JP3494559B2 - Flow type particle image analysis method and apparatus - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a flow-type particle image analysis method and a flow-type particle image analysis apparatus, and relates to static particles suspended in a flowing liquid, particularly cells or particles in blood or urine. The present invention relates to particle image analysis for capturing an image and performing particle analysis.

[0002]

2. Description of the Related Art In conventional particle image analysis, cells in blood, cells and particles in urine are classified and analyzed by preparing a sample on a slide glass and observing with a microscope. . In the case of urine, since the particle concentration in urine is low, the sample is centrifugally concentrated with a centrifuge in advance and then observed.

As a method or apparatus for automating these observation and inspection operations, a sample such as blood is smeared on a slide glass and set on a microscope, and the microscope stage is automatically scanned to detect the presence of particles. At this position, the microscope stage is stopped and a still particle image is captured, and the feature extraction and pattern recognition techniques of image processing technology are used to classify and analyze the particles in the sample.

However, in the above method, it takes time to prepare a sample, and it is necessary to find particles while mechanically moving the microscope stage and move the particles to an appropriate image capturing area. Therefore, there are disadvantages that analysis takes time and the mechanical mechanism becomes complicated.

In a particle image analysis method or particle image analysis apparatus that does not form a smear as described above, a flow site in which a sample sample is suspended in a liquid and is flown into a flow cell for optical analysis. The meter method is known.

This method using a flow cytometer observes the fluorescence intensity and scattered light intensity from each particle in a sample, and has a throughput of several thousand per second.

However, it is difficult to observe the feature amount that reflects the morphological features of the particles, and it is impossible to classify the particles by the morphological features that have been conventionally used under a microscope.

As an attempt to capture static particle images in a continuously flowing sample sample and classify and analyze the particles from each static particle image, Japanese Patent Publication No. 57-50099.
5, JP-A-63-94156, JP-A-4-
The technology described in Japanese Patent No. 72544 is known.

In the technique described in Japanese Patent Publication No. 57-500995, a sample sample is passed through a channel having a special shape into a wide imaging area, a static particle image is taken by a flash lamp, and the image is used. A method for particle analysis is shown.

In the above method, when a magnified image of sample particles is projected on a CCD camera using a microscope, a flash lamp, which is a pulse light source, periodically emits light in synchronization with the operation of the CCD camera.

Since the light emission time of the pulse light source is short, a still image can be obtained even when particles are continuously flowing, and the CCD camera can take 30 still images per second.

Further, in the technique disclosed in Japanese Patent Laid-Open No. 63-94156, a particle detection system is provided upstream of the particle image capturing area in the sample flow, in addition to the stationary particle image capturing system. In other words, it is a method in which the particle detection system knows particle passage in advance, and when the particles reach the particle image capturing area, the flash lamp, which is a pulse light source, is turned on at an appropriate timing.

In this method, it is possible to detect the passage of particles and take a static particle image at a timing only at that time without periodically emitting light from the pulsed light source. Even in the case of a sample sample that is collected and has a low concentration, it does not capture and process a meaningless image in which particles are not present.

Further, in JP-A-5-296915, as in JP-A-63-94156, a means for detecting particles in a sample flow is provided separately from a stationary particle image system,
In addition, a means for determining the actual number of particles in the sample particles and the number of particles of each classified type from the total number of particles image-processed in the detected particle image is provided.

Further, in Japanese Patent Laid-Open No. 7-83817,
The above-mentioned Japanese Patent Laid-Open No. 5-296915 discloses a one-to-one correspondence method between detected particles and detected particle images, which is a problem.

Further, in Japanese Patent Laid-Open No. 8-34748,
The adjustment stage of the particle detection system for making a one-to-one correspondence between the detected particles and the detected particle image is described.

[0017]

In the conventional particle image analyzer, generally, a still image of a sample particle flowing continuously is analyzed to efficiently determine the number and classification of plural kinds of particles in the sample. In order to perform well, it is necessary to dispose a particle detection system for detecting passing particles in the static particle image pickup area or upstream thereof, as is done in the above-mentioned known example. That is, it is necessary to turn on the pulse light source and capture a still image of the sample particle only when the sample particle passes.

The above-mentioned method of arranging the particle detection system for detecting passing particles can process a measurement sample having a small particle concentration very efficiently, so that the amount of measurement sample is increased, the analysis time is shortened, and the analysis accuracy is improved. You can measure improvement.

In such a flow-type particle image analysis apparatus, a particle detection system for detecting passage of particles to a stationary particle image pickup area or its upstream side, and a pulse light source being turned on only when the particles have passed therethrough An image pickup system for picking up an image is provided, but the particle detection system and the image pickup system generally have different measurement principles.

As the particle detection system, a method is used in which a laser beam is focused on a measurement sample that normally flows in a flow cell and irradiated, and light scattered light from particles that cross the laser beam is detected. There is.

The optical signal is converted into an electric signal by a photodetector. The magnitude of the optical signal is the optical refractive index of the particles,
It is affected by absorption, size, internal state of particles, scattered light detection conditions, etc., and does not always completely match the particle diameter, perimeter, color information, and texture information that are morphological information of particles used in image processing.

In the particle detection system, if the particle detection condition is a predetermined particle detection level or higher, the size of an actual static particle image obtained by the image pickup system is small and does not reach the particle detection level due to light scattering. The presence of particles is a frequent occurrence.

As a result, the number of particles counted by the particle detection system and the number of particles subjected to image processing do not match even if the counting of particles due to dead time, which is unavoidable in image capture, is taken into consideration, and particle analysis accuracy and reproduction are reproduced. It causes a decrease in sex.

The technique disclosed in Japanese Patent Application Laid-Open No. 7-83817 provides a method for correctly addressing these problems. The method disclosed in Japanese Patent Laid-Open No. 7-83817 has a one-to-one correspondence relationship between particles captured in one image and particles detected by a particle detection system.

To this end, means for counting, from the particle detection signal, particles corresponding to detected particles among particles shown in the image when the flash lamp light source is turned on,
This count value is made to correspond to the detected particles among the particles in the image, and means for distinguishing from the particles which are not regarded as the detected particles is provided.

The above-described method includes a particle detection system and an image pickup system, respectively, and the flow detection type particle image analysis method and the flow type particle image differ in the principle of the particle detection system and the principle of particle image identification of the image pickup system. In the analyzer, the detected particles and the particles in the static particle image are made to correspond to each other, and even if particles other than the detection particles of the particle detection system exist in the static particle image of the image capturing system, the number of particles of each type in the sample The present invention provides a flow-type particle image analysis method and a flow-type particle image analysis device which can accurately know the particle abundance ratio, particle density, and concentration information and have good analysis accuracy.

However, the above-mentioned Japanese Patent Laid-Open No. 7-838.
The method described in Japanese Patent No. 17 is a method in which the detection particles are associated with each still image, so that the work can be performed relatively reliably, but the processing itself becomes complicated,
It takes time to process a still image of particles flowing continuously. A complicated circuit configuration is required to process this still image at high speed.

As a method for alleviating this problem, there is a method, as disclosed in Japanese Patent Application Laid-Open No. 9-72842, in which the detected particles and the particle image particles are made to correspond to each other collectively after the measurement is completed.

