Microparticle measurement system and microparticle measurement method

Abstract

A microparticle measuring system includes a light source, an objective lens, an imaging lens, an image sensor, and an information processing unit. The light source emits illumination light to liquid including a microparticle to be measured. The objective lens condenses the illumination light. The imaging lens forms an image of the illumination light. The image sensor captures the image of the illumination light and outputs a captured image. The information processing unit detects the microparticle appearing in the captured image, calculates detection accuracy of microparticle detection based on the detected microparticle and a predetermined index, and determines next processing based on the calculated detection accuracy of microparticle and preset target accuracy.

Description

MICROPARTICLE MEASUREMENT SYSTEM AND MICROPARTICLEMEASUREMENT METHODFIELDEmbodiments of the present invention relate generally to a microparticlemeasurement system and a microparticle measurement method.BACKGROUNDConventionally, in organic wastewater treatment, organic matter decompositionin wastewater, removal of nitrogen and phosphorus, and the like are performed usingvarious useful microorganisms. At that time, wastewater treatment is performed basedon indices such as sludge density and treated water quality. Therefore, it is meaningfulif the density of useful microorganisms that contribute to organic matter decomposition,nitrogen removal, and the like can be measured.As a technique for measuring the density of useful microorganisms(microparticles such as Bacillus), for example, a pour plate method and a polymerasechain reaction (PCR) method are known. However, the measurement in these methodsrequires highly specialized equipment, and there is a problem in that it takes effort totransport the specimen to a specialized facility, and further the measurement time islong.Therefore, a technology for detecting microparticles (for example, Bacillusspores) from a captured image by image processing using deep learning usingcharacteristics of refraction of light by microparticles to be measured has beenproposed.As a result, transport of the specimen to the specialized facility becomesunnecessary, and the density of the microparticles can be measured in a short time.However, the above-described deep learning method has a problem in that themeasurement accuracy is not constant. For example, the density of Bacillus in thesludge is not uniform but varies, and, the density, the number of Bacillus spores,appearing in the captured image varies. Therefore, for example, in a case where thenumber of Bacillus spores detected per captured image is small, the accuracy of themeasurement result becomes low if the number of captured images is small. However,it is not always easy for the operator to determine whether the number of capturedimages is small.The present invention has been made in view of the above circumstances, and anobject of the present invention is to provide a microparticle measurement system and amicroparticle measurement method capable of improving accuracy in a case wheremeasurement on microparticles is performed using captured images.SUMMARYA microparticle measuring system according to one embodiment includes a lightsource, an objective lens, an imaging lens, an image sensor, and an informationprocessing unit. The light source is configured to emit illumination light to liquidincluding a microparticle to be measured. The objective lens is configured to condensethe illumination light. The imaging lens is configured to form an image of theillumination light condensed by the objective lens. The image sensor is configured tocapture the image of the illumination light formed by the imaging lens and output acaptured image. The information processing unit is configured to detect themicroparticle appearing in the captured image, calculate detection accuracy ofmicroparticle based on the detected microparticle and a predetermined index, anddetermine next processing based on the calculated detection accuracy of microparticleand preset target accuracy.BRIEF DESCRIPTION OF THE DRAWINGSFIG. 1 is a schematic configuration diagram of a microparticle measurementsystem according to an embodiment;FIG. 2 is an explanatory diagram of parameters in a ray transfer matrix;FIGS. 3A and 3B are explanatory views of relative transmitted light intensity ofa Bacillus spore;FIGS. 4A to 4D are explanatory views of relative transmitted light intensity ofan acrylic particle;FIG. 5 is a view showing an example of captured images of sludge;FIGS. 6A and 6B are views showing an example of a captured image and ateaching image used in deep learning;FIG. 7 is a diagram illustrating an example of a Bacillus detection result by deeplearning;FIG. 8 is a diagram illustrating an example of a data table relative to the numberof detected Bacillus spores and a 95% confidence interval; andFIG. 