JP2826448B2 - Flow type particle image analysis method and flow type particle image analysis device - 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 more particularly to a particle image analysis for capturing an image of a stationary particle suspended in a flowing liquid and analyzing the particles. Things. In particular, it is used to analyze cells or particles in blood or urine.

[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 performed by preparing a specimen on a slide glass and observing the specimen 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.

[0003] As a method or an apparatus for automating these observation and inspection work, a sample such as blood is smeared on a slide glass, set on a microscope, and the microscope stage is automatically scanned to detect the presence of particles. The microscope stage is stopped at a position where a static particle image is captured, and the classification and analysis of particles in the sample sample are performed by using feature extraction and pattern recognition techniques by image processing technology.

[0004] 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 drawbacks that analysis requires time and a mechanical mechanism is complicated.

[0005] In a particle image analyzing method or a particle image analyzing apparatus which does not produce a smear as described above, a flow site in which a sample sample is suspended in a liquid and flows through a flow cell to perform optical analysis. The meter method is known.
The method using a flow cytometer is used to observe the intensity of fluorescence and the intensity of scattered light from each particle in a sample, and has a processing capacity of several thousands per second. However, it is difficult to observe features that reflect the morphological characteristics of particles, and particles cannot be classified by morphological characteristics conventionally performed under a microscope.

As an attempt to take an image of a stationary particle in a continuously flowing sample sample and classify and analyze the particle from each static particle image, Japanese Patent Application Laid-Open No. 57-50099 has been proposed.
No. 5, JP-A-63-94156, JP-A-4-94.
A technique described in Japanese Patent No. 72544 is known. In the technique described in JP-T-57-500995, a sample sample is caused to flow through a specially shaped flow path into a wide imaging area, and a static particle image is captured by a flash lamp.
A method of performing particle analysis using the image is shown.

In the method, when an enlarged image of sample particles is projected on a CCD camera using a microscope, the 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 capture 30 still images per second.

In the technique described in Japanese Patent Application Laid-Open No. 63-94156, a particle detection system is provided upstream of a particle image photographing region in a sample flow separately from a still particle image photographing 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 pulse light source is not periodically performed, and the passage of the particles is detected, and the static particle image can be taken at the timing only at that time. Is collected, and a meaningless image free from particles is not captured and processed even in the case of a sample sample having a low concentration.

Japanese Patent Application Laid-Open Nos. 60-260830 and 63-231244 disclose an example of a conventional flow cytometer or particle analyzer which does not take a static particle image and which has a separate particle detection system. JP-A 1-2
A technique described in Japanese Patent No. 45131 is known.

[0011]

In the above-mentioned conventional particle image analyzing apparatus, generally, a still image of a continuously flowing sample particle is analyzed to determine the number and classification of a plurality of types of particles in the sample. In order to perform the process efficiently, a particle detection system for detecting a passing particle at a still particle image capturing area or upstream thereof is required, as is performed in the above-described 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 the above method can process a measurement sample having a low particle concentration very efficiently, it is possible to increase the amount of the measurement sample, shorten the analysis time, and improve the analysis accuracy. However, as described above, to analyze the still image of each particle for the particles flowing continuously and to analyze and classify the number of multiple types of particles contained in the sample, it is described below. Such a problem exists.

In the technique described in the above-mentioned Japanese Patent Application Laid-Open No. 63-94156, a flow type particle image analyzer includes a particle detection system for detecting a passing particle at a still particle image imaging area or upstream thereof, and a method for detecting a passing particle. Only a pulse light source is turned on and an image capturing system for capturing a still particle image is provided. However, the above-described particle detection system and the image capturing system generally have different measurement principles.

As the above-mentioned particle detection system, there is used a method in which a laser beam is condensed and irradiated onto a measurement sample which normally flows in a flow cell, and light scattered light from particles crossing the laser beam is detected. . The light scattered light generally produces an optical signal of an intensity proportional to the effective scattering cross section of the particle.