That is, in Japanese Patent Application Laid-Open No. 9-72842, the number of expected particle images is calculated by using the total number of passing particles detected by the particle detection system, and the image-processed particle images are first sized. Only the number of particles expected to correspond to the detected particles is considered.

In this method, the processing is simple and there are few time constraints, but the particle characteristic parameters must be accumulated until the end of the measurement. In addition, a large image memory is required for a measurement target that must store a particle image. In the latter case, in order to solve this problem, at the stage of performing the process for one image, a process of rejecting particles smaller than a preset particle size among particles in the image is performed.

The two methods disclosed in the above-mentioned JP-A-7-83817 and JP-A-9-72842 can discard unnecessary particle images, and further reduce dead time in an image pickup system. Even if there is a dropout of particle images due to it, the correct classification ratio of particles can be obtained, and as a result, correct particle density can be obtained and analysis data that correlates well with human microscopic examination can be obtained. .

However, the following problems exist in order to establish correspondence between the particles detected by the above-mentioned particle detection system and the particles detected by the particle detection system in the imaged particle image. That is, in these two methods, it is implicitly assumed that the width of the sample flow is narrower than the imaging region of the particle image capturing system as a condition for making the detected particles correspond to the particle images. In an actual flow system, this assumption is not always satisfied, and there are cases where the assumption cannot be satisfied depending on the measurement conditions.

For example, the following facts can be considered. There are problems in the production of flow cells, problems with the adjustment of the optical system, and the phenomenon that the width of the sample flow changes greatly due to changes in ambient temperature. Further, in the case where there are a plurality of measurement modes and the sample flow conditions have to be changed, the phenomenon that the sample flow shape changes or the flow position moves may occur.

When the particle image sample is processed under such conditions, the phenomenon that the measurement particles pass outside the image pickup region or a part of the particle image is picked up occurs.
If one complete grain image cannot be obtained, it is usually discarded at the image processing stage.

FIG. 6 shows an example of a case where the above-mentioned problem that the specific sample flow and the particle image pickup visual field do not match occurs. In FIG. 6, two particles are detected by the particle detection system, but a particle image is not captured for particles running outside the visual field.

In this case, the number of particles to be image-processed is smaller than actually expected, and it becomes impossible to make a one-to-one correspondence between the particles detected by the above-mentioned detection system and the particle image. As a result, the reliability of the particle analysis data is reduced.

That is, when the sample flow width does not match the visual field of the particle image pickup system, the detected particles may flow outside the visual field of the image, and the detected particle and the particle image may have a correct correspondence relationship. The problem that can not occur occurs.

Further, there is an error factor existing in the measurement system, for example, a systematic error such that the sample particle concentration becomes small by replacing the pipe system with the aqueous solution adhering to the pipe wall at the stage when the sample flows. Occurs.

An object of the present invention is to quantify the state of the sample flow, maintain the apparatus in the best condition, and appropriately correct the measured data to improve the reliability of the flow type particle image. It is to realize an analysis method and apparatus.

In order to achieve the above object, the present invention is configured as follows. (1) A particle detection step of flowing a liquid sample in which particles to be measured are suspended into a flow cell and counting the number of particles in the sample by a particle detection system, and a static image of particles passing through an imaging region in the flow cell. an imaging step for imaging the particle imaging system, the image analysis unit in the flow type particle image analyzing method having an analysis step of performing a morphological classification of particles in the still image, a given particle
Prepare a liquid sample containing
Count the total number of particles that passed during the measurement time of this particle sample.
Image processing particles expected from the total number of particles counted and passed
And calculate the actual image for the expected number of image processing particles.
A particle image validator based on the ratio of the number of image-processed particle images.
Calculate the total number of particles and the number of particles
Particles based on the percentage of particle detections expected from the child concentration
A step of obtaining a concentration correction coefficient, a step of counting the total number of particles passed during the measurement time in the sample measurement step, and a step of calculating an expected number of image-processed particles from the total number of passed particles, A step of calculating the number of effective image particles by multiplying the number of image- processed particles by the above-mentioned particle image effective coefficient and the above-mentioned particle concentration correction coefficient, and measuring only the number of effective image particles from the larger size of the sample particle images An image and a defining step.

(2) Preferably, in determining the particle image effective coefficient in (1) above, during the particle measurement time.
At the stage of counting the total number of passed particles, set the particle detection conditions.
Apart from normal particle detection conditions, fine particles other than target particles
The particle detection condition is switched so as not to detect.

[0042]

[0043]

(3) Further, preferably, in ( 1 ) above , the magnitude of the particle image effective coefficient measured in advance is
The step of comparing with a predetermined value and the measured particle image effective coefficient
Is larger than the specified value, the sample flow is normal.
And the small sample flow is not normal.
And a step of disconnecting.

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

(4) Liquid sun in which particles to be measured are suspended
Flow the pull through a flow cell and use the particle detection system described above.
Particle detection means for counting the number of particles in the pull, and the above flow
Particle imaging of still images of particles passing through the imaging area in the cell
The image capturing means for capturing an image by the system and the image analyzing means
Includes analysis means for morphological classification of particles in still images.
Flow type particle image analyzer
The means is a liquid containing predetermined particles and having a known particle concentration.
Prepare the sample and let it pass during the measurement time of this particle sample.
Count the total number of particles that have passed and predict from the total number of particles that have passed.
Calculate the number of image processing particles
Based on the ratio of the number of particle images actually processed to the number
And the particle image effective coefficient determined by
The expected particle detection based on the number and the known particle concentration.
Enter the particle concentration correction coefficient calculated based on the ratio of the number of particles.
Memorizing memory and measuring time at sample measuring stage
A means to count the total number of particles that have passed through, and a total number of particles that have passed
Means for calculating the number of image processing particles expected from the number of particles,
This particle image effective coefficient is added to this expected number of image processed particles.
And calculate the effective image particle number by multiplying by the particle concentration correction coefficient
Which is the larger of the sample particle images
To determine the number of effective image particles as the measured particle image
And

[0056]

[0057]

(5) Preferably, in the above ( 4 ), when there are a plurality of measurement modes, each measurement mode is
Further, means for determining the particle image effective coefficient are further provided.

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

In order to make the particles detected by the particle detection system correspond to the particles detected by the particle detection system in the imaged particle image, the following problems occur. If the sample flow width does not match the visual field of the particle image capturing system, the detected particles may flow outside the visual field of the image, and a correct correspondence between the detected particles and the particle image cannot be obtained.

In order to correct this influence, the particle image effective coefficient Ks is introduced and the correspondence between the particle image and the particle detected particles is obtained, so that the correct number of particles can be measured and the particle concentration can be calculated.

Furthermore, by measuring the particle image effective coefficient Ks using standard particles before the start of measurement, it is possible to check the state of the sample flow of the particle image analyzer, and to obtain the information of the imaging position of the particle image. On the basis of this, the state of the sample flow can be grasped.

At the same time, a systematic error factor existing in the measurement system or a system in which the sample particle concentration is reduced by substituting the pipe system with an aqueous solution adhering to the pipe wall at the stage where the sample flows To the problem that causes the error
The particle concentration in the sample can be more accurately measured by previously examining the particle concentration correction coefficient Ku using standard particles and using a means for correcting the particle image measurement data.

