JPH0972842A - Method and apparatus for flow-type particle image analysis - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a method and an apparatus in which the accuracy of a measurement is improved by a method wherein a particle which has been detected by a particle detection system is made to correspond to a detected particle static image. SOLUTION: A sample is made to flow in a flow cell 100, the passage of particles is detected by a laser luminous flux 10, a pulse lamp 1 is light-emitted on the basis of their detection, and the static image of the particles is imaged by a TV camera 8 so as to be image-processed. In a sorted-result collection part at a particle analysis means 103, the particles detected by a particle detection means 102 and every particle image which has been image-processed by the particle analysis means 103 are made to correspond by using a particle size, and the particles in the sample are particle-analyzed.

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 device.
The present invention relates to a flow-type particle image analysis method and device suitable for analyzing cells and particles in blood or urine, which captures a static image of particles suspended in a flowing liquid sample and analyzes the particles.

[0002]

2. Description of the Related Art In conventional particle image analysis, classification and analysis of cells in blood and cells and particles in urine have been carried out by preparing a sample on a slide glass and observing with a microscope. In the case of urine, since the concentration of particles in the urine is low, the sample is observed after being centrifugally concentrated by a centrifuge in advance.

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 a long time to prepare a specimen, and furthermore, it is necessary to find particles while mechanically moving the microscope stage and to 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.

The method using the flow cytometer observes the fluorescence intensity and scattered light intensity from each particle in the sample, and has a processing capacity 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.
Techniques described in Japanese Patent Laid-Open No. 5 and Japanese Patent Laid-Open No. 63-94156 are known.

In the technique disclosed in Japanese Patent Publication No. 57-500995, a sample sample is flowed through a channel having a special shape into a wide image pickup area, a static particle image is taken by a flash lamp, and the image is taken. Particle analysis is performed using. In this method, a magnified image of sample particles is C-imaged using a microscope.
When projecting on a CD camera, a flash lamp, which is a pulse light source, periodically emits light in synchronization with the operation of the CCD camera. Since the emission time of the pulsed light source is short, it is possible to obtain a still image even when particles are flowing continuously, and moreover,
The CCD camera can take 30 still images per second.

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. This is a method in which the passage of particles is known in advance by a particle detection system, and when the particles reach a particle image capturing area, a flash lamp as a pulse light source is turned on at an appropriate timing. In this method, the light emission of the pulsed light source is not performed periodically, it is possible to detect the passage of particles and to capture a still particle image at the same time only, so the still particle images are efficiently collected, Captures meaningless images without particles even in the case of low-concentration sample specimens
There is no processing.

Further, in Japanese Patent Application Laid-Open No. 6-142086, as in the case of Japanese Patent Application Laid-Open No. 63-94156, a means for detecting particles in a sample flow is provided in addition to a stationary particle image system, and in addition, detection is performed. The actual number of particles in the sample particles from the total number of particles image-processed in the generated particle image,
It provides a means to obtain the number of particles for each classified type.

Further, Japanese Patent Laid-Open No. 7-83817 discloses that
Japanese Unexamined Patent Publication No. 6-142086 discloses a one-to-one correspondence method between detected particles and detected particle images, which is a problem.

[0013]

In a conventional particle image analyzer, generally, a still image of a sample particle flowing continuously is analyzed to count or classify the number of plural kinds of particles in a sample. In order to efficiently perform the above, a particle detection system for detecting passing particles is required in the static particle image capturing area or upstream thereof, as is done in the above-mentioned known example. That is, the pulse light source is turned on only when the sample particles pass, and a still image of the sample particles is captured. Since this method can process a measurement sample having a low particle concentration very efficiently, it is possible to increase the amount of measurement sample, shorten the analysis time, and improve the analysis accuracy.

However, in such a flow-type particle image analyzer, a particle detection system for detecting passing particles in a stationary particle image capturing area or an upstream thereof, and a stationary particle image by turning on a pulse light source only when the particles have passed. Although an image pickup system for picking up the image is included, the particle detection system and the image pickup system generally have different measurement principles. As a particle detection system, a method is generally used in which a laser beam is focused on a measurement sample flowing in a flow cell and irradiated, and light scattered light from particles that cross the laser beam is detected. The scattered light generally becomes an optical signal having an intensity proportional to the effective scattering cross section of the particle, and this optical signal is converted into an electric signal by a photodetector.

The magnitude of this optical signal is influenced by the optical refractive index of particles, absorption, size, internal state of particles, scattered light detection conditions, etc., and is necessarily morphological information of particles used in image processing. It does not completely match the particle diameter, perimeter, color information, and texture information.

If the particle detection system is set to have a particle detection condition above a predetermined particle detection level, there are particles that are small in size and do not reach the particle detection level due to light scattering in an actual image pickup system. Doing is something that often happens. As a result, the number of particles counted by the particle detection system and the number of particles that have been image-processed 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 reproducibility deteriorate Cause to cause.

Japanese Patent Application Laid-Open No. 7-83817 provides a method for correctly addressing these problems. The method shown therein has a one-to-one correspondence relationship between particles imaged in one image and particles detected by the particle detection system. Therefore, from the particle detection signal, the number of particles corresponding to the detection particles among the particles reflected in the image at the time when the pulse lamp light source is turned on is counted, and the count value and the particles detected in the image are detected particles. They are matched and distinguished from particles that are not considered detection particles.

In the above-mentioned method, a flow type particle image analysis method and a flow type particle image are provided, each of which has a particle detection system and an image pickup system, and the principle of the particle detection system and the principle of particle image discrimination of the image pickup system are different. 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 that can accurately know the particle existence ratio, particle density, and concentration information, and have good analysis accuracy.

However, in the above method, since the particles corresponding to the detected particles existing in one image are extracted from the particle detection signal and counted, a complicated time timing is required,
When the circuit configuration of this part becomes complicated, it is not possible to deal with the case where the timing relationship changes due to the flow velocity change such as pulsation of the sample liquid, when there are a plurality of measurement modes where the sample flow velocity is different, There are problems such as the need for circuit configurations of multiple systems.

When manufacturing a product, it is necessary to strictly adjust the particle detection pulse width of the particle detection system and the particle size of the image processing system, and the laser output, the optical system, and the detection sensitivity of the particle detection system. If the characteristics of each device are different, adjustment becomes complicated.

It is an object of the present invention to provide a flow-type particle image analysis method and apparatus suitable for performing particle analysis without substantial influence due to fluctuation factors in the particle detection system. .

[0022]

According to the present invention, a liquid sample is flowed through a flow cell and particles in the liquid sample that pass through a predetermined position of the flow cell during a measurement time are detected, The number is counted. Further, the number of particles to be statistically predicted is calculated from the counted number of particles.

