same in (a3) ​​and (a4), while (a2) is darker than them and (a1) is even darker.

[0058] In Figure 6(b), the focus position moves closer in the order of (b1) → (b4), i.e., the shooting distance increases. 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.

[0059] Next, Figure 7 shows examples of images of Bacillus spores captured at each focus position. As shown in (a), Bacillus spores are photographed at focus positions (1), (2), and (3). The photographed images are as shown in (b), and the transmitted light intensity of the Bacillus spores is greatest at focus position (2). Therefore, it is preferable to detect Bacillus spores using an image captured at an in-focus position such as focus position (2).

[0060] FIG. 8 is an explanatory diagram of a method for calculating the focus index value. FIG. 8 is a diagram showing a local region including the bacillus spore, centered on the bacillus spore, extracted from a captured image. The pixel value P1 is the maximum pixel value within the local region, i.e., the pixel value of the brightest part of the bacillus spore. The pixel value P2 is the minimum pixel value within the local region, i.e., the pixel value of the bacillus spore and its surrounding darkest part. The focus index value can be calculated, for example, by calculating P1 / P2.

[0061] Alternatively, the focus index value may be calculated as follows. Explaining this with reference to Figure 8, first, in a local region of a captured image as shown in Figure 8, for example, the pixel value of the central coordinate is set to pixel value P3 of a bright portion of the Bacillus spore. Furthermore, the pixel value of the coordinate of a portion spaced a predetermined distance (for example, 10 pixels) from the central coordinate is set to pixel value P4 of the Bacillus spore and its surrounding dark portion. In this case, the focus index value can be calculated, for example, by calculating P3 / P4.

[0062] 9 is a graph showing an example of the calculation results of the focus index value. Fig. 9 shows the results of calculating the focus index value for images captured while shifting the focus. As can be seen from the results of (a), (b), and (c), the higher the focus index value, the brighter the central portion of the Bacillus spores, making the captured image more suitable for use in detecting Bacillus spores.

[0063] Next, FIG. 10 shows examples of captured images and training images used in deep learning. (a) is a captured image showing Bacillus spores B and impurities C. The 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 using these images to train a network (neural network) to detect the center position P of Bacillus spores B in the captured image. After creating a trained model through deep learning, when detecting Bacillus spores using a new captured image and the trained model, detection accuracy can be improved by using a captured image with a high focus index value as the captured image.

[0064] 11 is a diagram showing an example of a Bacillus detection result obtained by deep learning. The detection unit 23 calculates the likelihood of the Bacillus spore detection result obtained by image processing using deep learning (the possibility (likelihood) that each pixel is the center position of the Bacillus spore).

[0065] Fig. 11(a) is an input image (photographed image). The detection unit 23 calculates, for example, a likelihood map as shown in Fig. 11(b). In this likelihood map, the brighter the area, the higher the likelihood, and the darker the area, the lower the likelihood. The symbol Q indicates a portion with high likelihood corresponding to Bacillus spore B (Fig. 11(a)).

[0066] The detection unit 23 then performs threshold processing on this 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 Figure 11(c). In Figure 11(c), the symbol S indicates the center position of the detected bacillus spore. In this way, bacillus spores can be detected using a deep learning technique.

[0067] Figure 12 is a diagram that shows a schematic of a neural network used in deep learning. A neural network consists of an input layer, a hidden layer, and an output layer. The input layer receives input data. The hidden layer inherits data from the input layer and performs various calculations. Generally, the more hidden layers there are, the more accurate the analysis can be, but the longer the processing time. The output layer outputs data.

[0068] Here, specific methods for making the detection method of Bacillus spores by calculation unit 22 faster than the detection method of Bacillus spores by detection unit 23 include, for example, methods 1 and 2 below.

[0069] (Method 1) Bacillus spores are detected using a deep learning technique in both the calculation unit 22 and the detection unit 23. In this case, the number of intermediate layers of the neural network used in the calculation unit 22 is set to be smaller than the number of intermediate layers of the neural network used in the detection unit 23.