[0087]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Prior to the description of the embodiments of the present invention, the basic principle of the present invention will be described. First, particles that have passed through the particle detection area in the flow cell are detected, and after a certain time, when the detected particles arrive at a predetermined position in the still image capturing area, the flash lamp is turned on and a still image of the particle is captured.

In general, it is often the case that small particles other than the particles detected by the particle detection system are also picked up in one picked-up still particle image. This phenomenon becomes more prominent as the particle detection signal level is increased. Therefore, there is a need for a method of distinguishing which of the captured particle images is the detection target particle.

Here, as a detection principle of the particle detection system, for particles exceeding the signal level of the particle detection signal,
A signal whose pulse width exceeds a predetermined value is regarded as a particle detection signal. This pulse width corresponds to the time taken for the measurement particles to pass through the particle detection system, and is proportional to the size of the passing particles.
Then, the total number of particles that have passed and are detected during the measurement time is counted to obtain the total number of detected particles.

Upon detection of particles, the flash lamp light source is turned on, a still image of the detected particles is photographed, image processing is performed on all particles in the image, and the particle feature amount necessary for particle classification is extracted to perform particle classification processing. I do. This feature extraction and particle classification identification processing is executed for all image particles imaged at the measurement time.

At the end of the measurement, the number of detected image particles in the particle still image due to lighting of the flash lamp during the measurement time is calculated from the total number of detected particles that have passed the measurement time in the above-mentioned particle detection system. The number of detected image particles is calculated according to the particle concentration in the sample, the measurement time, and the image capturing conditions of the TV camera. Although the form of this expected detection image particle number calculation formula changes depending on the measurement conditions, it can be obtained by assuming that the occurrence event of particle passage is a Poisson process in statistics.

Generally, it is expressed in the form of the following equation (1). N = Nfγ {λ + exp (-λ (1-h))} ----- (1) In the equation (1), Nf is the total number of frame images that can be captured by the TV camera during the measurement time. Measuring time is Tm, 1
If the frame time is Tf, then Nf = Tm / Tf.
The term γ is the ratio of the number of frames imaged by particle detection under actual sample conditions to Nf in the total number Nf of frame images. The content of γ changes depending on the measurement conditions. The value of Nf · γ corresponds to the expected number of frames in which the particle image is actually captured.

{Λ + exp (-λ (1-h))} of the equation (1) is the expected average number of particles including the detection particles existing in the image capturing area. Here, λ is the average number of particles existing in the volume Vg corresponding to the volume of the image capturing area, h is a value determined depending on where in the image capturing area the flash lamp is lit by particle detection, and is a value between 0 and 1. is there. Normally, the condition is a value close to 1. Vm is the measurement sample volume.

Further, λ is expressed by the following equation (2). λ = (Nm / Vm) ・ Vg −−− (2) Next, a specific example of the form of the expression of γ is shown below.

Let us consider a case where the particle image is most efficiently captured without the restriction on the lighting timing of the flash lamp, that is, the restriction that the flash lamp can be lit only in the odd field or even field of the TV camera. When the image accumulated in the CCD / TV camera over two fields is read out to the image memory after the flash lamp is turned on, the form of the equation of γ is represented by the following equation (3). γ = {2 (1-exp (-Nm ・ Tf / (2 ・ Tm)))} / {2-exp (-Nm ・ Tf / (2 ・ Tm))} −−− (3) Above (3) In the equation, Tf represents one frame time of the TV camera system. The dead time increases by one field under the condition that three fields are required for image reading and image processing. In this case, the form of γ is represented by the following expression (4). γ = {2 (1-exp (-Nm ・ Tf / (2 ・ Tm)))} / {3-2 ・ exp (-Nm ・ Tf / (2 ・ Tm))} −−− (4) Either way However, the form of the γ equation is particle concentration, measurement time,
It depends on the conditions of the TV camera imaging system. Statistical variations are unavoidable in this expected number of detected image particles, but a relatively correct number of detected image particles is obtained.

Further, in the equation (1), the value of Nf · γ corresponds to the average number of flash lamp emission during the measurement time, that is, the average number of frames of the TV image in which particles are imaged by particle detection. Therefore, the total number of images actually image-processed, that is, the total number N of frames in which particle images are captured
It is better to replace with fs in the actual device.
The calculation is simplified because it is not necessary to calculate the value of γ. When Nf · γ is replaced with Nfs in the equation (1), N is given by the following equation (5). N = Nfs * {[lambda] + exp (-[lambda] (1-h))} --- (5) As described above, the value of Nf * [gamma] corresponds to the expected number of frames in which the particle image is actually captured.

Depending on the measurement conditions, it is possible to derive an approximate expression from the expressions (1) to (5) and simplify the calculation process. However, the condition for satisfying the above-described calculation formula is satisfied when the sample flow is narrower than the particle image capturing visual field.

When the sample flow does not coincide with the imaging field of view, particles are detected, but particles may flow outside the imaging field of view, and the detected particles and the particle image described above cannot correspond to each other. There is. For this reason, this correction is necessary.

This phenomenon occurs not only when the sample flow does not coincide with the imaging field of view, but also when the position of the sample flow shifts, and when the sample liquid and the sheath liquid change in viscosity due to temperature rise. If it is caused,
Furthermore, it occurs when there are a plurality of measurement conditions having different flow rates, flow velocities, and the like. Further, if air bubbles or dust are present in the flow system, the position and shape of the sample flow change.

In order to deal with this problem, a particle image effective coefficient Ks is introduced, and the expected particle image number N in the above-mentioned calculation formula is multiplied by this coefficient to determine the effective image particle number.

The particle image effective coefficient Ks is calculated as follows. Before measuring the sample, a process of knowing the particle image effective coefficient is performed in advance. In order to perform this processing, prepare a liquid sample containing predetermined particles, count the total number of particles that passed during the measurement time of this particle sample, and the number of image-processed particles expected from the total number of particles that passed. And a step of obtaining a particle image effective coefficient by the ratio of the number of particle images actually image-processed to the expected number of image-processed particles.

As the predetermined particles, it is desirable to use a liquid sample in which standard particles of uniform size are suspended. As the standard particles, latex standard particles containing polystyrene as a main component are preferable.

In the step of counting the total number of particles that have passed during the measurement time of particles, the particle detection conditions are set separately from the particle detection conditions in the case of normal measurement so that fine particles other than the target particles are not detected. It is desirable to switch the conditions. As a result, only standard particles are detected. For this reason, it is desirable to use larger standard particles.

As the method of calculating the total number of particles that have passed through the particle detection system and the number of image-processed particles expected from the measurement conditions, the same calculation method as that described above is used. That is,
The equations (1) and (3) are used in the step of calculating the total number of particles that have passed through the particle detection system and the number of image-processed particles expected from the measurement conditions.

When the imaging conditions are different, the equation (4) is used instead of the equation (3). Further, when substituting the number of image frames, Expression (5) can be used instead of Expression (1).