On the other hand, the particles passing through the imaging area of the flow cell are imaged on the basis of the particle detection, whereby a still image of the particles passing through the imaging area is obtained. The resulting image is processed to obtain size data of the particles in the image and the particle of the larger particle size of the particles in the entire processed still image is the calculated particle. The number of particles to be analyzed is selected, and the particle analysis is performed on the selected particles.

According to this, even if there is a variation factor in the particle detection system, a flow type particle image analysis method and apparatus suitable for performing particle analysis without substantially affecting it are provided.

In selecting the particle having the larger particle size among the particles in all the processed still images by the calculated number of particles for particle analysis, the size data of the particles in all the processed still images are selected. The selection can be easily performed by rearranging in order of size.

The expected number of particles to be analyzed may be determined in consideration of measurement conditions in addition to the counted number of particles. There are various kinds of measurement conditions such as sample amount, sample flow velocity, measurement time, particle image capturing condition, etc., but it is possible to statistically predict the number of particles for particle analysis if the measurement conditions are determined. . As a result, even if the measurement conditions change, it is possible to deal with it.

Particles passing through a predetermined position are optically detected to generate a particle detection signal, and a particle detection signal of a predetermined level or higher is detected as a width of the particle detection signal at a predetermined level. The particle count may be performed by converting the pulse width into a pulse width corresponding to the pulse width and counting the pulse width of the pulse width of the predetermined pulse width or more. This is because even if the particle detection system and the image processing system have different particle detection principles, the pulse width of the particle detection signal and the particle size of the image, especially the particle size in the flow direction, correspond well.

The above-described particle detection and particle analysis processing may be performed on the same liquid sample in a plurality of measurement modes, and then the particle analysis results in all measurement modes may be integrated. For example, for a certain type of particle, if analysis is performed so that separate-frame particle analysis target particles in the first analysis mode and normal particle analysis target particles in the second analysis mode are performed, individual analysis modes are set after the analysis is completed. The analysis accuracy can be improved by summarizing the particle analysis results of (1) and organizing them as the particle analysis results of the entire one analysis sample.

When a plurality of different measurement modes are used, such as when the measurement conditions are different or the range of particles to be analyzed is different, different particle detection conditions, that is, the level of the particle detection signal, You may make it change the predetermined value of a pulse width. This is because it is possible to perform particle detection and particle analysis processing under optimum measurement conditions.

Among the particles in all the processed still images, particles other than the selected particles, that is, particles other than the normal particle analysis target particles are subjected to particle analysis in a separate frame, and the analysis result is the particle analysis result of the selected particles. It may be added to. According to this, the accuracy of particle analysis can be improved.

The expected number of particles to be analyzed may be compared with the number of processed images. By doing so, it becomes possible to cope with the processing when the number of particle images actually processed is smaller than the expected number of image particles.

It is also possible to calculate the expected number of image processing frames from the counted number of particles and compare this with the number of image frames actually processed. As a result of the comparison, if the number of frames predicted by calculation is extremely different from the number of frames actually subjected to image processing, it can be predicted that there is a problem in the particle detection system or the like. However, if they match within the range of statistical fluctuation, it can be considered that the particle detection system and the like are operating normally.

As described above, based on the total number of detected particles detected by the particle detection system, the expected number of particles to be analyzed and the expected number of image frames are compared with the actual number of particle images and the number of image frames. Check whether the device is operating normally,
You can see if there is a problem.

The present invention will be described more specifically in detail.
However, the specific examples and expressions considered to be limited in the description are not used to limit the present invention, but are merely used to help the understanding of the present invention. Please understand that it is a thing.

Particles that have passed through the particle detection area in the flow cell are detected, and after a certain period of time, when the detected particles arrive at a predetermined position in the still image capturing area, the flash lamp is turned on and the still image of the particle is displayed. To shoot.

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 image. This tendency becomes larger as the particle detection signal level becomes higher. Therefore, it is necessary to determine which of the captured particle images is the particle analysis target particle.

Here, as a detection principle of the particle detection system, when it is considered that particles which exceed the signal level of the particle detection signal and whose pulse width exceeds a predetermined value are detected, The 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.
Therefore, since the size of the particle in the sample flow direction of the captured individual particle image corresponds to the pulse width due to particle passage, it is possible to determine whether or not the particle is the particle to be analyzed based on the particle size determined by the pulse width. I can. Usually, the particle size in the flow direction is often used as one of the characteristic parameters necessary for particle analysis of particle images, specifically, classification and identification, so it is easy to determine whether the particles are particles to be analyzed. be able to.

However, the determination of the particles to be analyzed by particles based on such a standard is not always correct. If the particle detection level is constant, the pulse width changes depending on the size of the signal, because normalization based on the size of the particle detection signal is not performed. Also, the laser output of the particle detection system and the sensitivity of the detection system may differ from device to device, and the degree of optical system adjustment may vary.
In addition, fluctuations in the sample flow result in fluctuations in the pulse width. Considering such a pulse width variation factor, the particle detection operation cannot be said to be constant. Therefore,
When the product is commercialized, there is no one-to-one correspondence between the divided value of the particle size feature amount obtained from the particle image captured for each device and the pulse width detection value determined by the particle detection system. Further, it is not possible to deal with the problem simply by adjusting the particle detection system for each device. Further, it is necessary to adjust the particle detection conditions of the particle detection system every time the laser is replaced or the optical system is readjusted.

Therefore, how to determine which of the image-processed particles is the particle for particle analysis will be described without giving due consideration to these complicated adjustment operations.

It is premised that the same particle detection system as that described so far is used. That is, the scattered light from the particles that have passed through the particle detection system is detected by a photodetector,
Only particles having a predetermined detection level of the detection signal and a pulse width of the particles exceeding a predetermined value and having a size equal to or larger than a specified value are detected by the particle detection system.

The total number of detected particles that have passed the measurement time is counted, and this is taken as the total number of detected particles. The pulse lamp light source is turned on by particle detection, 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 analysis is extracted. Perform particle classification processing. This feature extraction and particle classification identification processing is executed for all image particles imaged at the measurement time.

Next, the number of particles to be analyzed, which is expected in the particle still image obtained by lighting the pulse lamp during the measurement time, is calculated from the total number of detected particles that have passed through the particle detection system during the measurement time. The number of detected image particles is calculated by the particle concentration in the sample, the measurement time, and the image capturing conditions of the TV camera. If this expected number of particles to be analyzed is N, the form of the calculation formula for this N will change depending on the measurement conditions, but it can be obtained by assuming that the occurrence event of particle passage goes through the Poisson process in statistics. I can.

Generally, N is given by the equation (1).

N = Nfγ {λ + exp (-λ (1-h))} (1) Nf is the total number of frame images that can be captured by the TV camera during the measurement time. Nf = Tm / Tf, where Tm is the measurement time, which is the measurement condition, and Tf is the frame time. The term γ is the ratio of the number of frames imaged by particle detection under actual sample conditions to Nf in Nf. γ
The content of 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.