9 is a flowchart illustrating processing by the microparticle measurementsystem of the embodiment.DETAILED DESCRIPTIONHereinafter, embodiments of a microparticle measurement system and amicroparticle measurement method of the present invention will be described withreference to the drawings. Hereinafter, a Bacillus spore may be simply referred to asBacillus.FIG. 1 is a schematic configuration diagram of a microparticle measurementsystem 10 according to an embodiment. The microparticle measurement system 10includes a light source 11, a stage 13, a stage drive unit 14, a laser displacement meter15, an objective lens 16, an imaging lens 17, an image sensor 18, a measurement controlunit 19, and an information processing apparatus 20. Note that the measurement controlunit 19 and the information processing apparatus 20 may be integrally configured.Moreover, the information processing apparatus 20 may be divided into two or more.The light source 11 emits illumination light L to a measurement sample SP(liquid specimen) including microparticles to be measured.The stage 13 supports a slide glass (preparation) 12 that holds the measurementsample SP. Instead of the slide glass, a hemocytometer may be used.The stage drive unit 14 moves the stage 13 in the vertical direction in FIG. 1along the optical axis. The laser displacement meter 15 detects the position of the slideglass 12 by a laser.The objective lens 16 condenses the illumination light L into parallel light. Theimaging lens 17 condenses the illumination light L that has become parallel light toform an image.The image sensor 18 captures an image of the illumination light formed by theimaging lens 17 and outputs a captured image. The measurement control unit 19controls the stage drive unit 14 and the image sensor 18.The information processing apparatus (information processing unit) 20 includesan acquisition unit 21, a detection unit 22, a calculation unit 23, a processing unit 24, astorage unit 25, a display unit 26, and an input unit 27.The acquisition unit 21 acquires a captured image from the image sensor 18.The detection unit 22 detects microparticles, contaminants, and the likeappearing in the captured image.The calculation unit 23 calculates the detection accuracy of microparticle basedon the detection result by the detection unit 22 and a predetermined index. In oneexample, the index is the number of microparticles.The processing unit 24 executes various types of information processing. Forexample, the processing unit 24 determines the next processing (or subsequentprocessing) based on the detection accuracy of microparticle and preset target accuracythat is the target accuracy requested by the user (operator).In a case where the detection accuracy of microparticle is less than the targetaccuracy, the processing unit 24 determines and executes one of the following processes(1) to (5) as the next processing.(1) Process of displaying, on the display unit 26, information or a message forrequesting the user to perform a predetermined operation to improve detection accuracyof microparticle(2) Process of displaying information or a message requesting the user to acquirean additional captured image on the display unit 26(3) Process of automatically acquiring additional captured image(4) Process of displaying detection accuracy of microparticle on the display unit(5) Process of displaying, on the display unit 26, the time required forcompleting measurement, which is estimated from the number of microparticles and thetotal number of captured imagesThe storage unit 25 stores an operation program of the units 21 to 24, variousparameters, the captured images acquired by the acquisition unit 21, the detection resultby the detection unit 22, the calculation result such as accuracy by the calculation unit23, the processing result by the processing unit 24, and the like.The display unit 26 is a display device such as a liquid-crystal display (LCD).The display unit 26 displays various types of information in accordance with aninstruction from the processing unit 24.The input unit 27 is information input means by the user. The input unit 27 mayinclude a keyboard, a mouse, a touch panel, or the like.Note that all or part of the processing performed in the units 21 to 24 describedabove may be executed by a processor (control unit) in accordance with an operationprogram and various parameters stored in the storage unit 25.Next, the principle of microparticle measurement will be described. In a casewhere illumination light is emitted to the microparticle in a liquid, the illumination lightis condensed at a position corresponding to the particle diameter and the refractiveindex of the microparticle due to the lens effect of the microparticle.The transmitted light intensity increases toward the condensing position, thetransmitted light intensity is maximized at the condensing position, and the transmittedlight intensity decreases away from the condensing position. In short, the positionwhere the transmitted light intensity is maximized is the condensing position. At thistime, the condensing position can be specified by measuring the distance between theobjective lens 16 and the position where the transmitted light intensity is maximized.In this case, the optical path of the illumination light can be expressed by thefollowing equation. Therefore, if the particle diameter of the microparticle is known inaddition to the distance between the objective lens 16 and the position where thetransmitted light intensity is maximized, the refractive index of the microparticle can befound by solving an equation represented by a ray transfer matrix presented below.a ant b, Ng anf Nn = 0) x0.(u,) = ( lat i) Dt-2 JC ) ee 1) Ge)nxTFIG. 