WO2023181438A1

WO2023181438A1 - Particle measuring system, and particle measuring method

Info

Publication number: WO2023181438A1
Application number: PCT/JP2022/028718
Authority: WO
Inventors: 杜朗鳥居; 周平野田; 徳介早見; 建至柿沼; 勇太橋本; 錦陽胡; 理映子水内
Original assignee: 株式会社東芝; 東芝インフラシステムズ株式会社
Priority date: 2022-03-24
Filing date: 2022-07-26
Publication date: 2023-09-28
Also published as: JP2023142064A

Abstract

A particle measuring system (10) according to an embodiment comprises: a light source (11) for emitting illuminating light onto a liquid containing a particle to be measured; an objective lens (16) for condensing the illuminating light; an imaging lens (17) for imaging the condensed illuminating light; an image sensor (18) for capturing an image of the imaged illuminating light and outputting a captured image; a detecting unit (22) for detecting the particle appearing in the captured image; a calculating unit (23) for calculating a particle detection reliability on the basis of a detection result from the detecting unit (22) and a predetermined indicator; and a processing unit (24) for determining next processing on the basis of the reliability.

Description

Particle measurement system and particle measurement method

Embodiments of the present invention relate to a particle measurement system and a particle measurement method.

Conventionally, in organic wastewater treatment, various useful microorganisms have been used to decompose organic matter and remove nitrogen and phosphorus from wastewater. At that time, wastewater treatment is performed based on indicators such as sludge concentration and treated water quality, but it would be meaningful if the concentration of useful microorganisms that contribute to organic matter decomposition and nitrogen removal could be measured.

Conventional techniques for measuring the concentration of useful microorganisms (particles such as Bacillus) include, for example, the colony counting method and the PCR (Polymerase Chain Reaction) method. However, measurement using these methods requires highly specialized equipment, requires time and effort to transport the specimen to specialized facilities, and requires a long measurement time.

Therefore, a technology has been proposed that uses the characteristics of light refraction by particles to be measured to detect particles (for example, Bacillus spores) from captured images through image processing using deep learning. This eliminates the need to transport the specimen to a specialized facility and enables particle concentration measurement in a short time. Note that useful microorganisms such as Bacillus are sometimes called microparticles.

Japanese Patent Application Publication No. 2016-147044

However, the deep learning method described above has a problem in that the measurement accuracy is not constant. For example, the concentration of Bacillus in sludge is not uniform but has shades, and there are variations in the number (concentration) of Bacillus appearing in the captured image. For this reason, for example, if the number of bacilli detected per captured image is small, the accuracy of the measurement result will be low if the number of captured images is small. Furthermore, it is not always easy for the operator to determine whether or not there are a small number of captured images.

Therefore, the present invention has been made in view of the above circumstances, and an object thereof is to provide a particle measurement system and a particle measurement method that can improve the accuracy when measuring particles using captured images. do.

The particle measurement system of the embodiment includes a light source that emits illumination light to a liquid containing particles to be measured, an objective lens that focuses the illumination light, and an imager that forms an image of the focused illumination light. a lens, an image sensor that captures the formed illumination light and outputs a captured image, a detection unit that detects the particles appearing in the captured image, and a detection result by the detection unit and a predetermined index. and a processing section that determines the next process based on the reliability.

FIG. 1 is a schematic configuration diagram of a particle measurement system according to an embodiment. FIG. 2 is an explanatory diagram of parameters in the ray tracing matrix. FIG. 3 is an explanatory diagram of relative transmitted light intensity, etc. for Bacillus spores. FIG. 4 is an explanatory diagram of relative transmitted light intensity, etc. for acrylic particles. FIG. 5 is a diagram showing an example of a captured image of sludge. FIG. 6 is a diagram showing an example of a captured image and a teaching image used in deep learning. FIG. 7 is a diagram showing an example of Bacillus detection results by deep learning. FIG. 8 is a diagram showing an example of a reference table for reliability calculation. FIG. 9 is a flowchart showing processing by the particle measurement system of the embodiment.