The number in {..} 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 pulse lamp lighting by particle detection is performed, and a value between 0 and 1. is there. Normally, the condition is a value close to 1. λ is expressed by equation (2). The measurement condition Vm is the measurement sample volume. Nm is the total detected particle count value in the measurement volume.

Λ = (Nm / Vm) · Vg (2) A specific example of the form of the expression of γ is shown below.

Consider the case where the particle image is most efficiently captured without the restriction of the lighting timing of the pulse lamp, that is, the restriction that the lighting is possible only in the odd field or even field of the TV camera. After lighting the pulse lamp,
When the image stored in the CCD / TV camera over two fields is read into the image memory, the form of γ is
It is expressed by equation (3).

Γ = 2 · {1 − exp (−Nm · Tf / 2 · Tm)} / {2−exp (−Nm · Tf / 2 · Tm)} (3) In this equation, Tf is the value of the TV camera system. It represents one frame time.
Also, when 3 fields are required for image reading and image processing, the dead time increases by 1 field.
The form of the equation of γ in this case is given by equation (4).

Γ = 2 · {1 − exp (−Nm · Tf / 2 · Tm)} / {3 −2 · exp (-Nm · Tf / 2 · Tm)} (4) In any case, The form of the equation is particle concentration, measurement time,
It depends on the conditions of the TV camera imaging system.

The expected number N of particles to be analyzed, which is calculated as described above, gives a relatively correct value, although statistical variations are inevitable.

In the equation (1), the value of Nf · γ is
It corresponds to the average number of flash lamp emission during the measurement time, that is, the average number of frames of a TV image in which particles are imaged by particle detection. Therefore, the total number of images actually image-processed, that is, the total number Nf of frames in which particle images are captured
It is better to replace it with s in the actual device.
Processing is easy because you don't have to calculate the value of. When Nf · γ is replaced with Nfs in the equation (1), N is given by the equation (5).

N = Nfs · {λ + exp (−λ · (1−h))} (5) Depending on the measurement conditions, in equations (1) to (5),
It is also possible to derive an approximate expression and simplify the calculation process.

Next, for all image particles that have undergone image processing,
The features of the particle size, especially the particle size in the flow direction, are rearranged in order from the largest size. Next, by extracting the same number of expected particle analysis target particles as described above from the larger sorted particle size, it can be considered as particles detected by particle detection. This is the normal particle analysis target particle. The particles remaining in this comparison stage are considered as particles subject to separate frame analysis.

In the method described above, even if the particle detection conditions differ from device to device, the result appears as a fluctuation of all detected particles, and the result is that the number of detected image particles processed during the measurement time increases or decreases. Therefore, there is no difference between the particle type distribution of the particle detection system and the particle type distribution of the image-processed particles. That is, even if the detection conditions change, particle analysis can be performed without substantially affecting it.

For example, by setting the particle detection level low,
Even if the number of detected particles increases, the number of particles that the detected particles correspond to also increases in image processing, so even if particles near the detection level are not originally necessary, the results obtained from image processing Also, unnecessary particles are reflected, and the number of particle types originally in the sample can be correctly known.

As the particle image identification feature amount, morphology information or color information of the measurement particles, fluorescence intensity information, or a feature amount obtained by combining a plurality of these items of information can be used. It is desirable to change the particle detection conditions. Specifically, from the particle image, morphological information such as particle size, area, diameter, perimeter, and projection image, particle concentration, color information, particle internal structure, and fluorescence intensity are used. Information such as the presence of nuclei, cytoplasm, intracellular granules, etc., as in biological cells, can also be used.

By the above-mentioned method, it was found which of the particles existing in the image can be considered as the detection particles detected by the particle detection system, that is, the particles to be analyzed by the particles. The particle analysis process, specifically, the classification and identification process is executed. This is called normal particle analysis processing. Particles other than the particles to be subjected to particle analysis among the particles subjected to image processing are separately subjected to particle analysis processing.
This process is named another frame particle analysis process.

Further, in the particle analysis, particles having an image particle size smaller than a predesignated value are used as particles in another frame particle analysis processing from the beginning, so that the calculation time for changing the size of the image particle size data can be reduced. Can be shortened.

In particle detection, irrespective of still image capturing by particle detection, the number of particles having a detection level or more and a predetermined pulse width or more among particles that have passed through the particle detection system, that is, a total detection particle counter. Count the numbers. In this case, it is desirable to prevent erroneous counting of particle detection signals due to flash lamp light. To prevent this, the flashlamp light is prevented from reaching the particle detector or the flashlamp signal is not counted electrically. There is no problem even if the flash lamp light is also detected, and a false counting is prevented by a method of subtracting the number of times of lamp light emission at the end of the measurement.

When processing dense particle samples,
Since the counting of particles occurs in the particle detection system due to the dead time accompanying the image reading, the total number of particles in the count value of one image processed during the constant measurement time is the particle detection system during the constant measurement time. It does not always match the count value of all the detected particles that have passed through. However, although the shape varies depending on the measurement conditions, the number of particles to be analyzed as a predicted value can be known by using a calculation formula such as formula (1).

Further, since the particles to be subjected to the separate frame particle analysis processing are particles below the detection level of the particle detection system, they are not included in the total particle detection count value.

When particles having a larger particle size are selected by the number of particles to be subjected to particle analysis from particles in all processed still images, particle analysis processing is performed on the selected particles.
Specifically, pattern recognition and identification processing is performed to determine the type of particle. By performing this operation for all the images, the particle existence ratio of each type in the sample is determined. Particles that are present in the captured still particle image and that are lower than the particle detection level cannot be processed because they are not included in the total number of particles detected by the particle detection system.

When the particle existence ratio is determined, the total number of particles detected is separately measured by the particle detection counter.
Particle detection By multiplying the total number of particles by the particle existence ratio,
It is possible to calculate the number of particles for each type in the sample. Further, the particle concentration per unit volume can be calculated from the number of particles of each type.

In the case of an example of irradiating the sample stream with a laser beam as the particle detecting means to detect light scattering from particles in the sample, when the beam in the sample crosses the laser beam condensed and wide, Are scattered and received by the photodetector. The size of the particle detection pulse signal converted into the electric signal includes the size information of the particle. In particular, the time it takes to pass the particle detection system corresponds to the particle detection pulse signal. This particle detection pulse signal actually corresponds to the particle size plus the spot size at the particle detection position of the laser beam.

Next, the separate frame particle analysis processing will be described.

In addition to the normal particle analysis processing, particle analysis processing, specifically, classification and identification processing is executed for unprocessed particles remaining in the captured still particle image. These unprocessed particles are particles that are not detected by the particle detection system and are present in the captured still particle image. But,
Important particles may be contained in these untreated particles, and it is not preferable to discard the information.