2 is an explanatory diagram of parameters in the ray transfer matrix. In theray transfer matrix, r denotes the radius of the microparticle PC, n denotes the refractiveindex of the microparticle, and z denotes the distance between the microparticle and theobjective lens 16 when the transmitted light intensity of the illumination light L ismaximized in the target microparticle. In addition, xo denotes the distance from theoptical axis when the illumination light L is incident on the microparticle, and uodenotes an incident angle when the illumination light L is incident on the microparticle.In addition, x1 denotes the distance from the optical axis of the illumination light Lincident on the image sensor 18, and u; denotes the incident angle of the illuminationlight L incident on the image sensor 18.Moreover, the distance between the objective lens 16 and the imaging lens 17 isdenoted by 11, and the distance between the imaging lens 17 and the image sensor 18 isdenoted by lz. In addition, the focal length of the objective lens is fi, and the focallength of the imaging lens 17 is fo.Then, as described above, if the refractive index of the microparticle is known,the particle diameter of the microparticle can be calculated by solving the equationrepresented by the ray transfer matrix.In addition, useful microorganisms used in organic wastewater treatment can beregarded as microparticles depending on conditions. The condition is, for example, acase where the useful microorganism forms spores. This is because when spores areformed, the shape and the like do not change, and the shape is also substantiallyconstant depending on useful microorganisms.Spores of useful microorganisms have a unique size (for example, particlediameter) and a unique refractive index. Therefore, it is possible to detect such usefulmicroorganisms and measure the density, the number of such useful microorganisms perobservation field, by handling them in the same manner as microparticles.In the case of measuring the density, the density can be measured by scanningthe observation position (image capturing position) along the optical axis direction tomeasure the number of useful microorganisms in the volume corresponding to(observation field) x (scanning distance).By the way, in the spores (Bacillus spores) of the Bacillus strain in sludgehaving a known refractive index of light and a particle size of 1 um or less, it is knownthat the position corresponding to the distance z at which the transmitted light intensityis maximized is located within the depth of field (effective focal position) correspondingto the focal length f; in image acquisition. For this reason, a portion having a lightintensity equal to or higher than a preset transmitted light intensity threshold based onthe threshold can be regarded as Bacillus spores.In this case, the transmitted light intensity of liquid containing Bacillus spores islarger than the transmitted light intensity of liquid not containing Bacillus spores.Therefore, it is possible to reliably detect Bacillus spores by setting the threshold of thetransmitted light intensity, for determining whether Bacillus spores are contained or not,to a value slightly larger than the transmitted light intensity in a liquid containing nospores.Moreover, by sequentially capturing images while continuously moving thesample in the optical axis direction using the set threshold, and combining deep learning(machine learning) using the transmitted light intensity for each location (each pixel)obtained from the captured image and the size (particle diameter) of a Bacillus sporebeing a microparticle as determination criteria, the detection of Bacillus spores and themeasurement of the number of the Bacillus spores, and the measurement of the densityof the Bacillus spores can be performed with high accuracy.In the case of performing deep learning, for example, multiple samples areadjusted in advance to have different density of microparticles, supervised learning isperformed so that a detection result manually obtained by a user (operator) can be equalto a detection result obtained by deep learning for each sample, and a detection result ofmicroparticles obtained according to a particle diameter and a refractive index ofmicroparticles to be learned may be obtained.FIGS. 3A and 3B are explanatory views of relative transmitted light intensityand the like of a Bacillus spore. Specifically, FIGS. 3A and 3B are views for explainingthe relationship between the relative transmitted light intensity and the difference in theactual position of the objective lens 16 with respect to the distance z between theBacillus spore and the objective lens 16 when the transmitted light intensity ismaximized for the Bacillus spore.FIGS. 4A to 4D are explanatory views of relative transmitted light intensity andthe like of an acrylic particle. Specifically, FIGS. 4A to 4D are views for explaining therelationship between the relative transmitted light intensity and the difference in theactual position of the objective lens 16 with respect to the distance z between the acrylicparticle (microparticle) and the objective lens 16 when the transmitted light intensity ismaximized for the acrylic particle having a particle size of 30 um.First, images of the Bacillus spore and the acrylic particle at focal positions wereacquired by the image sensor 18. Thereafter, the stage 13 was moved in the verticaldirection along the optical axis direction by the stage drive unit 14, and the positionaldifference Az between the position of the objective lens 16 and the actual position of theobjective lens 16 when the relative transmitted light intensity of each microparticle wasmaximized on the image sensor 18 was measured by the laser displacement meter 15.FIG. 3A shows a captured image when the relative transmitted light intensity ismaximized