[0070] (Method 2) If the pattern matching method for detecting bacillus spores is faster than the deep learning method for detecting bacillus spores, the calculation unit 22 detects bacillus spores using the pattern matching method, and the detection unit 23 detects bacillus spores using the deep learning method.

[0071] However, these methods 1 and 2 are merely examples, and the present invention is not limited to these. Below, a method for detecting Bacillus spores using pattern matching will be described.

[0072] As mentioned above, in addition to deep learning, bacillus spore detection processing can also be performed using pattern matching. Figure 13 is an explanatory diagram of bacillus spore detection processing using pattern matching. (a) is an input image (photographed image). (b) is multiple template images. The similarity between the input image (a) and the template image (b) is calculated, and coordinates with a similarity above a certain level are determined as the bacillus spore detection result (c).

[0073] 14 is a flowchart showing processing by the microparticle measurement system 10 of the embodiment. The measurement operator collects water from a water treatment device containing the microorganism (bacillus) to be measured, performs predetermined pretreatment (filter treatment, heat treatment, etc.) on the collected sample, and sets the sample after pretreatment 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.

[0074] Generally, the specific gravity of the microorganisms (bacillus) in the measurement sample SP is greater than 1, so for example, after preparing the measurement sample SP, wait 10 to 20 minutes for the microorganisms (bacillus) to settle and become stable, then perform the processing from step S1 onwards.

[0075] In step S1 , the acquisition unit 21 acquires a captured image from the image sensor 18 and stores it in the storage unit 25 .

[0076] Next, in step S2, the calculation unit 22 detects Bacillus spores from the captured image. This detection process is executed every time a captured image is acquired (real-time processing), so it is preferable to implement it using an algorithm (e.g., pattern matching) that prioritizes speed over accuracy.

[0077] Next, in step S3, the calculation unit 22 calculates a focus index value from the detected bacillus and stores it in the storage unit 25.

[0078] Next, in step S4, processing unit 24 displays the focus index value calculated in step S3 on display unit 26. Here, as a method for displaying the focus index value, for example, the latest focus index value may be displayed in real time. Alternatively, time-series data of the focus index values ​​calculated up to that point may be displayed in a graph.

[0079] Next, in step S5, 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 S7, and if No, proceeds to step S6. Note that this predetermined number is not limited to 20 times and may be another number.

[0080] In step S6, the stage driving unit 14 changes the height of the stage 13 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 interval at which the stage 13 is moved may be, for example, approximately 1.0 μm. This length is an example of a length that takes into account that the diameter of Bacillus bacteria in a spore state is approximately 3.4 μm, but is not limited to this. This length can be obtained empirically, for example, through experiments. The interval at which the stage 13 is moved may or may not be equal.

[0081] In step S7, the detection unit 23 detects Bacillus spores using the captured image with the highest (largest) focus index value among the multiple captured images stored in the storage unit 25. This detection process is highly related to the accuracy of the finally calculated Bacillus concentration, and therefore it is preferable to implement it using an algorithm (e.g., deep learning) that prioritizes accuracy over speed.

[0082] Next, in step S8, detection unit 23 calculates the number of detected Bacillus spores and the concentration from the detection result in step S7.

[0083] Next, in step S9, the processing unit 24 causes the display unit 26 to display the measurement results.

[0084] In this way, according to the microparticle measurement system 10 of this embodiment, the focus index value is calculated for multiple captured images taken at different shooting distances, and Bacillus spores are detected based on one captured image selected according to the focus index value, thereby making it possible to easily perform high-precision measurements.

[0085] In other words, since the detection process can be performed using a captured image in which the Bacillus spores are in focus, it is possible to avoid performing the detection process using an out-of-focus captured image and prevent a decrease in detection accuracy.

[0086] Furthermore, even if the camera (image sensor 18) or microscope used does not have an autofocus function, operations and processes equivalent to autofocus can be executed.

[0087] Furthermore, the Bacillus spores are characterized by the fact that the intensity of transmitted light increases as they become more in focus, and by calculating the focus index value based on the difference in brightness between the Bacillus spores and their surroundings in the captured image, it is possible to accurately achieve focus, making it possible to adjust the focus even on objects that appear too small to be distinguished by the human eye.