To determine the concentration and quantity of untreated particles in a sample, the number per unit volume, in fact, the number of particles per sample volume corresponding to all images after processing all images was calculated. Then, the number of particles occupying the sample, the ratio of the number of particles, and the like are determined by reconverting the values for all the measurement samples. Thus, it is necessary to calculate the sample volume corresponding to the image processed sample.

At the end of the measurement, the results of the normal particle analysis processing and the separate frame particle analysis processing are combined to obtain the final result. In the particle analysis result in another frame, there are particles that should originally be included in the normal particle analysis result, and it is necessary to add them to the normal particle analysis result. The results are also reported when particles that are exceptionally important but are not included in the normal particle analysis process are included.

When the particle size is small and the signal level is the same as the noise signal for detection by particle detection, or when the number of particles is very large and the analysis image becomes huge, the particle detection level is increased and analysis is performed. A method that reduces the number of static particle images is preferable.

By performing the above-mentioned series of image processing on the measurement sample, the existence ratio and the number of particles for each particle type in the sample can be determined. From these values, the particle concentration per unit volume in the measurement sample, or the number of particles converted into a microscopic field is known. The number of particles in terms of the microscope field is the average number of particles present in the microscope field with an appropriate magnification, and it is necessary to set the sample volume corresponding to the microscope field in advance.

When the separate-frame particle analysis processing is executed, the sample volume to be image-processed is required to maintain the measurement accuracy. However, when the number of particles detected by the particle detection system is small, a sufficient number of still particle images cannot be secured, and as a result, a sufficient volume of a sample to be subjected to image processing may not be obtained.

Since the separate frame particle analysis processing is performed on the basis of the imaged still particle image, when the ratio of the original particles for normal particle analysis processing to exist in the sample is small, the image processed sample volume is used as the basis. Since the particle concentration in the separate particle analysis process is calculated, there is a risk that errors will increase. This is because there are not many particles in the sample, and there is no problem in analysis accuracy.

In order to perform the separate-frame particle analysis when the volume of the sample to be image-processed in this way is not sufficient, the separate-frame particle analysis process is stopped or the separate-frame particle analysis process is performed because the measurement accuracy cannot be sufficiently obtained. It is necessary to inform the operator that the measurement accuracy is poor and the data is unreliable even after performing. However, it is preferable to report the case where particularly important particles are present among particles to be subjected to the separate-frame particle analysis processing regardless of the measurement accuracy.

Separate frame particle analysis The total number of particles processed was counted,
If the number of particles is equal to or more than the preset number of particles, it is determined that the separate frame identification processing result is valid. If the number of particles has not been reached, the validity of the measurement result is determined by judging that the measurement result is invalid or displaying the degree of measurement accuracy. In this case, the reproducibility of the number of particles for each type may be used as a criterion, or the number of processed images may be simply used as the reference, or the sample volume of another frame analysis processing may be used as the reference. It is desirable to set these criteria before measurement. Furthermore, when the measurement result in the separate frame particle analysis processing does not meet the above-described determination criteria, a means for notifying the operator is required. It is better to add comments or symbols after the measurement results.

In addition, the particle analysis result, specifically, the classification result is provided for the particles that provide the operator with important information due to the presence of even one specific particle rather than the risk in the above-described separate frame particle analysis processing. Is output as is.
In this way, the problem that measurement results cannot be obtained for exceptional particles does not occur.

In addition, when the particles to be subjected to the normal particle analysis processing exist in the particles processed by the above-mentioned separate frame particle analysis processing, the particle concentration per unit volume is calculated, and then the particles are incorporated into the normal particle analysis processed particles. The particle analysis accuracy of particles, that is, the classification accuracy is improved. Also in this case, since the risk of the measurement accuracy in the above-mentioned separate frame particle analysis processing is taken into consideration and incorporated into the normal particle analysis result, the incorporation does not reduce the measurement accuracy.

To know the number of classifications and the classification ratio of all the particles detected by selecting the particles to be subjected to the normal particle analysis processing from the images picked up by all the particle detections and further executing the separate frame particle analysis processing. Can be done.

In the above description, the analysis conditions were considered to be constant for the same analysis sample, but in the actual sample analysis, the analysis mode may be changed. Even when there are a plurality of such analysis modes, the above-described normal particle analysis processing and separate frame particle analysis processing are performed for each analysis mode.

For example, for a certain type of particle, if the analysis is carried out so that the particles to be analyzed in different frames in the first analysis mode and the particles to be analyzed in the normal particle mode in the second analysis mode, the individual particles are analyzed after the analysis is completed. The analysis accuracy can be improved by integrating the particle analysis results in the analysis mode, that is, the classification and identification results, and organizing them as the particle analysis results of the entire one analysis sample.

When there are a plurality of different measurement modes, such as when the measurement conditions are different or the range of particles to be analyzed is different, different particle detection conditions, that is, the pulse level of the particle detection signal, are obtained. The predetermined value of pulse width can be changed, and as a result, the particle detection condition can be optimized.

Further, in a plurality of different measurement modes, if there is provided a means for changing and having a plurality of values of a predetermined particle size corresponding to the particle analysis unit corresponding to different particle detection conditions, the measurement condition And the accuracy of particle analysis can be improved.

From the count value of the total number of particles that have passed through the particle detection unit during the measurement time, the number of particles to be analyzed, which is expected from the total number of particles that have passed, can be calculated.
It is compared with the total number of image-processed particle images. As a result of comparison, there is no problem if the actual number of particles subjected to image processing is larger than the number of particles targeted for particle analysis predicted by calculation. However, if the number of particles that have undergone image processing is small due to statistical fluctuations or device malfunctions, a state determination is made as to whether there is a statistical error or device malfunction. If there is no problem, the number of particles is corrected based on the normal classification result and classification ratio. If there is a problem, the particle number correction is stopped and the operator is notified that there is a problem.

The same thing can be done with respect to the total number of frames in which particle images were taken during the measurement time. The expected number of image processing frames can be calculated from the count value of all particles that have passed through the particle detection unit during the measurement time.By comparing this value with the number of image frames actually processed, the device status You can know As a result of comparing the number of image frames, if the number of frames predicted by calculation is extremely different from the actual number of frames processed by image processing, it is expected that there is a problem in the particle detection system and pulse lamp lighting system. . However, if they match within the range of statistical fluctuation, it is considered that the particle detection system and the pulse lamp lighting system are operating normally. The calculation of the expected number of image frames is given by Nf · γ in equation (1), which differs depending on the measurement conditions.

Thus, based on the total number of detected particles detected by the particle detection system, the expected number of image-processed particle images and the expected number of image frames are compared with the actual number of particle images and the number of image frames. By doing so, it is possible to know whether the device is operating normally or a problem has occurred.