in a liquid containing the Bacillus spore. As shown in FIG. 3A, it can beseen that the relative transmitted light intensity is maximized at the center of thecapturing region. Then, as shown in FIG. 3B, in the liquid containing the Bacillusspore, it was calculated that the relative transmitted light intensity was maximized at thepositional difference Az of 0 um.On the other hand, as shown in FIG. 4B, in the case of a liquid containing theacrylic particle with a particle diameter of 30 pm, the relative transmitted light intensityhas a negative value at a positional difference Az of 0 um at which the relativetransmitted light intensity is maximized in the case of the Bacillus spore describedabove. It can be seen that the transmitted light intensity is lower than the backgroundlight intensity. In addition, as shown in FIG. 4A, it can be seen that the relativetransmitted light intensity is minimized around the acrylic particle. As shown in FIG.4B, in the liquid containing the acrylic particle with a particle size of 30 um, it wascalculated that the relative transmitted light intensity was maximized outside thepositional difference Az of + 15 um.FIG. 4C is a captured image when the relative transmitted light intensity ismaximized in a liquid containing the acrylic particle with a particle size of 30 um. Asshown in FIG. 4C, it can be seen that the relative transmitted light intensity ismaximized at the center of the capturing region. As shown in FIG. 4D, in the liquidcontaining the acrylic particle with a particle size of 30 um, it was calculated that therelative transmitted light intensity was maximized at a positional difference Az of 26uum.Based on the measurement results, for the Bacillus spore and the acrylic particlewith a particle diameter of 30 um, the positional difference Az corresponding to thedifference between the distance z from the Bacillus spore and the acrylic particle with aparticle diameter of 30 um, which are microparticles when the transmitted lightintensity is maximized, to the objective lens 16 and the focal length of the objective lenswas calculated by using the ray transfer matrix described above. As a result, it wasfound that the positional difference Az in the liquid containing the Bacillus spore was0.9 um, the positional difference Az in the liquid containing the acrylic particle with aparticle diameter of 30 um was 22.5 um, and the positional difference Az was almostthe 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 was set to 11 = 130mm, the distance between the imaging lens 17 and the image sensor 18 was set to l2 =164.5 mm, the focal length of the objective lens was set to f; = 4.1125 mm, and thefocal length of the imaging lens 17 was set to f2 = 164.5 mm. In addition, r of theBacillus spore was 1 um, n of the Bacillus spore was 1.4, and n of the acrylic particlewas 1.5.In particular, it was found that the positional difference Az of 0.9 um in theliquid containing the Bacillus spore is substantially equal to the focal length of theobjective lens 16 (within the depth of field), and the relative transmitted light intensityis maximized at the focal position. Then, it was found that in the measurement ofBacillus spores, it is possible to detect each Bacillus spore by measuring the intensity oftransmitted light at the focal length.In this way, it is possible to detect Bacillus spores alone from the captured imageby utilizing the characteristic that the central part of a Bacillus spore brightly shineswhen the transmitted light intensity is maximum.FIG. 5 is a view showing an example of captured images of sludge. As shownin captured images (a) and (b) in FIG. 5, contaminants C may appear in addition toBacillus spores B.FIGS. 6A and 6B are views showing an example of a captured image and ateaching image used in deep learning. FIG. 6A is a captured image showing Bacillusspores B and contaminants C. The user gives, to the captured image of FIG. 6A, therespective center positions P of the Bacillus spores B as the correct answer data toobtain the teaching image shown in FIG. 6B. Deep learning can be performed bycausing the network to learn to detect the center positions P of the Bacillus spores B inthe captured image by using these images.FIG. 7 is a diagram showing an example of a Bacillus detection result by deeplearning. The detection unit 22 calculates the likelihood of the detection result ofBacillus spores obtained by image processing using deep learning, namely, calculatespossibility (certainty) of the center positions of Bacillus spores for each pixel.FIG. 7(a) shows an input image (captured image). The detection unit 22calculates, for example, the likelihood map shown in FIG. 7(b). This likelihood maprepresents that the brighter it is, the higher the likelihood, and the darker it is, the lowerthe likelihood. The symbols Q each indicate a location or an area where the higherlikelihood has been given in response to the corresponding Bacillus spore B (FIG. 7(a)).Then, the detection unit 22 performs threshold processing on the likelihood andobtains the detection result shown in FIG. 7(c) by setting pixels each having a givenlevel or more of likelihood as the center positions of the Bacillus spores B. In FIG. 7(c),the symbols S indicate the respective center positions of the detected Bacillus spores.FIG. 8 is a diagram illustrating an example of a data table regarding the numberof detected Bacillus spores and the 95% confidence interval. It is assumed that, whensludge in which Bacillus spores are present at