Hereinafter, embodiments of the particle measurement system and particle measurement method of the present invention will be described with reference to the drawings. In addition, below, Bacillus spores are also simply called Bacillus.

FIG. 1 is a schematic configuration diagram of a particle measurement system 10 according to an embodiment. The particle measurement system 10 includes a light source 11, a stage 13, a stage drive unit 14, a laser displacement meter 15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement control unit 19, and an information processing unit. A device 20 is provided. Note that the measurement control section 19 and the information processing device 20 may be configured integrally. Further, the information processing device 20 may be divided into two or more parts.

The light source 11 emits illumination light L to a measurement sample SP (liquid, specimen) containing (micro) particles to be measured.

The stage 13 supports a slide glass (preparation) 12 that holds the measurement sample SP.

The stage drive unit 14 moves the stage 13 in the vertical direction in FIG. 1 along the optical axis.
The laser displacement meter 15 detects the position of the slide glass 12 using a laser.

The objective lens 16 condenses the illumination light L into parallel light.
The imaging lens 17 condenses the parallel illumination light L and forms an image.

The image sensor 18 captures the illumination light formed by the imaging lens 17 and outputs a captured image.
The measurement control section 19 controls the stage drive section 14 and the image sensor 18.

The information processing device 20 includes an acquisition section 21, a detection section 22, a calculation section 23, a processing section 24, a storage section 25, and a display section 26.

The acquisition unit 21 acquires a captured image from the image sensor 18.

The detection unit 22 detects particles, contaminants, etc. appearing in the captured image.

The calculation unit 23 calculates the reliability of particle detection based on the detection result by the detection unit 22 and a predetermined index (details will be described later).

The processing unit 24 executes various information processing. For example, the processing unit 24 determines the next process based on the reliability. For example, when the reliability is less than a predetermined threshold, the processing unit 24 causes the display unit 26 to display a screen requesting the user (worker) to take a predetermined action to improve the reliability. For example, the processing unit 24 requests the user to obtain additional captured images when the reliability is less than a predetermined threshold.

Furthermore, for example, the processing unit 24 may automatically acquire an additional captured image when the reliability is less than a predetermined threshold.

The storage unit 25 stores operating programs of each unit 21 to 24, various parameters, captured images acquired by the acquisition unit 21, detection results by the detection unit 22, calculation results such as reliability by the calculation unit 23, and the processing unit. 24 is stored.

The display unit 26 displays various information according to instructions from the processing unit 24.

Note that all or part of the processing performed by each of the units 21 to 24 described above may be executed by one processor (control unit) based on the operating program and various parameters stored in the storage unit 25. .

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

Note that the intensity of the transmitted light increases as it approaches the condensing position, and the intensity of the transmitted light reaches its maximum at the condensing position. Then, by moving away from the condensing position again, the transmitted light intensity decreases. That is, the position where the transmitted light intensity is maximum is the light condensing position. At this time, the light condensing position can be specified by measuring the distance between the objective lens 16 and the position where the transmitted light intensity is maximum.

In this case, the optical path of the illumination light can be expressed by the following formula, so if the particle diameter of the particles 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 particle can be found by solving the equation expressed by the ray tracing matrix below.

FIG. 2 is an explanatory diagram of parameters in the ray tracing matrix. In the above ray tracing matrix, the radius of the particle PC is r, the refractive index of the particle is n, and the distance between the particle and the objective lens 16 when the transmitted light intensity of the illumination light L is maximum in the target particle is Let it be z. Further, the distance from the optical axis when the illumination light L is incident on the particle is x ₀ , and the angle of incidence when the illumination light L is incident on the particle is u ₀ . Further, the distance from the optical axis of the illumination light L that has entered the image sensor 18 is x ₁ , and the angle of incidence of the illumination light L that has entered the image sensor 18 is u ₁ .

Further, the distance between the objective lens 16 and the imaging lens 17 is l ₁ , and the distance between the imaging lens 17 and the image sensor 18 is l ₂ . Further, the focal length of the objective lens is f ₁ and the focal length of the imaging lens 17 is f ₂ .

As described above, if the refractive index of the particles is known, the diameter of the particles can be calculated by solving the equation expressed by the ray tracing matrix.