Other features and benefits of the present invention will be apparent from the description of the embodiments of the invention made with reference to the drawings.

[0086]

DESCRIPTION OF THE PREFERRED EMBODIMENTS In FIG. 1, 100 is a flow cell, 101 is an image pickup means, 102 is a particle detection means, and 1 is a particle detection means.
Reference numeral 03 is a particle analyzing means, 1 is a flash lamp, 1a is a flash lamp driving circuit, 2 is a field lens, 3 is a microscope condenser lens, 5 is a microscope objective lens, 6 is an image forming position, 7 is a projection lens, and 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
Is a reflecting mirror, 19 is a minute reflecting mirror, 20 is a beam splitter, 21 is a diaphragm, 22 is a light detection 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. , 27 is a feature extraction circuit,
28 is an identification circuit, 29 is a central control unit, 40 is a particle number analysis unit, and 50 is a display unit.

The flow-type particle image analyzer according to the embodiment of FIG. 1 comprises a flow cell 100 to which a sample liquid in which particles are suspended is supplied, and an image pickup means 101 for picking up an image of particles in the sample liquid flowing through the flow cell 100. And particle detection means 102 for detecting particles to be imaged, and particle analysis means 103 for analyzing the imaged particles.

The sheath liquid 111 is supplied to the flow cell 100 together with the flow of the sample liquid 110.
The flow forms a flow surrounded by the sheath liquid 111. The flow of the sample liquid 110 is the image pickup means 101 (described later).
A stable steady flow having a flat cross section in a direction perpendicular to the microscope optical axis 9 (described later), that is, a so-called sheath flow, which flows down from the center of the flow cell 100 downward from above the paper surface. The flow velocity of the flow 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 includes a flash lamp 1 which is a pulse light source, a flash lamp drive circuit 1a for causing the flash lamp 1 to emit light, and a field which makes a pulse light flux from the flash lamp 1 parallel. Microscope condenser lens 3 for focusing the lens 2 and the parallel pulsed light flux 10 from the field lens 2 on the flow of the sample liquid 110 in the flow cell 100.
A microscope objective lens 5 for condensing the pulsed light flux irradiated on the flow of the sample liquid 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. TV camera 8
For this, a CCD camera or the like with a small afterimage is generally used.

The particle detecting means 102 includes a semiconductor laser 15 that emits laser light as detection light, and a semiconductor laser 1.
Collimator lens 16 for converting laser light from laser beam 5 into parallel laser light flux 14, cylindrical lens 17 for focusing only one direction of laser light flux 14 from collimator lens 16, and reflection for reflecting laser light flux 14 from cylindrical lens 17 A micro-reflecting mirror 19 which is located between the mirror 18, the microscope condenser lens 3 and the flow cell 100 and guides the laser light flux 14 from the reflecting mirror 18 to the vicinity of the upstream side of the image capturing area (imaging area) on the flow of the sample liquid 110. It consists of and.

Further, a microscope objective lens 5 for condensing the scattered light of the laser light flux 14 by the particles (this microscope objective lens 5 is also used as the image forming microscope objective lens 5 of the image pickup means 101) and a microscope objective. The beam splitter 20 that reflects the scattered light condensed by the lens 5 and the scattered light from the beam splitter 20 are received via the diaphragm 21,
Photodetection circuit 22 that outputs an electrical signal based on the intensity
And a flash lamp light emission control circuit 23 for operating the flash lamp drive circuit 1a based on an electric signal from the light detection circuit 22.

The particle analysis means 103 is an AD for converting the image data signal transferred from the TV camera 8 into a digital signal.
A converter 24, an image memory 25 that stores data based on a signal from the AD converter 24 at a predetermined address, an image processing control circuit 26 that controls writing and reading of data in the image memory, and an image memory 25. A feature extraction circuit 27 that performs image processing based on a signal from the device and performs particle number and classification, an identification circuit 28, and a central control unit 29.
And a particle number analysis unit 40 that determines the number of particles in the sample liquid 110, and a display unit 50 that displays the number of particles and the classification as a result of image processing.

The central control unit 29 is connected to the photographing condition of the TV camera 8, the condition of the sample liquid flow of the flow cell 100, the control of the image processing control circuit 26, the storage of the image processing result from the identification circuit 28, and the particle number analysis unit 40. Data transfer and display 5
It is configured to display data to 0.

Next, referring to FIG. 2, the particle number analysis unit 40
The configuration of will be described. In the figure, the same reference numerals as those in FIG. 1 denote the same parts, and a detailed description thereof will be omitted.

In FIG. 2, 41 is a pulse level detection,
42 is a pulse width detection, 43 is a total particle counting circuit, 44 is an image processing particle number calculation unit, 50 is a display unit, 61 is a particle image size data unit, 62 is a particle classification result unit, 63 is a total image number counting unit, 64 is an analysis condition setting unit, 65 is a size rearrangement unit, 66 is a measurement condition evaluation unit, 67 is a particle classification result corresponding unit, 68 is a normal classification result (normal particle analysis result) processing unit,
69 is a separate frame classification result (separate frame particle analysis result) processing unit, 70
Is a counting result (particle analysis result) counting unit.

In FIG. 2, a pulse level detection circuit 41 constituting the particle number analysis unit 40 detects a particle detection signal from the photodetection circuit 22 which is equal to or higher than a predetermined signal level. Also,
The pulse width detection circuit 42 is a pulse width detection circuit that detects a particle detection signal having a predetermined pulse width or more in the output signal of the pulse level detection circuit 41. The total particle counting circuit 43
The total number of detected particles that are above a predetermined signal level and that the pulse width is above a predetermined value and that are detected during the entire analysis time is counted. More specifically, when the particle detection signal from the photodetection circuit 22 is introduced to the pulse level detection circuit 41, the particle detection signal of a predetermined level or more that can be changed is specified by the particle detection signal. Converted to a pulse with a pulse width corresponding to the width at the level.
When the converted pulse is introduced into the pulse width detection circuit 42, a pulse having a pulse width equal to or larger than a predetermined pulse width that can be changed is extracted, and the extracted pulse is the total particle counting circuit 43. Is counted as the total number of detected particles. The setting of the value of the predetermined level and the predetermined pulse width may be performed by the setting from the central control unit 29.

The analysis condition setting unit 64 is set to sample analysis conditions, the total image counting unit 63 counts all images, that is, the count amount is incremented by 1 each time one image is processed, and the particle classification result unit 62 is set to count particle images. As a result of the particle classification of the processed particle image, the parameter data which is the particle size feature amount of each particle processed during the measurement time is set in the particle image size parameter section 61 from the central control section 29. Further, the particle size parameter unit 61 also performs a process of setting a particle image as a target particle for another frame from the beginning for an image particle that has a particularly small particle image size and does not need to correspond to the detected particle from the beginning. Based on the data of the particle size parameter section 61, the size sorting section 65 sorts the image particle sizes in size.