a given density is collected and an imageis taken, the number of Bacillus spores detected from the image follows a Poissondistribution.Then, when the number of the detected Bacillus spores is denoted as k, and theconfidence interval of the confidence coefficient a% is obtained by interval estimationusing the Poisson distribution, the upper limit and the lower limit of the confidenceinterval correspond to the upper limit and the lower limit of the error of the number ofthe detected Bacillus spores, namely, the error of the measured density of the number ofthe detected Bacillus spores.In FIG. 8, a 95% confidence interval when the number of detected Bacillusspores is k (1 <k < 20) is illustrated on assumption of the Poisson distribution. Forexample, it is assumed that ten Bacillus spores (k=10) are detected as a measurementresult. In this case, from FIG. 8, it can be seen that the relative error of themeasurement result falls within the interval of -52.0% to +84.0% with a probability of95%.Here, for example, it is assumed that the allowable range of the relative errorbetween the true density and the measured density is set to -43.8% to +77.8% as thetarget accuracy requested by the user.In addition, the relative error can be calculated by the following Equation (1).Relative error = ((measured density) — (true density)) / (true density) --: (1)In such a case, as can be seen from FIG. 8, the relative error between the numberof detected Bacillus spores and the lower limit of the confidence interval is -44.0%when the number of detected Bacillus spores is fifteen (k=15), and -42.81% when thenumber of detected Bacillus spores is sixteen (k=16). Thus, it is sufficient that thenumber of detected Bacillus spores is sixteen or more.In addition, the relative error between the number of detected Bacillus sporesand the upper limit of the confidence interval is 79.0% when the number of detectedBacillus spores is eleven (k=11) and 74.75% when the number of detected Bacillusspores is twelve (k=12), and thus it is sufficient that the number of detected Bacillusspores is twelve or more.Therefore, in order to achieve the target accuracy requested by the user, it issufficient that measurement is performed such that the number of detected Bacillusspores is sixteen or more in total.FIG. 9 is a flowchart illustrating processing by the microparticle measurementsystem of the embodiment. Here, it is considered that the target accuracy requested bythe user is input and measurement is performed to achieve the target accuracy.First, in step S1, the information processing apparatus 20 receives an input oftarget accuracy to be achieved, from a measurement operator who uses the input unit 27.For example, the measurement operator inputs, as the target accuracy, a target value (forexample, an allowable range -43.8% to +77.8%) of a relative error between the truedensity and the measured density.Next, the measurement operator collects water from a water treatment apparatusin which a microorganism (Bacillus) to be measured is present, performs predeterminedpretreatment (filter treatment, heat treatment, and the like) on the collected specimen,and sets the specimen after the pretreatment in the microparticle measurement system10.In step 82, the acquisition unit 21 acquires the captured image from the imagesensor 18.In step S3, the detection unit 22 detects Bacillus spores appearing in the capturedimage.In step S4, the calculation unit 23 calculates the detection accuracy of theBacillus based on the detection result obtained by the detection unit 22 and apredetermined index. Note that the processing unit 24 may display the calculatedaccuracy on the display unit 26 (display device).In step S5, the processing unit 24 determines whether the target accuracy hasbeen achieved or not. The processing ends in the case of Yes in S5, and the processingproceeds to step S6 in the case of No in S5.In step S6, the processing unit 24 determines whether or not the number ofrepetitions ("the number of times reaching step S6" - 1 (times)) exceeds the numberthreshold (for example, about 5 to 10 times). The processing ends in the case of Yes inS6, and the processing proceeds to step S7 in the case of No in S6. Note that the case ofYes in S6 represents that a state where the accuracy does not become equal to or higherthan the target accuracy continues even if the operation (predetermined operation) forimproving the accuracy is performed a given number of times or more. In this case, themeasurement result is set to "the density equal to or lower than the measurement lowerlimit" or "unmeasurable", and the measurement is ended. Additionally, in this case, thedisplay unit 26 may display that the measurement result is "the density equal to or lowerthan the measurement lower limit" or "unmeasurable".In step S7, the processing unit 24 causes the display unit 26 to display a screenfor requesting the user to perform a predetermined operation to improve accuracy. Inthis case, the accuracy of the current measurement result may also be displayed on thedisplay unit 26.Note that the process of step S7 is the process of (1) among the above-describedprocesses (1) to (5), and the processes of (2) and (3) may be executed in place of theprocess of (1).The processes of (2) and (3) are reposted below.