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

If the particle detection system sets the particle detection condition to be equal to or higher than a predetermined particle detection level, an actual still particle image obtained by the image pickup system has a small particle size that does not reach the particle detection level due to light scattering. Is that
That happens often. These relationships will be specifically described with reference to FIG. FIG. 6 is a schematic explanatory diagram of particle detection and still particle image capture in the related art.
FIG. 6A is a diagram illustrating a particle detection signal due to the passage of a particle by a particle detection system. The measurement principle of the particle detection includes a method of detecting a light scattering signal from the particle by laser light as described above. And

In the diagram of FIG. 6A, the particles a and d are larger than a predetermined particle detection level, and after the particle a is detected by the particle detection system, a predetermined time elapses, and then the image pickup system stops. A flash lamp emits light to capture a particle image. FIG. 6C shows a captured still particle image.

A total of five a,
There are b, c, d, and e particles. When this captured image is made to correspond to the particle detection signal in FIG.
Since the two particles a and d are larger than the particle detection level, they are counted as signals of the particle detection system, but the remaining c,
No particles are detected for d and e3. The undetected particles are particles that do not require particle analysis. The particle detection pulse signal by the particle detection system is as shown in FIG.

By reducing the particle detection level, it is possible to detect all of these five a, b, c, d, and e particles. However, in that case, the frequency of particle detection increases significantly, and on the contrary, the detection rate of the analysis target particles decreases. Furthermore, the dead time due to image capture may cause counting down of particles. As a result, a reduction in particle analysis accuracy and reproducibility is caused.

Further, in general, the number of particles detected by the particle detection system does not match the number of particles processed by the image pickup system. If there are many types of particles contained in the sample, the abundance ratio of particles detected by the particle detection system,
This causes the abundance ratios of the particles processed in the image capturing system to not match. This is because, in many cases, particles below the detection level of the particle detection system are also included as particles to be processed by the image capturing system.

In such a state, based on information on the total number of particles detected by the particle detection system and information on the abundance ratio of all particles processed by the image pickup system, the number of particles of each type and the abundance ratio of particles in the sample are determined. However, it is not possible to know the information on the particle density and concentration correctly. Some of the particles that have been image-processed
There may be particles that are smaller than the particle detection level of the particle detection system but are to be processed by the image capturing system, or particles that are not processing target but provide important information.

The particle analysis is also performed correctly on these particles, the extent to which the particles are present in the sample is analyzed, and unless reflected in the analysis results as particles to be processed by the image pickup system, the number of particles of each type in the sample is determined. , The information such as the particle abundance ratio, the particle density, and the concentration cannot be known correctly.

In the technique described in Japanese Patent Application Laid-Open No. Hei 4-72544, a counter for counting all the particles passing through is used in the particle detection system. In this case, the number of particles of each type in the sample can be known from the value of the particle counter and the existence ratio of the number of particles subjected to image processing in the image capturing system.

However, for this purpose, a prerequisite is that the distribution by type of particles to be detected by the counter of the particle detection system matches the distribution by type of particles to be image-processed by the image pickup system. It is. However, as described above, in the particle processing by the measurement of the particle detection system and the particle processing by the image processing of the image pickup system, the distribution by type often does not match due to different measurement methods and principles.

The present invention has been made to solve the above-mentioned problems of the prior art, and has a particle detection system and an image pickup system, respectively. The principle of the particle detection system and the particle image identification of the image pickup system are provided. In the flow type particle image analysis method and the flow type particle image analysis device having different principles, the detection particles correspond to the particles in the still particle image, and the particles other than the detection particles in the particle detection system are used as the still particle image in the image pickup system. Even if it is present in the sample, the flow particle image analysis method and the flow particle image analyzer with good analysis accuracy can correctly know the number of particles, the particle abundance ratio, the particle density, and the concentration information of each type in the sample. Is provided.

[0026]

In order to achieve the above object, the present invention according to a flow type particle image analysis method comprises:
Flowing a liquid sample to be suspended into a flow cell, a detection step of counting the number of particles in the sample by a particle detection system, and a step of capturing a still image of particles passing through an imaging region in the flow cell by a particle imaging system. In the flow type particle image analysis method, comprising: an analysis step of performing morphological classification of particles in the image by image analysis means, the analysis step, one image particle count value by the particle detection system,
An extraction step of causing each particle in the static particle image to correspond to a particle image identification feature, and a particle distribution or particle classification in the liquid sample from the correspondence and the total particle count value by the particle detection system in the detection step. Is characterized by comprising a particle analysis step.