On the other hand, since the total particle counting circuit 43 counts the total number of particles detected in the measurement time, the expected particle image actually image-processed within the measurement time is based on this value. Calculate the total number of using a predetermined formula. Although the calculation formula depends on the measurement condition, the measurement condition set in the measurement condition setting unit 64 is used. An example of the formula is
Equations (1) to (4) are shown. The image processing particle number calculation unit 44 has a function of calculating an expected value of the total number of frames imaged by the TV camera during the measurement time.

Based on the total number of predicted particle images obtained by calculation, among the particle image size rearrangement data in the size rearrangement unit 65, the total number of predicted particle images from the larger one is regarded as the particle image in which the particle is detected. Considered normal particle analysis processing particles, specifically, normal classified particles. After taking this correspondence relationship, the rest are considered as particles in another frame analysis processing, specifically, particles in another frame classification. Separately classified particles are particles that have not been detected by the particle detection system, and are small particles that happen to appear on the screen captured by the TV camera.

Based on the result of the particle classification result correspondence unit 67, the particle classification results set in the particle classification result unit 62 are sorted into the target particles of the regular classification process and the target particles of the separate frame classification process, respectively. It is set in the regular classification result processing unit 68 or the separate frame classification result processing unit 69.

Finally, the total particle circuit 4 for counting the total number of particles
1. Based on the particle identification processing results of the total image number counting unit 63 for counting the number of all processed images, the analysis condition setting unit 64, the normal classification result unit 68, and the separate frame classification result unit 69, the classification result totaling unit 70 determines Processing results are compiled. The result is transmitted to the central control unit 29,
The information is output and displayed on the display unit 50 as needed. Also,
In the measurement condition evaluation unit 68, the particle analysis is performed by comparing the measurement condition, the total number of particle images and the total number of image frames predicted by the image processing particle number calculation unit 44 with the number of actually processed image particles and the number of image frames. It is judged whether there is a defect in the device, especially in the particle detection system and the pulse lamp lighting control system, or whether the measurement is normally performed within the statistical error range. The control unit 29 is informed.

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 operation of the image pickup means 101 and the particle detection means 102 will be described. The semiconductor laser 15 constantly oscillates continuously and always observes that particles in the flow of the sample liquid 110 pass through the detection region.

Laser beam 1 from the semiconductor laser 15
The collimator lens 16 converts the laser beam 4 into parallel laser beams, and the cylindrical lens 17 focuses the beam 14 in only one direction.

The laser beam 14 is reflected by the reflecting mirror 18 and the minute reflecting mirror 19 and is irradiated onto the flow of the sample liquid 110 in the flow cell 100. This irradiation position is a particle detection position where the laser light flux 14 is focused by the cylindrical lens 17, and is a position in the vicinity of the upstream side of the image capturing region (imaging region) on the flow of the sample liquid 110.

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 is reflected by the beam splitter 20, received by the photodetector circuit 22, and converted into an electric signal based on its intensity.

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

When the particles are detected, it is assumed that the particles to be image-processed have passed, and the detection signal is transmitted to the flash lamp emission control circuit 23.

The predetermined delay time is determined by the distance between the particle detection position and the image capturing area, the flow velocity of the sample liquid 110, and the like.

When the flash light emission signal is transmitted 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 optical axis 9 of the microscope, becomes parallel light by the field lens 2, and is focused by the condenser lens 3 of the microscope to be focused on the flow cell 100.
The sample liquid 110 inside is irradiated onto the flow.

The width of the pulse beam 10 is limited by the field stop 11 and the aperture stop 12.

The sample liquid 11 in the flow cell 100
The pulsed light flux radiated to the zero flow is the objective lens 5 of the microscope.
The light is focused at and an image is formed 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.

The particle analysis means 103 will be described.

The image data signal captured by the TV camera 8 and converted by the interlace system is converted into a digital signal by the AD converter 24, and the data based on this is controlled by the image processing control circuit 26 and the image memory 25.
At a predetermined address.

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, and is subjected to image processing. Is memorized.

The contents stored in the central control unit 29 are as follows.
It is a particle identification feature parameter used for particle classification and particle classification result data.

The classification and identification processing of particles is automatically performed by the pattern recognition processing 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 central control unit 29 to the particle number analysis unit 40. In the particle number analysis unit 40, based on the particle detection signal from the central control unit 29, the photodetection circuit 22 and the control signal from the image processing control circuit 26, the correspondence between the detected particles and the particle classification result is examined, The final classification and classification result of the particle image is summarized.

Next, the particle number analysis unit 40 will be described with reference to FIG.

The total particle counting circuit 43 counts particles having a predetermined size or more from the detected particle signal through the pulse level detecting section 41 and the pulse width detecting section 42 based on the particle detecting signal from the photodetecting circuit 22. Then, at the end of the measurement, the total number of detected particles is obtained.

The data of the sample analysis conditions set in the measurement condition setting unit 64 is determined by the processing contents in the classification result totaling unit 70.

Basically, data such as analysis sample volume, analysis time, sample volume equivalent to one static particle image, sample volume equivalent to one field of view of the microscope, analysis mode, etc. are required, but depending on the classification processing contents, it is described above. Some items may be omitted in the data.

In the total image counting unit 63, the count amount is incremented by 1 each time one image is processed, and the total number of images processed in the analysis time is stored at the end of the analysis.

The particle classification result of the image-processed still particle image is set in the particle classification result section 62 via the identification circuit 28 and the central control section 29. At the same time, of the feature amounts used for classification and identification of the classified particles, the feature amount closest to the detection principle of the particle detection system is set in the particle image size parameter 61 for all the image-processed particles. This is necessary to correspond to the particle detection conditions of the detection system.

Next, in the particle image size parameter section 61, based on the size data, for particles that are too small to be regarded as detected particles from the beginning,
Here, the particles are classified according to another frame.

The particle image size data stored in the image size parameter section 61 is rearranged in the order of larger size by the size rearrangement 65. Of the results of rearranging the particles in the descending order of image size, from the larger one, only the expected number of image-processed particles calculated by the image-processing particle number calculation unit 44 is regarded as a normal particle detected particle, and the rest is classified by another frame. The processing corresponding to the target particles is performed by the particle classification result correspondence unit 67. The results are totaled by the regular classification result processing unit 68 and the separate frame classification result processing unit 69, respectively.

When the analysis of one sample is completed, the total number of particles above the particle detection level for the analyzed sample is the total particle counting circuit 41, the total number of image-processed images is the total image number counting unit 63, and the normal identification processing target particles. Is the regular classification result part 68
In addition, the particles subject to the separate frame identification processing are the separate frame classification result part 69.
Will be stored in.