(2) Process of displaying information or a message requesting the user to acquirean additional captured image on the display unit 26(3) Process of automatically acquiring additional captured imageRegarding the process of (2), it is conceivable that, for example, an additionalcaptured image is acquired after the capturing region is changed by shifting the slideglass 12. In addition, it is also conceivable that an additional captured image is acquiredafter the slide glass 12 is replaced with another one.Regarding the process of (3), in a case where the configuration of themicroparticle measurement system 10 is different from that described above and anadditional new captured image can be automatically acquired, the processing unit 24automatically acquires an additional new captured image. In this case, for example, adevice for automatically shifting the slide glass 12 may be provided.In addition, in step S7, the process of (4) or (5) may be performed together. Theprocesses of (4) and (5) are reposted below.(4) Process of displaying detection accuracy of microparticle on the display unit(5) Process of displaying, on the display unit 26, the time required forcompleting measurement, which is estimated from the number of microparticles and thetotal number of captured imagesRegarding the process (5), the time required to complete the measurement ispredicted, and "remaining xx minutes until completion" is displayed on the display unit26. Specifically, for example, when additional image acquisition is requested, the timerequired for the additional image acquisition can be calculated from the information(number of captured images, time taken for measurement, and the like) acquired so far.For example, in a case where ten captured images have been acquired in ten minutes sofar and the number of additionally acquired captured images is 5, the time required foracquiring the additional images can be calculated as "5 minutes" by proportionalcalculation.In FIG. 8, after step S7, the user performs a predetermined operation, and thenthe process returns to step S2. Note that, in the case of the process (3), thepredetermined operation by the user is not performed, and the captured image isautomatically acquired in the next step S82.In this manner, according to the microparticle measurement system 10 of thepresent embodiment, it is possible to improve the accuracy of Bacillus measurement bycalculating the detection accuracy of Bacillus and determining the next processingbased on the accuracy and the target accuracy requested by the user. For example, in acase where the accuracy is less than the target accuracy, a concrete operation isrequested to an operator. Therefore, even if the operator does not have specializedknowledge, he / she can easily and appropriately perform the operation.In addition, by displaying the accuracy calculated from the measurement result,the operator can easily confirm and quantitatively evaluate the accuracy of themeasurement result.In addition, if the configuration of the microparticle measurement system 10 isconfigured to be able to automatically acquire an additional new captured image, theadditional new captured image can be automatically acquired when the accuracy is lessthan the target accuracy. In such a case, the burden on the operator is reduced.Note that the case where the microparticle measurement system 10 is configuredas a Stand-alone has been described above, but the present invention is not limitedthereto. In one example, the microparticle measurement system 10 may acquire acaptured image by the image sensor 18 on the local terminal side, transfer the capturedimage to a cloud server via a communication interface and a communication network,perform processing by the information processing apparatus 20 on the cloud server side,and display a processing result on the local terminal side.Moreover, the information processing apparatus 20 of the present embodimentincludes a control device such as a central processing unit (CPU), a storage device suchas a read only memory (ROM) and a random access memory (RAM), an externalstorage device such as a hard disk drive (HDD), a display device such as a displaydevice, and an input device such as a keyboard and a mouse, and has a hardwareconfiguration using a normal computer.Moreover, a computer program executed by the information processingapparatus 20 according to the present embodiment is a file in an installable format or anexecutable format, and is provided by being recorded in a computer-readable recordingmedium such as a semiconductor storage device such as a digital versatile disk (DVD),a universal serial bus (USB) memory, or a solid state drive (SSD).In addition, the program may be stored on a computer connected to a networksuch as the Internet and provided by being downloaded via the network. Moreover, theprogram may be provided or distributed via a network such as the Internet. Further, theprogram may be provided by being incorporated in a ROM or the like in advance.While some embodiments have been described, these embodiments have beenpresented by way of example only, and are not intended to limit the scope of theinventions. Indeed, the novel embodiments described herein may be embodied in avariety of other forms; moreover, various omissions, substitutions and changes in theform of the embodiments described herein may be made without departing from thespirit of the inventions. The accompanying claims and their equivalents are intended tocover such forms or modifications as would fall within the scope and spirit of theinventions.In one example, numerical values having been stored in the storage unit 25 maybe used as the target accuracy to be achieved, instead of being input by the user.