Additionally, useful microorganisms used in organic wastewater treatment can be considered particles depending on the conditions. The condition is, for example, when useful microorganisms form spores. This is because when spores are formed, the shape etc. do not change and the shape is also almost constant depending on the useful microorganism.

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 particles, it is possible to detect such useful microorganisms and reduce the number of spores per observation field ( In turn, it becomes possible to measure the concentration.

When measuring the concentration, the number of useful microorganisms in the volume corresponding to the observation field x scanning distance is measured by scanning the observation position (image capture position) along the optical axis direction. measurement becomes possible.

By the way, for spores of a Bacillus strain in sludge (Bacillus spores) with a known refractive index of light and a particle size of 1 μm or less, the position corresponding to the distance z where the transmitted light intensity is maximum is determined in image acquisition. is known to be located within the depth of field (effective focal position) corresponding to the focal length f ₁ of . Therefore, based on a preset transmitted light intensity threshold, a portion having a light intensity equal to or higher than the threshold can be regarded as a Bacillus spore.

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

Furthermore, using the set threshold, images were taken sequentially while moving the sample in the optical axis direction, and the transmitted light intensity and Bacillus spores as particles were obtained for each location (each pixel) from the captured images. By combining deep learning (machine learning) that uses the size (particle diameter) as a criterion, it becomes possible to detect and measure the number of Bacillus spores, and by extension, to measure the concentration of Bacillus spores with high accuracy.

When performing deep learning, for example, multiple samples with different particle concentrations are prepared in advance, and supervised learning is performed so that the manual detection result of the user (operator) is equal to the detection result by deep learning for each sample. Then, a particle detection result obtained according to the particle diameter and refractive index of the particle to be learned may be obtained.

Next, FIG. 3 is an explanatory diagram of relative transmitted light intensity, etc. for Bacillus spores. Specifically, FIG. 3 shows the difference in the actual position of the objective lens 16 with respect to the distance z between the Bacillus spores and the objective lens 16 when the transmitted light intensity for the Bacillus spores is maximum, the relative transmitted light intensity, FIG.

Further, FIG. 4 is an explanatory diagram of relative transmitted light intensity, etc. for acrylic particles. More specifically, FIG. 4 shows the actual position of the objective lens 16 with respect to the distance z between the acrylic particles (particles) and the objective lens 16 when the transmitted light intensity is maximum for acrylic particles with a particle diameter of 30 μm. It is a figure explaining the relationship between a difference and relative transmitted light intensity.

First, images of Bacillus spores and acrylic particles at the focal position were acquired by the image sensor 18.
After that, the stage 13 is moved up and down along the optical axis direction by the stage drive unit 14, and the position of the objective lens 16 when the relative transmitted light intensity of each particle becomes maximum on the image sensor 18 and the actual position are determined. The positional difference Δz from the position of the objective lens 16 was measured by the laser displacement meter 15.

FIG. 3(A) is a captured image when the relative transmitted light intensity reaches the maximum in a liquid containing Bacillus spores. As shown in FIG. 3A, it can be seen that the relative transmitted light intensity is maximum at the center of the imaging region. As shown in FIG. 3(B), it was calculated that in the liquid containing Bacillus spores, the relative transmitted light intensity was maximized at a positional difference Δz=0 μm.

On the other hand, as shown in Fig. 4(B), in the case of a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity is It has a negative value. That is, it can be seen that the transmitted light intensity is lower than the background light intensity. Moreover, as shown in FIG. 4(A), it can be seen that the relative transmitted light intensity is minimum around the acrylic particles. As shown in FIG. 4(B), in a liquid containing acrylic particles with a particle diameter of 30 μm, it was calculated that the relative transmitted light intensity becomes maximum outside the positional difference Δz=±15 μm.

Further, FIG. 4(C) is a captured image when the relative transmitted light intensity reaches the maximum in a liquid containing acrylic particles with a particle diameter of 30 μm. As shown in FIG. 4C, it can be seen that the relative transmitted light intensity is maximum at the center of the imaging region. As shown in FIG. 4(D), in a liquid containing acrylic particles with a particle diameter of 30 μm, the relative transmitted light intensity was calculated to be maximum at a position difference Δz=26 μm.