The particle image effective coefficient Ks is obtained from the following equation (6) from the expected number N of image-processed particles and the number Ng of standard particle images actually image-processed. Ks = Ng / N --- (6) In a measurement system having multiple measurement modes, it is necessary to determine the particle image effective coefficient for each measurement mode.

The particle concentration in the measurement sample is determined as follows. A liquid sample to be suspended is flown into a flow cell, the number of particles in the sample is counted by a particle detection system, and a static image of particles passing through an imaging region in the flow cell is imaged by the particle imaging system.

Then, an analysis process is carried out to morphologically classify the particles in the image by the image analysis means, and the total number Nm of particles passed during the measurement time and the number of image-processed particles expected from the total number of particles passed. N is calculated, and the expected number N of image-processed particles is multiplied by the particle image effective coefficient Ks to calculate the number of effective image particles. Next, among the sample particle images, only the above-mentioned effective image particle numbers from the larger size are determined as measurement particle images.

If the object of particle classification is limited only to the measured particle image obtained by performing the above-mentioned processing, and if ni particles of particle classification i are present in this image, they are present in the measurement sample. The number of particles Ni is expressed in the form of the following expression (7). Ni = Nm · (ni / (N · Ks)) −−− (7) When expressing in the form of particle concentration, the number Ni of particles may be expressed by the number per unit volume using the measurement volume.

The step of measuring the particle image effective coefficient Ks in advance before measuring the actual particle sample is an important step for knowing the state of the particle image measuring system. That is, this is because the sample flow serves as a material for determining how the sample flow is flowing in the imaging visual field.

Specifically, it can be performed by comparing the particle image effective coefficient Ks measured in advance with a predetermined value. If it is larger than the predetermined value, it is judged that the sample flow is flowing normally, and if it is smaller than it, it is judged that the sample flow is not normal. If Ks is a value close to 1, it means that the sample flow is sufficiently flowing in the imaging visual field.

In addition to this, if a means for displaying the position information for each individual particle at which position in the visual field for picking up the particle image is provided, the state of the sample flow can be grasped more visually. .

In order to display the state of the sample flow, it is appropriate to express it as histogram information of the imaging position of the imaging field of view, especially as a histogram of the number of particles with respect to the position in the width direction or the flow direction of the sample flow.

If a liquid sample having a known particle concentration can be used as a standard particle at the stage of measuring the particle image effective coefficient Ks in advance, correction due to an error factor existing in the measurement system can be performed. Error factors include changes in concentration due to quantitative errors of the individual elements of the measurement system, systematic errors in the measurement system, and particle concentration by replacing the solution on the pipe wall with the sample as it flows through the pipe system. It is possible to correct the phenomenon that becomes smaller.

The ratio between the number of particles actually detected and the number of particles detected expected from the particle concentration known in advance is used as the particle concentration correction coefficient Ku.

Using the particle image effective coefficient Ks and the particle concentration correction coefficient Ku, the number Ni of particles of the particle component i of the measurement sample can be obtained using the following equation (8) instead of the above equation (7). I can. Ni = (Nm / Ku) * ni / (N * Ks) --- (8) To measure the particle concentration of the measurement sample, the number of particles per unit volume may be obtained by dividing by the measurement volume. When there are a plurality of measurement modes, it is necessary to perform the processing separately in each measurement mode.

When calculating the number of particles of the particle image effective coefficient and the particle concentration correction coefficient, a plurality of particles may be concatenated and regarded as one particle. In this case, it is necessary to determine the existence ratio of the connecting particles by the pattern recognition method of the particle image and perform the above correction based on this. When the abundance ratio of particles connected to each other is small, this effect can be ignored.

Further, before the measurement is started, the above-mentioned two correction coefficients are periodically obtained for each measurement mode, and the step of determining the particle concentration in the sample based on the values is provided. The reliability of the analysis result of the image processing result can be secured.

Furthermore, the state of the sample flow due to bubbles and dust existing in the flow system can be known from these correction coefficients, and can be used as information for determining whether or not the measurement system is operating normally.

Next, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a schematic configuration diagram of a flow-type particle image analysis device used in a flow-type particle image analysis method according to an embodiment of the present invention, and FIG. 2 is a schematic diagram of a particle detection system processing unit in the example of FIG. It is a block diagram which shows a structure.

FIG. 3 is a time chart showing the operation of the particle detection system shown in FIG. FIG. 4 is a schematic explanatory diagram of particle detection and still particle image capture. FIG. 5 is a flow chart showing the flow of processing when implementing the present invention.

In FIG. 1, 100 is a flow cell and 10 is a flow cell.
Reference numeral 1 is an image pickup means, 102 is a particle analysis means, and 103 is a particle detection means. Also, 1 is a flash lamp, 1a
Is a flash lamp drive circuit, 2 is a field lens,
3 is a microscope condenser lens, 5 is a microscope objective lens,
6 is an image forming position, and 7 is a projection lens.

In addition, 8 is a TV camera, 11 is a field stop,
12 is an aperture stop, 15 is a semiconductor laser, 16 is a collimator lens, 17 is a cylindrical lens, 18 and 19
Is a reflector.

Further, 20 is a beam splitter, 21 is a diaphragm, 22 is a photodetector circuit, 23 is a flash lamp lighting control circuit, 24 is an AD converter, 25 is an image memory, 26 is an image processing control circuit, and 27 is feature extraction. A circuit, 28 is an identification circuit, 29 is a central control unit, 40 is a particle detection processing unit, and 50 is a display unit.

In FIG. 1, a flow type particle image analysis apparatus used in a flow type particle image analysis method according to an embodiment of the present invention is a flow cell 100 to which a sample liquid in which particles are suspended is supplied, and a flow cell 100. An image capturing means 101 for capturing an image of the sample liquid therein, a particle detecting means 103 for detecting particles from the captured image, and a particle analyzing means 102 for analyzing the detected particles are provided.

The flow cell 100 has a sample solution 110.
At the same time, the sheath liquid is supplied, and a flow in which the sample liquid 110 is wrapped in the sheath liquid is formed. And sample liquid 1
The flow of 10 is a stable steady flow having a flat cross-sectional shape in the direction perpendicular to the microscope optical axis 9 (described later) of the image pickup means 101 (described later), that is, a so-called sheath flow, and the center of the flow cell 100 is the paper surface. Run down from above. The flow velocity of the sample liquid 110 is controlled by the central control unit 29 (described later).

The image pickup means 101 has a function as a microscope, and makes the flash lamp 1 which is a pulse light source, the flash lamp drive circuit 1a for causing the flash lamp 1 to emit light, and the pulse light flux from the flash lamp 1 parallel. The field lens 2 and the microscope condenser lens 3 for focusing the parallel pulsed light flux 10 from the field lens 2 on the sample liquid flow 110 in the flow cell 100.
A microscope objective lens 5 for condensing the pulsed light flux irradiated on the sample liquid flow 110 in the flow cell 100 to form an image at the image forming position 6, and a particle image at the image forming position 6 projected via the projection lens 7. , A so-called interlace method, a TV that converts the image data signal which is an electric signal taken in
Camera 8 and field stop 1 for limiting the width of pulsed light flux 10
1 and an aperture stop 12. As the TV camera 8, a CCD camera or the like having a small afterimage is generally used.