[0088] 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 .

[0089] 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, imaging lens 17, and image sensor 18 together, the shooting distance can be easily changed by controlling the imaging device drive unit.

[0090] In addition, higher detection accuracy can be achieved by detecting Bacillus spores using image processing based on deep learning.

[0091] Furthermore, by detecting microparticles through image processing using pattern matching, it is possible to achieve a certain level of detection accuracy while shortening the calculation time.

[0092] Furthermore, when multiple microparticles are detected in a captured image and focus index values ​​for each microparticle are calculated, the detection unit 23 detects the microparticles based on one captured image selected from multiple captured images taken at different shooting distances according to the sum, average, or median of the multiple focus index values.Whether to use the sum, average, or median can be selected based on the user's convenience through experiments, etc., taking into consideration detection accuracy, calculation time, etc.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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. [Explanation of symbols]

[0097] 10...microparticle measurement system, 11...light source, 12...slide glass, 13...stage, 14...stage drive 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...calculation unit, 23...detection unit, 24...processing unit, 25...storage unit, 26...display unit, 27...input unit

Claims

1. a light source that emits illumination light toward a container in which a liquid containing microparticles to be measured is placed; an objective lens that condenses the illumination light; an imaging lens that forms an image from the condensed illumination light; an image sensor that captures the formed image of the illumination light and outputs a captured image; an adjustment mechanism for adjusting a photographing distance, which is a distance from the image sensor to the photographing container; a calculation unit that detects the microparticles for each of the plurality of captured images at different shooting distances and calculates a predetermined focus index value that is correlated with the degree to which the detected microparticles are in focus; a detection unit that detects the microparticles based on one of the captured images selected in accordance with the focus index value from among the plurality of captured images having different shooting distances; A microparticle measurement system comprising:

2. The microparticle measuring system according to claim 1 , wherein the image sensor captures the illumination light and outputs the captured image each time the photographing distance is changed by the adjustment mechanism.

3. The microparticle measuring system according to claim 1 , wherein the calculation unit calculates the focus index value based on a difference in brightness between the detected microparticle and its surroundings in the captured image.

4. 2. The microparticle measuring system according to claim 1, wherein the adjustment mechanism is a stage driving unit that adjusts the photographing distance by changing the height of a stage on which the photographing container is placed.

5. 2. The microparticle measuring system according to claim 1, wherein the adjustment mechanism is an imaging device drive unit that adjusts the imaging distance by integrally moving the objective lens, the imaging lens, and the image sensor.

6. The microparticle measuring system according to claim 1 , wherein at least one of the calculation unit and the detection unit detects the microparticles by image processing using deep learning based on the captured image.

7. 2. The microparticle measuring system according to claim 1, wherein at least one of the calculation unit and the detection unit detects the microparticles by image processing using pattern matching based on the captured image.

8. The microparticle measuring system according to claim 1 , wherein the calculation section detects the microparticles using a detection method that is faster than a detection method used by the detection section.

9. When the calculation unit detects a plurality of the microparticles for each of the plurality of captured images having different shooting distances and calculates the focus index value for each of the microparticles, 2. The microparticle measurement system according to claim 1, wherein the detection unit detects the microparticles based on one of the captured images selected from the captured images having different shooting distances according to a sum, an average, or a median of the focus index values.

10. A microparticle measurement method using a microparticle measurement 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 condenses the illumination light, an imaging lens that forms an image of the condensed illumination light, an image sensor that photographs the image of the condensed illumination light and outputs a photographed image, an adjustment mechanism, a calculation unit, and a detection unit, The adjustment mechanism adjusts a photographing distance, which is a distance from the image sensor to the photographing container; a calculation step in which the calculation unit detects the microparticles for each of the plurality of captured images having different shooting distances, and calculates a predetermined focus index value that is correlated with a degree of focus on the detected microparticles; a step in which the detection unit detects the microparticles based on one of the captured images selected according to the focus index value from among the plurality of captured images having different shooting distances; A method for measuring fine particles, comprising:

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