Based on this measurement result, using the above-mentioned ray tracing matrix, the objective lens 16 When we calculated the positional difference Δz, which corresponds to the difference between the distance z and the focal length of the objective lens, we found that the positional difference Δz in the liquid containing Bacillus spores was 0.9 μm, and the position in the liquid containing acrylic particles with a particle size of 30 μm. It was found that the difference Δz was 22.5 μm, which was almost the same as the measurement result using the laser displacement meter 15. At this time, the distance between the objective lens 16 and the imaging lens 17 is l ₁ =130 mm, the distance between the imaging lens 17 and the image sensor 18 is l ₂ =164.5 mm, and the focal length of the objective lens is f ₁ =4. The focal length of the imaging lens 17 was f ₂ =164.5 mm. Moreover, r=1 μm and n=1.4 for Bacillus spores, and n=1.5 for acrylic particles.

In particular, the positional 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. Understood.
That is, in measuring Bacillus spores, it was concluded that Bacillus spores can be detected by measuring the intensity of transmitted light at a focal length.

In this way, only Bacillus spores are detected from the captured image by utilizing the characteristic of Bacillus spores that the center shines brightly when the transmitted light intensity is maximum.

Next, FIG. 5 is a diagram showing an example of a captured image of sludge. As shown in FIGS. 5A and 5B, in addition to Bacillus spores B, contaminants C may also be seen in the captured image.

Next, FIG. 6 is a diagram showing an example of a captured image and a teaching image used in deep learning. (a) is a captured image showing Bacillus spores B and contaminants C. The user gives the center position P of the Bacillus spore B as correct data to this captured image, resulting in a teaching image shown in (b). Using these images, deep learning can be performed by training the network to detect the center position P of Bacillus spore B in the captured image.

Next, FIG. 7 is a diagram showing an example of Bacillus detection results by deep learning. The detection unit 22 calculates the likelihood (probability (likelihood) that each pixel is the center position of a Bacillus spore) of the detection result of Bacillus spores by image processing using deep learning.

FIG. 7(a) is an input image (captured image). The detection unit 22 calculates, for example, a likelihood map shown in FIG. 7(b). This likelihood map shows that the brighter the map, the higher the likelihood, and the darker the map, the lower the likelihood. The symbol Q indicates a portion where the likelihood is high corresponding to Bacillus spore B (FIG. 7(a)).

Then, the detection unit 22 performs threshold processing on this likelihood and sets a pixel with a likelihood above a certain value as the center position of the bacillus, thereby obtaining the detection result shown in FIG. 7(c). In FIG. 7(c), the symbol S indicates the center position of the detected Bacillus spore.

Next, the reliability threshold will be explained in detail. The reliability threshold is set based on, for example, the number of images required to measure the dominant concentration of Bacillus, the minimum concentration that the particle measurement system 10 can measure, the measurement error of the particle measurement system 10, and the like.

Further, for the reliability threshold, for example, a value preset in the particle measurement system 10 may be used, or a different value may be set for each site where the particle measurement system 10 is introduced.

Specifically, the reliability threshold is set to, for example, 1.0 for each of the following examples of reliability indicators. In that case, the measurement work is performed so that the reliability becomes 1.0 or more. Furthermore, if it is desired to further increase the reliability of the concentration measurement results, the threshold value may be set to a value greater than 1.0. Conversely, if the reliability does not need to be high, the threshold value may be set smaller than 1.0.

Hereinafter, examples of reliability indicators will be explained.

(The reliability index is the number of particles)
When the reliability index is the number of particles, the calculation unit 23 calculates the reliability based on the number of detected particles.

For example, it is assumed that if L Bacillus spores are measured, the measured concentration and the actual concentration will statistically match. At this time, the reliability is calculated using the following formula.

The biometric method (standard counting method) has the idea that if approximately 30 target organisms are measured, the measured concentration will statistically match the actual concentration. Therefore, for example, the variable L can be set to 30.