In the extracting step, as the particle image identification feature amount,
It is characterized in that any one of particle size information, color information, light scattering information, fluorescence intensity information or a combination thereof is used.

[0028]

In the particle analysis step, the remaining particles in the still particle image which could not correspond to one image particle count value of the particle detection system are analyzed as a separate frame, and at the end of analysis, the particles corresponding to one image particle count value are analyzed. It is characterized in that the separate frame processing is added to the result. The particle analysis stage is characterized in that a process in a case where the number of processed images is small and the volume to be processed is not sufficient to perform the separate frame process is determined. In the particle analysis step, when the number of images to be processed is small and there is not enough volume to be processed for performing the separate frame processing, the evaluation procedure is input and set in advance.

[0030] The detection step is characterized in that an erroneous count due to light emission of a pulsed light source for capturing a still particle image is corrected for the one image particle count value and the total particle count value obtained by the particle detection system.

A particle detection system for flowing a liquid sample to be suspended into a flow cell and counting particles in the sample; a particle imaging system for capturing a still image of particles passing through an imaging region in the flow cell; In a flow-type particle image analysis apparatus comprising an analysis unit that performs morphological classification of particles in a particle image, the analysis unit includes one image particle count value of the particle detection system, and each particle in the static particle image. An extraction unit configured to correspond to a particle image identification feature amount, and a particle analysis unit configured to perform either particle distribution or particle classification in the sample from the correspondence and the total number of particles counted by the particle detection system. It is characterized by having done.

The particle analysis section has a plurality of measurement modes for the same measurement sample, analyzes each measurement mode, and integrates all data after the analysis of all the measurement modes is completed. I do.

The extracting unit is characterized in that only the particles corresponding to one image particle count value are made to correspond from the magnitude relationship between the one image particle count value and the particle morphological feature value. The particle analysis unit is characterized in that the number of particles or the particle ratio per sample volume corresponding to all the image-processed images is calculated, and further, the number of particles per total sample volume is obtained. The particle analysis unit is characterized in that it is configured to calculate the number of particles of the measurement sample, the particle concentration per unit volume in the measurement sample, or the number of particles in terms of a microscope visual field.

The particle analysis unit analyzes the remaining particles in the still particle image which could not correspond to one image particle count value of the particle detection system as a separate frame, and analyzes the particles corresponding to one image particle count value at the end of the analysis. It is characterized in that it is configured to add the separate frame processing amount to the result.

The particle analysis unit is characterized in that when the number of processed images is small and the volume to be processed is small enough to perform the separate frame processing, the particle analysis unit is configured to display or output that there is a problem with the separate frame processing.

The object of analysis is urine, particularly urinary sediment components present in urine.

This will be described more specifically. Particles that have passed through the particle detection area in the flow cell are detected, and after a predetermined time, when the detected particles reach a predetermined position in the still image imaging area, a flash lamp is turned on to capture a still image of the particles. At this time, the total number of particles having a level equal to or higher than a certain level of the detection signal existing in the portion of the sample flow captured in the one still image is defined as one image particle count value.

In general, in one captured still particle image, more particles exist than one image particle count value of the above-described particle detection system. This tendency increases as the particle detection signal level increases. Here, all particles existing in one still particle image are classified and analyzed using the particle identification feature amount that is the same as or close to the detection principle of the particle detection system.

As the particle image identification characteristic amount, morphological information or color information of the measured particles, fluorescent intensity information, or a characteristic amount obtained by combining a plurality of these pieces of information can be used. Specifically, from the particle image, morphological information such as the size, area, diameter, perimeter, and projected image of the particle, and particle density, color information, intra-particle structure, and feature amounts such as fluorescence intensity are used. Information such as the presence of nuclei, cytoplasm, intracellular granules, etc., as in biological cells, can also be used.