Claims

CLAIMS1. A microparticle measuring system comprising:a light source configured to emit illumination light to liquid including amicroparticle;an objective lens configured to condense the illumination light;an imaging lens configured to form an image of the illumination light condensedby the objective lens;an image sensor configured to capture the image of the illumination light formedby the imaging lens and output a captured image; andan information processing unit configured todetect the microparticle appearing in the captured image,calculate detection accuracy of microparticle based on the detectedmicroparticle and a predetermined index, anddetermine next processing based on the calculated detection accuracy ofmicroparticle and preset target accuracy.

2. The microparticle measuring system according to claim 1, wherein theinformation processing unit is configured to, when the detection accuracy ofmicroparticle is less than the target accuracy, determine and execute, as the nextprocessing, processing to cause a display device to display information requesting a userto perform a predetermined operation to improve the detection accuracy ofmicroparticle.

3. The microparticle measuring system according to claim 1, whereinthe index indicates the number of microparticles, andthe information processing unit is configured to calculate the detection accuracyof microparticle based on the number of detected microparticles.

4. The microparticle measuring system according to claim 1, wherein theinformation processing unit is configured to, when the detection accuracy ofmicroparticle is less than the target accuracy, determine and execute, as the nextprocessing, processing to cause a display device to display information indicating thatan additional captured image is required.

5. The microparticle measuring system according to claim 1, wherein theinformation processing unit is configured to, when the detection accuracy ofmicroparticle is less than the target accuracy, determine and execute, as the nextprocessing, processing to automatically acquire the additional captured image.

6. The microparticle measuring system according to claim 1, wherein theinformation processing unit is configured to, when the detection accuracy ofmicroparticle is less than the target accuracy, determine and execute, as the nextprocessing, processing to cause a display device to display the detection accuracy ofmicroparticle.

7. The microparticle measuring system according to claim 1, wherein theinformation processing unit is configured to, when the detection accuracy ofmicroparticle is less than the target accuracy, determine and execute, as the nextprocessing, processing to cause a display device to display a time required forcompleting measurement, the time being estimated from the number of microparticlesand a total number of acquired captured images.

8. A microparticle measuring method implemented by a computer included in amicroparticle measuring system, the microparticle measuring system being providedwith a light source to emit illumination light to liquid including a microparticle to bemeasured, an objective lens to condense the illumination light, an imaging lens to forman image of the illumination light condensed by the objective lens, and an image sensorto capture the image of the illumination light formed by the imaging lens and output acaptured image, the method comprising:a detection step of detecting the microparticle appearing in the captured image;a calculation step of calculating detection accuracy of microparticle based on adetected microparticle by the detection step and a predetermined index; anda processing step of determining next processing based on the calculateddetection accuracy of microparticle by the calculation step and preset target accuracy.

9. The microparticle measuring method according to claim 8, wherein theprocessing step includes, when the detection accuracy of microparticle is less than thetarget accuracy, determining and executing, as the next processing, processing to causea display device to display information indicating a predetermined operation is requiredto improve the detection accuracy of microparticle.

10. The microparticle measuring method according to claim 8, whereinthe index indicates the number of microparticles, andthe calculation step includes calculating the detection accuracy of microparticlebased on the number of detected microparticles.

11. The microparticle measuring method according to claim 8, wherein theprocessing step includes, when the detection accuracy of microparticle is less than thetarget accuracy, determining and executing, as the next processing, processing to causea display device to display information indicating that an additional captured image isrequired.

12. The microparticle measuring method according to claim 8, wherein theprocessing step includes, when the detection accuracy of microparticle is less than thetarget accuracy, determining and executing, as the next processing, processing toautomatically acquire the additional captured image.

13. The microparticle measuring method according to claim 8, wherein theprocessing step includes, when the detection accuracy of microparticle is less than thetarget accuracy, determining and executing, as the next processing, processing to causea display device to display the detection accuracy of microparticle.

14. The microparticle measuring method according to claim 8, wherein theprocessing step includes, when the detection accuracy of microparticle is less than thetarget accuracy, determining and executing, as the next processing, processing to causea display device to display a time required for completing measurement, the time beingestimated from the number of microparticles and a total number of acquired capturedimages.