Note that in that case, the reliability may be calculated using the reference table shown in FIG. 8 instead of the above-mentioned formula. However, the variable L=30 is an example, and the range of the number of Bacilli and the reliability value can be set arbitrarily.

(The reliability index is the number of captured images)
When the reliability index is the number of captured images, the calculation unit 23 calculates the reliability based on the number of captured images.

In organic wastewater treatment, the concentration at which Bacillus becomes dominant is 10 ⁵ [cells/ml] or higher. The reliability is calculated from the number of captured images required to measure this density.

For example, when the Bacillus concentration is 10 ⁵ [cells/ml], if ``m Bacilli appear per captured image'', in order to ``measure L organisms to be measured'', it is necessary to An image is required. Therefore, the reliability is calculated using the following formula.

Since the reliability is calculated based on the number of images, it is easy for the operator to understand. That is, for example, if the reliability does not meet the standard, it is sufficient to simply increase the number of captured images.

Furthermore, if the minimum concentration that the particle measurement system 10 can measure (or that it wants to measure) is determined, the reliability can be determined in the same way.

For example, when the minimum concentration that the particle measurement system 10 can measure is 10 ³ [particles/ml], if "n Bacilli appear per captured image", in order to "measure L organisms to be measured", requires L/n images. Therefore, the reliability is calculated using the following formula.

(The reliability index is the likelihood of Bacillus spore detection results)
When the reliability index is the likelihood of the detection result of Bacillus spores, the calculation unit 23 calculates the reliability based on the likelihood of the detection result of Bacillus spores by image processing using deep learning.

For example, when the reliability standard is "the average value of the likelihood of Bacillus detection results is r or more", the reliability is calculated using the following formula.

(The reliability index is the detection result of foreign matter)
When the reliability index is a detection result of a contaminant other than Bacillus spores, the calculation unit 23 calculates the reliability based on the detection result of the contaminant.

For example, when the reliability standard is "the number of pixels occupied by contaminants other than Bacillus is s or less", the reliability is calculated using the following formula.

Furthermore, in addition to the number of pixels occupied by foreign objects, the reliability may be calculated using the size and number of foreign objects.

Next, FIG. 9 is a flowchart showing processing by the particle measurement system 10 of the embodiment. First, the work before this process will be explained.

First, the measurement operator samples water from the water treatment equipment where the microorganism (bacillus) to be measured is present. Perform predetermined pretreatment (filter treatment, heat treatment, etc.) on the sampled water sample. The pretreated sample is set in the particle measurement system 10.

Next, in step S1 in FIG. 9, the acquisition unit 21 acquires a captured image from the image sensor 18.

Next, in step S2, the detection unit 22 detects Bacillus spores appearing in the captured image.

Next, in step S3, the calculation unit 23 calculates the reliability of particle detection based on the detection result in step S2 and a predetermined index.

Next, in step S4, the processing unit 24 determines whether the reliability calculated in step S3 is greater than or equal to the threshold value, and if Yes, the process ends, and if No, the process proceeds to Step S5.

In step S5, the processing unit 24 causes the display unit 26 to display a screen requesting the user to take a predetermined action to improve reliability. For example, the processing unit 24 requests the user to obtain additional captured images. The user, looking at this display, shifts the slide glass 12 to take an image with the image sensor 18, or replaces the slide glass 12 and takes an image with the image sensor 18, in order to increase the reliability.

Other operations performed by the user include, for example, filter processing to remove impurities, heat treatment to sporeify viable Bacillus bacteria, and injection of an activator.

In addition, if the reliability does not exceed the threshold even after performing operations to increase the reliability a certain number of times, the measurement result will be marked as ``concentration below the measurement lower limit'' or ``unmeasurable,'' and the measurement will be terminated. It's okay.

For example, if the configuration of the particle measurement system 10 is different and the specimen is directly imaged without using the slide glass 12, it is also possible to shake the specimen to change its condition and then perform imaging with the image sensor 18. good.