Finally, the measurement conditions of the measurement condition setting unit 64 and the total number-of-images data of the total image number counting unit 63 are related to the measurement conditions of the normal classification result processing unit 68 and the separate frame classification result processing unit 69. Using the information, the classification result totaling unit 70 calculates the number of particles classified, the particle concentration, the number of particles converted into a microscopic field, and the like.

[0130] The final result is sorted by the classification result totaling unit 70, the result is returned to the central control unit 29, and output and displayed on the display unit 50 as necessary.

The order of particle analysis will be described with reference to the flowchart shown in FIG.

In step 1, when the analysis of the particle sample is started, the analysis conditions are first set in the analysis condition setting unit 64.
Is set to The analysis conditions to be set include the division between normal classification and separate frame classification, analysis time, analysis amount, classification type, analysis mode, sample measurement conditions, and the like.

When the measurement is started, in step 2,
Particles that pass one after another are detected by a particle detection system, and the number of detected particles is counted.

When particles are detected in step 3,
Particle images are taken by a TV camera, feature extraction and pattern recognition processing are performed for each particle, and individual particle classification processing is performed. The classification result and the particle image size parameter are stored in the particle classification result part 62 and the particle image size parameter part 65, and the process proceeds to step 4.

In step 4, it is determined whether the size information stored in the particle image size parameter 65 indicates that the image size is small and the particle is not detected by the particle detection system. Step 5 if size is small
From the beginning, separate particles are used. Go to step 6.

In step 6, it is judged whether or not the measurement is completed, and if it is not completed, the procedure returns to step 2,
Repeat until the measurement is completed, and proceed to step 7 when the measurement is completed.

In step 7, the image processing particle number calculation unit 4
In 4, calculate the number of particle images expected from the total number of detected particles.

When calculating the expected number of particle images,
The number of particle images N may be calculated by the equation (1), or the total number of images actually image-processed by the equation (5), which is a simple process, that is, the total number of frames Nfs of the particle images, Nfs. You may decide.

Then, the process proceeds to step 8.

In step 8, the particle image size parameters corresponding to the detection are rearranged by the size rearrangement 65 in descending order of size. Go to step 9.

In step 9, from the size parameter data in which the particle images are rearranged, the predicted number of calculated particle image numbers is taken out from the larger one, and the taken out particles are regarded as normal classification processed particles.

In step 10, the remaining particles which cannot be detected by the particle detection system in step 9 are regarded as the particles to be classified by another frame, and the process proceeds to step 11.

In step 11, it is determined whether the above-described series of processing has been normally performed, or the measurement condition evaluation unit 66 is based on the measurement result.
Will be done at. The expected particle image processing number is compared with the actual number of particle images, or the expected image frame number is compared with the actual image frame number. If the difference exceeds the statistically expected error, it is considered that there is a problem somewhere in the measurement system, the cause is analyzed, and the result is displayed on the display unit 50 in step 12. , The process is ended.

In step 13, in the classification result totaling unit 70, the total number of particles above the particle detection level regarding the analysis sample is set by the total particle counting circuit 41, and the total number of images image-processed by the total image number counting unit 63 is set. And proceed to step 14.

In step 14, the cumulative classification number of the particles for the regular identification processing is the normal classification result processing section 68, and the cumulative classification number of the particles for the separate frame identification processing is the separate frame classification result processing section 69.
The data stored in each of the data are read out, and the classification result totaling unit 70 performs the normal identification process / the separate frame identification process to determine the number of particles in the analysis sample, and the step 1
Go to 5.

In step 15, since it may be expected that an analysis error will increase depending on the number of particles subjected to the separate frame identification processing and the sample volume subjected to the image processing, the result of the separate frame identification processing is checked to determine the number of particles, Go to step 16.

In step 16, the abundance ratio of each particle is calculated from the number of each particle of the normal identification processing result and the separate frame identification processing result. Next, with respect to the particles to be subjected to regular identification processing, the number of detected particles in the sample is determined by multiplying the total number of detected particles by the above-mentioned ratio.

Next, regarding the separate frame identification processing result, first, from the total number of processed images and the sample volume corresponding to one still particle image,
The total volume of the image-processed sample is calculated, and the number of particles in the total volume of the analysis sample is determined based on the abundance ratio of the particles to be subjected to identification processing in each frame.

Based on the results of the normal identification process and the separate frame identification process, particles existing in duplicate in both are sorted into one particle type, and non-analyzed particles are discarded. Based on these analysis results, the particle concentration in the sample and the field-of-view converted particle number are calculated, and the process proceeds to step 17.

At step 17, the analysis result is output and the process is terminated.

When analyzing a sample in a plurality of analysis modes, the operation is executed in each analysis mode, and the analysis data of each mode is integrated at the final stage to obtain the final result. In this case, when there are a plurality of different measurement modes, such as when the measurement conditions are different or when the range of the particles to be analyzed is different, different particle detection conditions, that is, the pulse level or pulse of the particle detection signal. Change the given width value.

Further, in a plurality of different measurement modes, a plurality of predetermined particle size values corresponding to the particle analysis unit are provided corresponding to different particle detection conditions, and a means for changing the values is provided so that the measurement conditions can be improved. The diversity of
It is possible to improve the accuracy of particle analysis.

The different measurement modes may be, for example, as follows.

Measurement mode 1: Particles to be measured are all particles in the sample.

Measurement mode 2: Particles to be measured are particles larger than a certain size in the sample.

The measurement mode may also be as follows.

Measurement mode 1: A particle having a small particle size is measured.

Measurement mode 2: A particle having a large particle size is measured.

It is necessary to have a means for preventing erroneous counting of particle detection signals due to flash lamp light for capturing a still particle image. For this purpose, there is a means for removing the influence of flash lamp emission. As a method, an optical system is considered so that the flash lamp light does not reach the particle detector, or the flash lamp signal is not electrically counted. On the contrary, it is possible to correct the erroneous count by a method of detecting the flash lamp light and subtracting the number of times of lamp light emission at the end of the analysis. This is performed on the count result of one image particle number and total particle number.

A flow-type particle image analysis method and a flow-type particle image analysis apparatus according to an embodiment of the present invention include a biological sample and cells suspended in a liquid, blood cell components such as red blood cells and white blood cells in blood, or urine. It is effective in classifying and analyzing the urinary sediment components present in it.

In particular, when counting the number of particles of the urinary sediment component in urine and classifying the particles, the number of particles present 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.

However, in the urinary sediment component, the types and sizes of particles are extremely varied, and it is impossible to accurately correspond the detected particles of the particle detection system and the particles in the still particle image. Accurate particle counting, particle classification,
Particle concentration can be analyzed.