Further, the transit time of the particles passing through the particle detection system may be used as a parameter for the particle size. That is, it is checked which particles are present in the image and which are the detection particles detected by the particle detection system. The corresponding number of particles corresponds to one image particle count value. This is referred to as regular identification processing. The remaining particles for which the correspondence between the detected particles and the particles in the image could not be obtained are subjected to a different identification process from the corresponding particles. This process is referred to as a separate frame identification process.

Further, the count value of all particles passing through the particle detection system and exceeding the detection level is measured irrespective of the still image pickup by the particle detection. In this case, it is necessary to prevent erroneous counting of the particle detection signal by the flash lamp light. To prevent this, the flashlamp light is prevented from reaching the particle detector or the flashlamp signal is not counted electrically. In addition, the flash lamp light may also be detected, and the erroneous count may be prevented by a method of subtracting the number of times of lamp emission at the end of the measurement.

When processing a dense particle sample,
Since the counting of particles occurs in the particle detection system due to the dead time associated with image reading, the total number of particles of one image particle count value during a certain measurement time passes through the particle detection system during the certain measurement time. It does not show total particle counts above the detected level. For this, the total number of particles above the detection level in the sample within a certain measurement time,
The total particle detection count is counted.

Further, the particles to be subjected to the separate frame identification processing are particles which are lower than the detection level of the particle detection system and are not included in the total particle detection count value. When one image particle count value is associated with a particle in the still particle image, pattern recognition identification processing is performed on the associated particle image to determine the type of the particle. This operation is performed on all the images to determine the particle abundance ratio of each type in the sample.

In order to determine the particle abundance ratio, the processing is performed only on the particles detected by the particle detection system, that is, the corresponding particles. 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 abundance ratio is determined, the total number of detected particles is separately measured by a particle detection counter, and the total number of the detected particles is multiplied by the particle abundance ratio.
Calculate the number of particles for each type in the sample. The particle concentration per unit volume is calculated from the number of particles of each type.
Further, a one-dimensional image sensor may be used for the particle detection system.

Next, a method for associating the number of one image particle detected by the particle detection system with the particle present in the still particle image will be described. A description will be given of a case where a laser beam is applied to a sample flow to detect light scattering from particles in the sample as a particle detection unit.

Since the light scattering intensity from a particle is proportional to the effective scattering cross section of the particle, it mainly contains information about the size of the particle. Therefore, the correspondence between the particle detection signal and the particle present in the still particle image is obtained. Is performed, for example, based on particle area or particle diameter information. The extraction of the characteristic parameters from the particle image can easily obtain the particle area and the diameter information by using a characteristic extraction method performed in normal pattern recognition.

When the analysis target is a predetermined detection level or more of the particle detection signal, the particle area or the diameter value of the particle present in one still particle image is calculated from the image analysis, and the calculated value is increased from the larger one. Sort in order. From the large value of the rearranged values, only one image particle count detected by the particle detection system is taken out, taken out as particles detected by the particle detection system, and analyzed as a normal identification processing target, and the remaining particles are analyzed. , A separate frame identification process is performed. In this manner, one image particle count value of the particle detection system is associated with the particles present in the captured still particle image.

Next, another frame identification processing will be described. After associating the number of particles of one image particle of the particle detection system with the particles to be subjected to regular identification processing, classification and identification processing is performed on 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. However, important particles may be contained in these unprocessed particles, and the information cannot be discarded.

To determine the concentration and quantity of the untreated particles in the sample, the number per unit volume, actually,
By calculating the number of particles per sample volume corresponding to all the images after processing all the images, and then converting them back to all the measurement samples, the number of particles in the sample, the ratio of the number of particles, and the like are determined. 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 regular identification processing and the separate frame processing are combined to obtain a final result.

In the separate frame identification result, there are particles that should be originally included in the normal identification result, and need to be added to the normal identification result. If particles that do not enter the regular identification process but are exceptionally important are included, the results are also reported. If the particle size is small and the signal level is the same as the noise signal to detect by particle detection, or if the number of particles is very large and the analysis image is enormous, increase the particle detection level and analyze the static particle image It is better to reduce the number.