Further, the particle detecting means 103 is a semiconductor laser 15 which is a detection light source for emitting laser light as detection light.
A collimator lens 16 for converting the laser light from the semiconductor laser 15 into a parallel laser light beam 14, a cylindrical lens 17 for focusing only one direction of the laser light beam 14 from the collimator lens 16, and a cylindrical lens 17 A reflector 18 for reflecting the laser beam 14, a microscope condenser lens 3, and a flow cell 10
The laser beam 1 from the reflecting mirror 18 is located between 0 and 0.
4 and a micro-reflecting mirror 19 for guiding the sample 4 to the vicinity of the upstream side of the image capturing area on the sample liquid flow 110.

Further, the particle detecting means 103 is a microscope objective lens 5 for collecting the scattered light of the laser light flux 14 by the particles (this microscope objective lens 5 is the image pickup means 10).
1 is also used as a microscope objective lens 5 for image formation), a beam splitter 20 for reflecting scattered light condensed by the microscope objective lens 5, and scattered light from this beam splitter 20 via a diaphragm 21. A photodetector circuit 22 that receives light and outputs an electric signal based on its intensity, and
A flash lamp emission control circuit 2 for operating the flash lamp drive circuit 1a based on an electric signal from the flash lamp 2.
3 and 3.

Further, the particle analysis means 102 converts the image data signal transferred from the TV camera 8 into a digital signal, and the data based on the signal from the AD converter 24 at a predetermined address. An image memory 25 for storing, and an image processing control circuit 26 for controlling writing and reading of data in the image memory 25.
And a feature extraction circuit 27 that performs image processing based on a signal from the image memory 25 to perform particle number and classification, and an identification circuit 28.
A central control unit 29, a particle detection processing unit 40 that determines the number of particles in the sample liquid 110, controls the particle detection system, and calculates a particle image, and the number of particles that is the result of the image processing described above. The display unit 50 displays the classification.

The central controller 29 controls the TV camera 8
Imaging conditions, sample liquid flow conditions of the flow cell 100, control of the image processing control circuit 26, storage of image processing results from the identification circuit 28, exchange of data with the particle detection processing unit 40, display on the display unit 50, etc. Is configured to do.

Next, referring to FIG. 2, the particle detection processing unit 4
The configuration of 0 will be described. In the figure, the same reference numerals as in FIG.
Since it is an equivalent part, detailed description is omitted. In FIG. 2, 44 is a particle detection circuit, 41 is a particle detection level setting unit, 42 is a particle detection pulse width setting unit, and 43 is a detection particle counter. Also, 70 is an image processing particle number calculation unit, 7
Reference numeral 1 is a flash lamp lighting frequency calculation unit, 72 is an image processing particle number counting unit, and 73 is a flash lamp lighting frequency counting unit. A particle detection system control / calculation unit 75 controls the entire particle detection system adjustment unit.

In FIG. 2, a particle detection circuit 44 constituting the particle detection processing section 40 detects a particle detection signal from the photodetection circuit 22 which is equal to or higher than a predetermined signal level. The particle detection level of the particle detection circuit 44 is compared with the detection conditions set by the particle detection level setting unit 41 and the particle detection pulse width setting unit 42, and those satisfying this condition are regarded as the particle detection signal. The detection particle counter 43 counts the number of pulses detected by the particle detection circuit 44. At the end of the measurement, the count value of the particle detection counter 43 becomes the total number of particles that have passed through the detection system.

Using the particle count value Nm of the particle detection counter 43 at the end of measurement, the particle image number calculation unit 70 calculates the number N of image-processed particles expected to be image-processed during the measurement time. It In addition, using the particle count value Nm, the flash lamp lighting number calculation unit 71 expects that the flash lamp will light during the measurement time n
f is calculated.

On the other hand, the number of particle images N ′ actually image-processed during the measurement time and the flash lamp lighting number nf ′ are
The particle image counting unit 72 and the flash lamp lighting frequency counting unit 73 are set by the image processing control circuit 26 and the flash lamp lighting control circuit 23, respectively. The particle detection system control / arithmetic unit 75 controls the entire particle detection system,
Further, a particle number correction calculation process is executed.

Since the detected particle counter 43 counts the total number of particles detected in the measurement time, the particle image number calculation unit 70 actually processes the image within the measurement time based on this value. Calculate the total number of expected particle images using a predetermined formula. The calculation formula depends on the measurement conditions. Examples of the calculation formulas are shown in the above formulas (1) to (4). The flash lamp lighting number calculation unit 71 has a function of calculating an estimated value of the total number of frames captured by the TV camera during the measurement time. The formula (6) is used as the calculation formula.

The operation of the flow-type particle image analysis method and the flow-type particle image analysis apparatus used therefor will be described. First, the image pickup means 101 and the particle detection means 10
The operation with 2 will be described. The semiconductor laser 15 constantly oscillates continuously and always observes that particles in the sample 110 pass through the detection region.

Laser beam 14 from semiconductor laser 15
Is converted into a parallel laser light beam by a collimator lens 16, and a light beam 14 is converted by a cylindrical lens 17.
Is focused only in one direction.

Then, the laser beam 14 is reflected by the reflecting mirror 18 and the minute reflecting mirror 19, and is irradiated onto the sample liquid flow 110 in the flow cell 100. This irradiation position is set by the cylindrical lens 17 by the laser beam 14
Is the particle detection position where the
It is a position near the upstream side of the upper image capturing area.

The particle to be analyzed is the laser beam 14
This laser light beam 14 is scattered when it crosses. The scattered light of the laser beam 14 is reflected by the beam splitter 20 and received by the photodetector circuit 22. Then, the light detection circuit 22 converts the light into an electric signal based on the intensity of the received light.

Further, the output of the light detection circuit 22 is sent to the particle detection system processing section 40, and signals having a predetermined level and a predetermined pulse width or more are regarded as detected particles, and the result is controlled by the flash lamp lighting control. Tell circuit 23.

That is, when particles are detected by the photodetector circuit 22, it is assumed that the particles to be image-processed have passed,
A signal indicating the detection is transmitted to the flash lamp emission control circuit 23. The predetermined delay time depends on the distance between the particle detection position and the image capturing area and the sample liquid 11
It is determined by a flow velocity of 0 and the like.

When the flash light emission signal is transmitted from the flash lamp lighting control circuit 23 to the flash lamp drive circuit 1a, the flash lamp drive circuit 1a causes the flash lamp 1 to emit light.

The pulsed light emitted from the flash lamp 1 travels on the microscope optical axis 9, becomes parallel light by the field lens 2, is focused by the microscope condenser lens 3, and is irradiated onto the sample liquid flow 110 in the flow cell 100. It The width of the pulsed light flux 10 is limited by the field stop 11 and the aperture stop 12.

Sample liquid flow 11 in flow cell 100
The pulsed light flux irradiated to 0 is condensed by the microscope objective lens 5 and forms an image at the image forming position 6. The image at the image forming position 6 is projected onto the image pickup surface of the TV camera 8 by the projection lens 7 and converted into an image data signal by the interlace method. In this way, the static particle image of the particles in the sample liquid 110 is captured.