In the above embodiment, the case where the still image of the particles contained in the sample liquid continuously flowing in the flow cell is picked up and the image is analyzed is described. It can also be applied to particle image analysis of moving slide specimens.

Further, as the particle detection of the particle detection system, the case where 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 has been described. It is possible to use transmitted light, 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.

Further, when the time for the sample particles to pass through the particle detection system, that is, the pulse width of the detection pulse signal converted into an electric signal is used for particle detection, the size information in the particle flow direction should be reflected. You can

When considering the particle transit time that crosses the laser beam as a reference for particle detection, the particle detection size must be determined in consideration of the spread of the laser beam. It is necessary to determine the value obtained by subtracting the spread of the laser light flux as the particle detection pulse width, or to carry out the size comparison in consideration of the spread of the laser light flux in the image-processed particle size value.

When the particle detection of the particle detection system and the captured still particle image are associated with each other, the particle diameter information or the projection information is appropriate.

Up to this point, interlaced scanning has been described as a TV camera imaging method in image capturing, but the operation and effects of the present invention do not change even in the non-interlaced scanning method.

According to the embodiment described above, a flow type particle image analysis method and apparatus suitable for performing particle analysis without substantial influence due to fluctuation factors in the particle detection system are provided. To be done. Further, the detection particles and the particles in the still particle image are made to correspond, and even if particles other than the detection particles of the particle detection system are present in the still particle image of the image capturing system, the number of particles of each type in the sample, Particle abundance ratio, particle density,
A flow-type particle image analysis method and device that can accurately know concentration information and have good analysis accuracy can be provided.

[Brief description of drawings]

FIG. 1 is an overall configuration diagram of a flow type particle image analysis apparatus showing an embodiment of the present invention.

FIG. 2 is a block diagram showing a configuration of a particle number analysis unit in the embodiment of FIG.

FIG. 3 is a flowchart for explaining the processing contents of a particle number analysis unit in FIG.

[Explanation of symbols]

 1 Flash Lamp 1a Flash Lamp Driving 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 14 Micro Reflector 15 Semiconductor Laser 17 Collimator Lens 18 Cylindrical Lens 19 Reflection Mirror 20 Beam splitter 21 Aperture 22 Photodetection 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 number analysis unit 41 Pulse level detection unit 42 Pulse width detection unit 43 Total particle counting unit 44 Image processing particle number calculation unit 50 Display unit 61 Particle image size parameter unit 62 Particle classification result unit 63 Total image number counting unit 64 Measurement condition setting unit 65 Size sorting Part 66 measurement condition setting unit 67 particle classification results corresponding portion 68 normal classification result processing unit 69 separate frame classification result processing unit 70 classification result counting unit 100 the flow cell 101 imaging unit 102 particle detection means 103 particle analysis means 110 sample liquid 111 sheath liquid

Claims (13)

[Claims] 1. A flow-type particle image analysis method including the following steps. (1) flowing a liquid sample into a flow cell, detecting particles in the liquid sample passing through a predetermined position of the flow cell during a measurement time and counting the number, (2) counting Calculating the number of particles to be statistically predicted from the number of particles analyzed, (3) obtaining a still image by imaging the particles passing through the imaging area of the flow cell based on the particle detection. , (4) processing the still image to obtain size data of particles in the image, (5) based on the size data, determining the particle size of the particles in the entire processed still image. Selecting larger particles by the number of particles calculated as the particle analysis target, and performing particle analysis on the selected particles. 2. The flow according to claim 1, wherein the step (5) is executed after the size data of particles in all the processed still images are rearranged in order of size.
Particle image analyzer. 3. The flow type particle according to claim 1, wherein the expected number of particles to be analyzed is determined in consideration of measurement conditions in addition to the counted number of particles. Image analysis method. 4. The flow-type particle image analysis method according to claim 1, wherein the expected number of particles to be subjected to particle analysis is given by the following equation, where N is set. N = Nf · γ · {λ + exp (-λ · (1 -h))} where Nf is the total number of frame images that can be captured during the measurement time, and γ is the frame image captured under actual sample conditions. The ratio of the number of mums to Nf, λ is the average number of particles present in the volume corresponding to the volume of the image capturing area, and h is a value determined by the position in the image capturing area where the image is captured. 5. The flow type particle image analysis method according to claim 4, wherein the γ is given by the following equation. γ = 2 · {1-exp (-Nm · Tf / 2 · Tm)} / {2 − exp (-Nm
* Tf / 2 * Tm)} Here, Nm is the total detected particle count value in the measurement volume, Tf is the time of one frame of the imaging system, and Tm is the measurement time. 6. The steps (1) to (5) are performed in a plurality of measurement modes for the same liquid sample, and then the particle analysis results in all the measurement modes are combined. The flow type particle image analysis method according to claim 1 or 2. 7. A first means for flowing a liquid sample through a flow cell, detecting particles in the liquid sample passing through a predetermined position of the flow cell during a measurement time and counting the number of the particles. The second means for obtaining a still image by imaging the particles passing through the imaging region of the flow cell based on the detection, and the number of particles to be analyzed statistically from the counted number of particles are calculated. Along with the calculation, the still image is processed to obtain the size data of the particles in the image, and based on the size data, one of the particles in the processed still images having the larger particle size is calculated. A flow-type particle image analysis device, comprising: a third means for selecting particles by the calculated number of particles to be analyzed, and performing particle analysis on the selected particles. 8. The means for optically detecting particles passing through the predetermined position to generate a particle detection signal, and the particle detection signal of a predetermined level or higher among the particle detection signals. And a means for converting a pulse having a pulse width corresponding to the width of the particle detection signal at the predetermined level, and counting the pulses having a pulse width of a predetermined pulse width or more among the converted pulses, The flow type particle image analyzer according to claim 7, wherein the detected particles are counted by counting pulses having a pulse width equal to or larger than the predetermined width. 9. The flow according to claim 8, wherein the predetermined level and the predetermined pulse width can be changed.
Particle image analyzer. 10. The third means calculates either the number of particles per sample volume or the ratio for each of all the image-processed images, and calculates the number of particles per total sample volume based on the calculation. The flow type particle image analysis device according to claim 7. 11. The third means performs particle analysis on particles other than the selected particles among particles in the processed still images in a separate frame, and the analysis result is a particle of the selected particles. The flow type particle image analysis device according to claim 7, wherein the flow type particle image analysis device is added to the analysis result. 12. The flow-type particle image according to claim 7, wherein the third means is configured to compare the number of particles to be analyzed with the number of the image-processed images. Analyzer. 13. The third means is configured to calculate an image processing frame number predicted from the counted number of particles and compare this with an actually processed image processing frame number. The flow-type particle image analysis device according to claim 7, wherein the flow-type particle image analysis device is provided.