Further, for example, if the configuration of the particle measurement system 10 is different and it is possible to automatically acquire an additional new captured image, the processing unit 24 automatically acquires an additional new captured image. You can do it like this. In that case, for example, it is assumed that a device for automatically shifting the slide glass 12 is provided.

After the predetermined operation requested in step S5 is completed, the process from step S1 onwards is executed again.

In this way, according to the particle measurement system 10 of the present embodiment, the accuracy of particle measurement can be improved by calculating the reliability of particle detection and determining the next process based on the reliability. . In other words, by requesting a worker to perform a specific task when reliability is low, even a worker without specialized knowledge can easily perform an appropriate task.

Note that, although the case where the particle measurement system 10 is configured as a stand-alone system has been described above, the present invention is not limited to this. For example, the particle measurement system 10 acquires a captured image by the image sensor 18 on the local terminal side, transfers the captured image to a cloud server via a communication interface and a communication network, and processes it by the information processing device 20 on the cloud server side. The processing results may be displayed on a local terminal.

In addition, the particle measurement system 10 of the present embodiment includes a control device such as a CPU (Central Processing Unit), a storage device such as a ROM (Read Only Memory) or a RAM (Random Access Memory), an HDD (Hard Disk Drive), etc. It is equipped with an external storage device, a display device such as a display device, and an input device such as a keyboard and mouse, and has a hardware configuration that uses a normal computer.

Further, the program executed by the particle measurement system 10 of this embodiment is a file in an installable format or an executable format, and can be installed on a DVD (Digital Versatile Disk), USB (Universal Serial Bus) memory, SSD (Solid State Drive). ) and other computer-readable recording media such as semiconductor storage devices.

Alternatively, the program may be stored on a computer connected to a network such as the Internet, and provided by being downloaded via the network. Further, the program may be provided or distributed via a network such as the Internet.

Furthermore, the program may be configured to be provided by being pre-installed in a ROM or the like.

DESCRIPTION OF SYMBOLS 10... Particle measurement system, 11... Light source, 12... Slide glass, 13... Stage, 14... Stage drive part, 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

Claims

a light source that emits illumination light to a liquid containing particles to be measured;
an objective lens that focuses the illumination light;
an imaging lens that forms an image of the condensed illumination light;
an image sensor that captures the imaged illumination light and outputs a captured image;
a detection unit that detects the particles appearing in the captured image;
a calculation unit that calculates reliability of particle detection based on the detection result by the detection unit and a predetermined index;
a processing unit that determines the next process based on the reliability;
A particle measurement system equipped with
The particle measurement system according to claim 1, wherein the processing unit causes a display unit to display a screen requesting a user to perform a predetermined operation to improve the reliability when the reliability is less than a predetermined threshold. .
The index is the number of particles,
The particle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the number of the detected particles.
The index is the number of captured images,
The particle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the number of captured images.
The particles are Bacillus spores whose refractive index of light in sludge is known,
The index is the likelihood of the detection result of the Bacillus spores,
The particle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on a likelihood of a detection result of the Bacillus spores obtained by image processing using deep learning using the captured image.
The particles are Bacillus spores whose refractive index of light in sludge is known,
The indicator is a detection result of impurities other than the Bacillus spores,
The detection unit detects the foreign matter appearing in the captured image,
The particle measurement system according to claim 1, wherein the calculation unit calculates the reliability based on the detection result of the contaminants.
The particle measurement system according to claim 1, wherein the processing unit requests the user to acquire the additional captured image when the reliability is less than a predetermined threshold.
The particle measurement system according to claim 1, wherein the processing unit automatically acquires the additional captured image when the reliability is less than a predetermined threshold.
a light source that emits illumination light to a liquid containing particles to be measured; an objective lens that condenses the illumination light; an imaging lens that forms an image of the condensed illumination light; A particle measurement method using a particle measurement system comprising an image sensor that captures illumination light and outputs a captured image, a detection unit, a calculation unit, and a processing unit,
a detection step in which the detection unit detects the particles appearing in the captured image;
a calculation step in which the calculation unit calculates reliability of particle detection based on the detection result from the detection step and a predetermined index;
A particle measuring method including a processing step in which the processing unit determines a next process based on the reliability.