The image pickup conditions of the TV camera 8 are preset in the central control unit 29, and the image pickup operation of the TV camera 8 is controlled by these image pickup conditions.

Next, the particle analysis means 102 will be described. An image data signal captured by the TV camera 8 and converted by the interlace method is used as an AD converter 2
Converted to digital signal in 4 and the data based on this is
It is controlled by the image processing control circuit 26 and stored in a predetermined address of the image memory 25.

The data stored in the image memory 25 is read out under the control of the image processing control circuit 26, is input to the feature extraction circuit 27 and the identification circuit 28, is subjected to image processing, and the central control unit 29 receives the result. Is memorized. Contents stored in the central control unit 29 are particle identification feature parameters used for particle classification and particle classification result data.

The classification and identification process of particles is automatically performed by the pattern recognition process which is usually performed. This image processing result, measurement conditions, and information on the number of images that have been image processed,
It is sent from the image processing control circuit 26 to the particle detection system processing unit 40.

Now, with reference to FIG. 4, the relationship between the particle detection position and the still particle image capture position in one image particle counting will be described.

As shown in FIG. 4, Tg is the time required for the particles to flow from the A end to the B end of the image, and Te is the particle from the flash lamp emission position P to the B end of the field of view for capturing the still particle image. And To is
Assuming that the time required for the particles to flow from the edge A of the image to the flash lamp emission position P is Tg = Te + To.

When the particles in the measurement sample pass through the particle detection position and the particles whose particle detection signal is above a predetermined level are detected, a detection pulse signal is output. After the detection pulse signal is output, the flash lamp 1 is caused to emit light after a delay time Td until the particles reach a predetermined position in the static particle image capturing area.

The light emitting conditions of the flash lamp 1 are as follows. After the still particle image capture by the previous particle detection is completed, that is, the flash lamp 1 emission after the flash lamp 1 emission is prohibited, and the image signal is transferred by one filled particle image signal by two field reading signals. , The flash lamp 1 is turned on again. It is the lamp light emission ready signal that makes this state,
It is made by the flash lamp control circuit 23.

The signal level of the lamp emission ready signal is
If it is "on", the flash lamp 1 is in a light emission ready state, and if it is "off", it is in a light emission prohibition state. In FIG. 3, the flash lamp 1 is caused to emit light after a lapse of Td time after y particles have been detected, but there is a possibility that particles that have passed before y particle detection exist on the downstream side of the flow.

The particles that may be present on the downstream side of the above-mentioned flow are those present at the position of the time interval Te before the passage of the y particles, and correspond to, for example, x particles. This is the time T during which the sample particles flow from point P to end B.
During e, the lamp light emission ready signal is still "o".
This is because it may not be in the n "state.

This is remarkable when the concentration of particles in the sample is high. The section in which such a state occurs is shown by the hatched section of the lamp light emission ready signal in FIG.

If the time from when the lamp light emission ready signal is turned "on" to when the lamp light is emitted becomes longer than Te,
Downstream particles, such as x particles, flow away from the image,
The shaded area disappears. One image particle counting time interval is the time T during which the particles pass through the stationary particle image capturing area.
between g.

The timing of each signal will be described with reference to FIG. In the particle detection signal, detection signals are generated from three particles x, y, and z. In order to cause the flash lamp 1 to emit light in a timely manner, a particle detection delay Td signal delayed by time Td may be transmitted after the particles are detected.

Here, the lamp light emission ready signal is "on".
Alternatively, it is determined whether or not the lamp should emit light in the “off” state. In FIG. 3, since the still particle image was being captured in the previous frame, the lamp light emission ready signal was not "on" from the beginning, and the lamp light emission ready signal was ready "on" due to the vertical synchronization signal. Became.

For this reason, the flash lamp does not emit light with particle x, but emits light with the next particle y, and the lamp emission ready signal becomes "off".

Next, referring to FIG. 2, the particle detection system processing unit 4
0 will be explained. First, the step of setting the particle image effective coefficient and the particle concentration correction coefficient will be described as a step before measuring the sample to be measured.

At this stage, in order to determine these correction factors, a sample solution in which standard particles of uniform size are suspended is used. It is assumed that the particle concentration is known by being measured by another means in advance. This standard particle sample is set in a flow type particle image analyzer and measurement is started. At this time, the particle detection conditions of the particle detection system are set to be stricter than normal measurement conditions, and it is necessary to set so that only the standard particles described above are detected.

The particle detection condition is the particle detection level setting unit 4
This can be performed by changing the setting values of 1 and the particle detection pulse width setting unit 42. The total number of standard particles that have passed within the measurement time is counted by the particle detection counter 43. The flash lamp lighting frequency is counted by the flash lamp lighting frequency counting unit 73. Further, as a result of processing the particle image, the number of images of standard particles obtained by subjecting this sample to the particle image processing is set in the image processing particle number counting section 72 through the image processing control section 26.

The expected number of particle image processes is calculated based on the total number of detected particles and the particle detection count obtained by these processes. As the calculation formula, the above formulas (1) to (4) are used. The calculation is executed by the image processing particle number calculation unit 70.

The value calculated by the image processing particle number calculating section 70 is generally larger than the count result value of the image processing particle counting section 72 because the sample flow and the particle image capturing visual field do not match. Further, when the expected number of particle image processes is calculated using the counting result of the flash lamp lighting number counting unit 73, the above equation (5) is used.

The particle image effective count Ks is the ratio of the count value of the image processing particle number counting section 72, which is the number of particle images actually processed, to the expected number of particle image processes, and is expressed by the equation (6).
Required by. The particle image effective count Ks approaches 1 when the sample flow and the particle image capturing visual field match, and shows a value smaller than 1 when they do not match.

Further, since the particle concentration correction coefficient Ku is premised on standard particles having a known particle concentration, the number of particles to be originally counted in the particle detection system can be predicted. Is calculated by the ratio of the count value of the particle detection counter 43 actually counted.

The actual calculation is performed by the particle detection system control / arithmetic unit 7
It will be held at 5. The particle concentration correction coefficient Ku is used to correct the particle concentration due to the configuration of the measurement system and systematic errors.
The value of the particle concentration correction coefficient Ku is around 1.

The values of the particle image effective count Ks and the particle concentration correction coefficient Ku are set in advance, and the values of the actually measured particle image effective count Ks and the particle concentration correction coefficient Ku and the values obtained by calculation are calculated. By providing a means for comparing, it is possible to grasp the device state of the particle image analysis device from the comparison result.

Further, if the particle image pickup position where the particles are picked up by the particle image processing system is known, it is possible to collate them with the values of the particle image effective count Ks and the particle concentration correction coefficient Ku obtained by the calculation.

For example, by using a means capable of displaying the imaged position of the particle image as an image of the entire measured particle, it can be used as a reference for pursuing the cause of what causes the correction value to be an abnormal value. Also, it can be displayed as histogram information of the image pickup position. In this case, it is preferable to display the histogram in the sample flow direction or the sample flow width direction.

From the degree of coincidence between the count value of the flash lamp lighting frequency counter 73 and the calculated value of the flash lamp lighting frequency calculator 71, it is possible to check the malfunction of the flash lamp lighting control circuit 23 and the flash lamp lighting operation. This check processing is performed in the particle detection system control / arithmetic unit 75.

Next, the step of measuring an actual particle sample will be described. Note that the measurement process is the same as that for obtaining the correction coefficient described above. The measurement sample is set in the particle image analyzer and the measurement is started. in this case,
Since the particle detection condition does not target only the standard particles like the above-mentioned standard particles, the particle detection condition needs to be relaxed so that a smaller particle component can be processed.

While detecting one particle sample,
The pattern recognition / classification process of the particle image is continued by the particle image process, and the next process is executed at the measurement end stage. Since the number of particles passed within the measurement time is stored in the particle detection counter 43, the image processing particle number calculation unit 7 is based on this value.
Calculate the expected number of particle image processes at 0. The calculation by the image processing particle number calculation unit 70 is executed by the above formulas (1) to (5).

Next, since the sample flow does not match the particle image pickup visual field, the number of particle images is corrected. The particle image effective count Ks measured as described above is multiplied by the expected particle image processing number to obtain the number of particles image-processed in the actual measurement. This calculation is performed by the particle detection system control / calculation unit 75.

Next, the work for establishing correspondence with the detected particle is carried out from the image-processed particle image. Specifically, among the characteristic parameters of the individual particles of the image-processed particle image, attention is paid to those corresponding to the particle size, and the particles are rearranged in order from the larger size. Then, the product of the particle image effective count Ks obtained as described above is calculated and set as the expected number of particle image processes, and only this number is determined as the particle image in which particles have been detected. By this work, the particle image corresponding to the particles detected by the particle detection system is selected. This correspondence is processed by the central control unit 29.

The number Ni of the pattern-recognized / classified component i in the sample is calculated from the above (7) as the number ni of particle images. When the particle concentration correction coefficient Ku is taken into consideration, the above equation (8) is calculated. here,
Nm is the number of particles counted by the particle detection system and is the count value of the particle detection counter 43.

In the above description, the number of particle images is focused on, but the particle detection system can be adjusted by focusing on the number of times the flash lamp 1 is turned on. The expected number of particle images is calculated from the number of times the flash lamp 1 has been turned on by the above equation (5), and is multiplied by the image processing effective coefficient Ks to perform the above-described operation. If the count value of the flash lamp lighting frequency coefficient unit 73 is used, the number of particle images can be calculated more easily.

When calculating the number of particles of the particle image effective coefficient and the particle concentration correction coefficient, a plurality of particles may be concatenated and regarded as one particle. This can occur during the particle detection system and particle image processing stages. It is necessary to determine the existence ratio of the connecting particles by the pattern recognition method of the particle image and perform the above correction based on this. When the abundance ratio of particles connected to each other is small, this effect can be ignored.

The flow of the above particle image analysis processing will be described with reference to the flowchart shown in FIG. First step (S1): The standard particles for correction, whose particle concentration is uniform and whose concentration is known, are measured. The measurement targets are the particle image effective coefficient Ks and the particle concentration correction coefficient Ku as the correction coefficient. At this time, the particle detection conditions are set so that only standard particles are detected.

Second step (S2): Of the measurement results, the total number of particles detected by the particle detection system, the number of particle images expected from this value, the number of image-processed particles relating to the standard particles actually processed by the image-processing system Further, the particle image effective coefficient Ks and the particle concentration correction coefficient Ku are calculated based on the number of particles detected by the particle detection system which is expected from the particle concentration. As the calculation formula, the above formulas (1) to (6) are used.

Third step (S3): A histogram is created based on the image pickup position of the image pickup field of the particle image.

Fourth stage (S4): Particle image effective coefficient K
s, the particle concentration correction coefficient Ku, and the state of the sample flow are displayed and output.

Fifth step (S5): Particle image effective coefficient Ks
The value of the particle concentration correction coefficient Ku and the state of the sample flow are checked to determine whether the sample flow is normal. If normal, the process proceeds to the sixth step (S6) of measuring the particle sample to be measured. If the sample flow is abnormal, the process proceeds to the 11th step (S11).

Sixth step (S6): It is judged whether or not a measurement sample is present. If no measurement sample is present, the particle image analysis operation is terminated. If the measurement sample exists, the process proceeds to the seventh step (S7).

Seventh step (S7): The particle detection condition is set to the condition for detecting the particles to be measured, the actual sample is measured, and the particle image analysis is executed.

Eighth step (S8): Here, the particle image and the detected particle caused by the mismatch between the sample flow and the particle imaging visual field are associated with each other. That is, the particle number correction calculation is performed.
For that purpose, the particle image effective coefficient Ks is used. A particle image that is likely to be detected by the detection system is selected from all the image-processed particle images. By this operation, the detected particles and the particle image can be associated with each other.

Ninth step (S9): Particle concentration correction coefficient Ku
And the number of particles Ni of the particle component i in the measurement volume,
In addition, the particle concentration is calculated.

Step 10 (S10): The particle analysis result is displayed and output. Then, returning to the sixth step, it is judged whether or not the measurement sample is present, and if the measurement sample is not present, the particle image analysis operation is ended. If the measurement sample exists, the process proceeds to the seventh step (S7).

Eleventh stage (S11): In the fifth stage, if it is determined that the sample flow is abnormal,
In this 11th stage, display of abnormal sample flow
Output and stop processing.

When there are a plurality of sample measurement modes, adjustment is performed for each measurement mode, and the operation check of the apparatus is executed for each measurement mode. Naturally, the particle image effective count Ks and the particle concentration correction coefficient Ku as the correction data need to be set for each measurement mode. This is because the values of the particle image effective count Ks and the particle concentration correction coefficient Ku generally change when the conditions of the sample flow are different.

The above-described flow type particle image analysis method and flow type particle image analysis device according to one embodiment of the present invention are used for biological samples and cells suspended in a liquid, and blood cell components such as red blood cells and white blood cells in blood. Or, it is effective for classification and analysis of urinary sediment components present in urine.

Particularly, in counting the number of particles of the urinary sediment component in urine and classifying the particles, the number of particles existing in each sample may differ by several digits or more. The treatment is effective in knowing the information on the number of particles in the sample liquid.

In the urinary sediment component, the types and sizes of particles are very diverse, and it is not possible to accurately correspond the detected particles of the particle detection system and the particles in the static particle image.
Accurate particle counting, particle classification,
The particle concentration can be analyzed.

The above-described example is an example in which the laser light flux from the semiconductor laser is used as the detection light and the laser light flux scattered by the particles is used as the particle detection of the particle detection system, but the present invention is not limited to this. It is also possible to use fluorescence or transmitted light from the above, a method of detecting particles by a one-dimensional image sensor, or a method of detecting particles by a change in electric resistance due to passage of particles.

It should be noted that, up to now, in image capturing, T
Although the interlaced scanning has been described as the V camera imaging method, the present invention can be applied to the non-interlaced scanning method, and the same effect as the above-described example can be obtained.

[0197]

Since the present invention is constructed as described above, it has the following effects. It is possible to realize a flow type particle image analysis method and apparatus capable of quantifying the state of sample flow, maintaining the apparatus in an optimum state, appropriately correcting measured data, and improving the reliability of data. it can.

That is, in order to make the particles detected by the particle detection system correspond to the particles detected by the particle detection system in the imaged particle image, the sample flow width does not match the visual field of the particle image imaging system. However, in order to correct this effect, the particle image effective coefficient Ks is introduced and the correspondence between the particle image and the particle detection particles is taken, so that the correct number of particles can be measured and the particle concentration can be calculated.

Furthermore, by measuring the particle image effective coefficient Ks using standard particles before starting the measurement, the state of the sample flow of the particle image analyzer can be checked, and the information of the imaging position of the particle image can be obtained. On the basis of this, the state of the sample flow can be grasped.

At the same time, a measurement error such as a quantitative error factor existing in the measurement system, or replacement of the piping system with an aqueous solution adhering to the tube wall at the stage when the sample flows to reduce the sample particle concentration, etc. To solve the problem of systematic error in the system, the particle concentration correction coefficient Ku is checked in advance using standard particles having a known particle concentration, and a means for correcting the particle image measurement data is used to measure the particles in the sample. The concentration can be measured more accurately.

[Brief description of drawings]

FIG. 1 is a schematic configuration diagram of a flow type particle image analysis apparatus used in a flow type particle image analysis method according to an embodiment of the present invention.

FIG. 2 is a block diagram showing a configuration of a particle detection processing unit in the example of FIG.

FIG. 3 is a diagram showing a signal timing relationship for counting one image particle in the example of FIG. 1.

FIG. 4 is a schematic explanatory diagram of particle detection and still particle image capturing.

FIG. 5 is a flowchart showing a flow of particle image analysis processing according to the present invention.

FIG. 6 is a schematic diagram showing the presence of unimaged particles due to a mismatch between the particle sample flow and the particle image imaging field of view.

[Explanation of symbols]

1 Flash Lamp 1a Flash Lamp Drive Circuit 2 Field Lens 3 Microscope Condenser Lens 5 Microscope Objective Lens 6 Imaging Position 7 Projection Lens 8 TV Camera 11 Field Stop 12 Aperture Stop 15 Semiconductor Laser 16 Collimator Lens 17 Cylindrical Lens 18, 19 Reflector 20 Beam splitter 21 Aperture 22 Light detection circuit 23 Flash lamp lighting control circuit 24 AD converter 25 Image memory 26 Image processing control circuit 27 Feature extraction circuit 28 Identification circuit 29 Central control unit 40 Particle detection system processing unit 41 Particle detection level setting unit 42 Particle detection pulse width setting unit 43 Particle detection counter 44 Particle detection circuit 70 Image processing particle number calculation unit 71 Flash lamp lighting frequency calculation unit 72 Image processing particle number counting unit 73 Flash lamp lighting frequency counting unit 75 Particle detection system control Calculation unit 100 Flow cell 101 Image capturing means 102 Particle analyzing means 103 Particle detecting means 110 Sample flow Nf Total number of frame images that can be captured by the TV camera at the measurement time Tm Measurement time Tf 1 Frame time γ Nf under actual sample conditions Ratio of the number of frames imaged by particle detection to Nf Vg Volume equivalent to image capture area volume λ Average number of particles present in volume Vg equivalent to image capture area volume h Flash lamp lighting by particle detection where in image capture area A value determined depending on whether or not to carry out. Vm between 0 and 1 Vm Measurement sample volume Td Particle detection delay time Te Time required for particle image to flow from flash lamp emission position P to B end Tg Particle image from A end of image Time required to flow to B end To Particle image Time required to flow from the end A to the flash lamp emission position P Ks: particle image effective coefficient Ku: particle density correction coefficient

─────────────────────────────────────────────────── ─── Continuation of the front page (58) Fields surveyed (Int.Cl. ⁷ , DB name) G01N 15/00-15/14

Claims (5)

(57) [Claims] 1. A particle detection step of flowing a liquid sample, in which particles to be measured are suspended, into a flow cell, and counting the number of particles in the sample by a particle detection system; and a step of detecting particles passing through an imaging region in the flow cell. In a flow-type particle image analysis method having an imaging step of capturing a still image with a particle imaging system and an analysis step of performing morphological classification of particles in the still image by image analysis means, a particle concentration including predetermined particles Known liquid sample
Was prepared, and all the particles that passed during the measurement time of this particle sample were prepared.
Image that counts the number of particles and is expected from the total number of particles that have passed
Calculated the number of particles processed, and actually processed the image against the expected number of particles processed.
Obtain the particle image effective coefficient based on the ratio of the number of particle images, and further from the total number of particles and the previously known particle concentration.
A particle concentration correction factor based on the expected number of detected particles.
The step of obtaining the number, the step of counting the total number of particles passed during the measurement time in the sample measurement step, the step of calculating the expected image processing number from the total number of particles passed, and the expected image processing Particle image effective coefficient before particle number
And a step of calculating the number of effective image particles by multiplying by the above-mentioned particle concentration correction coefficient, and a step of determining only the above-mentioned effective image particle number as a measurement particle image from a sample particle image having a larger size. Flow type particle image analysis method. 2. The flow-type particle image analysis method according to claim 1 , wherein when determining the particle image effective coefficient, a particle detection condition is set to a normal particle detection condition at the step of counting the total number of particles that have passed during the particle measurement time. In addition to the particle detection conditions, a flow type particle image analysis method is characterized in that the particle detection conditions are switched so that fine particles other than the target particles are not detected. 3. The flow-type particle image analysis method according to claim 1 , wherein the magnitude of the particle image effective coefficient measured in advance is compared with a predetermined value, and if the measured particle image effective coefficient is larger than the predetermined value. A flow-type particle image analysis method, further comprising: determining that the sample flow is flowing normally, and determining that the small flow sample flow is not normal. 4. A particle detecting means for flowing a liquid sample, in which particles to be measured are suspended, into a flow cell, and counting the number of particles in the sample by a particle detection system, and of particles passing through an imaging region in the flow cell. In a flow-type particle image analysis device having an image pickup means for picking up a still image by a particle image pickup system and an analysis means for performing morphological classification of particles in the still image by the image analysis means, the particle detection means has a predetermined size. Of particle concentration including particles of
Prepared liquid sample and measure the particle sample.
Count the total number of particles that passed in a fixed time
Calculate the expected number of image processing particles from the number, and actually perform image processing against the expected number of image processing particles
Particle image effective coefficient calculated based on the ratio of the number of particle images
Number, and the total number of particles above, and the particle concentration
Is calculated based on the percentage of particle detection
Storage means for storing the particle concentration correction coefficient and all particles that have passed during the measurement time during the sample measurement stage.
A means to count the number and calculate the expected number of image processing particles from the total number of particles passed
And the particle image effective coefficient to the expected number of image processing particles.
And calculate the effective image particle number by multiplying by the particle concentration correction coefficient
From the larger one of the sample particle image and the sample particle image
A flow-type particle image analysis device , comprising: a unit for determining only the number of image particles as a measured particle image . 5. The flow type particle image analyzer according to claim 4 , further comprising means for determining a particle image effective coefficient for each measurement mode when the flow type particle image analysis apparatus has a plurality of measurement modes. Particle image analyzer.