By performing the above-described 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 in terms of a microscope visual field is known. The number of particles in terms of the microscope visual field is the average number of particles present in the microscope visual field at an appropriate magnification, and it is necessary to set a sample volume corresponding to the microscope visual field in advance.

After associating one image particle count value of the particle detection system with the particles to be subjected to the normal identification processing, and performing another frame identification processing on the particles remaining in the still particle image, it is necessary to maintain the measurement accuracy. It requires a sample volume to be image-processed. 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.

In the separate frame identification processing, since the analysis processing is performed based on the captured still particle image, the image processing is performed when the ratio of the original particles for normal identification processing existing in the sample is small. Since the particle concentration of the separate frame identification processing is calculated based on the sample volume, there is a risk that errors may increase. This is because there are few particles in the sample, and there is no problem in the analysis accuracy.

In order to perform separate frame identification when the volume of the sample to be subjected to image processing is not sufficient as described above, the measurement accuracy is not sufficiently high.
Alternatively, it is necessary to notify the operator that the measurement accuracy is low even if the separate frame identification processing is performed and that the data is not reliable. However, among the particles that are subject to separate frame identification processing,
The presence of particularly important particles must be reported regardless of the measurement accuracy.

As the measurement accuracy of the separate frame identification processing, its reproducibility will be examined with reference to the total number N of particles subjected to the separate frame identification processing. Since the reproducibility in this case is 1 / SQRT (N), N = 400 based on a reproducibility of 5%, and N = 100 based on a reproducibility of 10%. However, if the reproducibility of the separate frame identification process can be set to 20% or more, N
= 25 is sufficient.

The total number of particles subjected to the separate frame identification processing is counted, and if the number reaches the preset number of particles or more, the result of the separate frame identification processing is determined to be 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.

Here, the total number of particles N to be subjected to the separate frame identification processing
Although the example in which the determination is made based on the reproducibility based on the above is shown, the reproducibility of the number of particles for each type may be used as the determination criterion, the number of processed images may be simply used as a reference, May be considered as a reference. These criteria are:
It is necessary to set before measurement.

Further, when the measurement result in the separate frame identification processing does not satisfy the above-mentioned criterion, a means for informing the operator is required. It is better to add comments or symbols after the measurement results. Also,
The classification result is output as it is for particles that provide more important information to the operator due to the presence of at least one specific particle than the risk in the above-described separate frame identification processing. In this way, the problem that measurement results cannot be obtained for exceptional particles does not occur.

When particles to be subjected to regular identification processing are present in the particles processed in the above-described separate frame identification processing, the particle concentration per unit volume is calculated and then incorporated into the particles for regular identification processing to classify the particles. Improve accuracy. Also in this case, since the risk of measurement accuracy in the above-described separate frame identification process is taken into account in the regular identification result, the measurement accuracy does not decrease due to the inclusion.

From the images picked up by all the particle detections, the associated particles for regular identification processing are selected.
By executing the separate frame identification process, it is possible to know the number of classifications and classification ratios of all the particles detected.

However, particles that are not normally processed may exist in the particles that have been subjected to the normal identification processing. If such an accurate correspondence cannot be obtained, the particle classification processing based on pattern recognition checks whether or not the particle is a regular classification identification processing particle that is not detected by the particle detection system, and is subjected to another frame classification identification processing. The method or the process of putting the particles into the separate frame classification target particles as a whole is executed, and at the end of the analysis, the number of particles per unit volume is calculated and subtracted from the result of the particle detection target particles.

In the description so far, the analysis conditions have been assumed to be constant with respect to the same analysis sample. However, in actual sample analysis, the analysis mode may be changed. Even when there are a plurality of such analysis modes, the normal identification process and the separate frame identification process described above are performed for each analysis mode.

For example, if a certain type of particle is analyzed so that the target particle is a separate frame identification target in the first analysis mode and the particle is a normal identification target particle in the second analysis mode, after the analysis is completed, the individual analysis mode is determined. Compiling the classification identification results of
The analysis accuracy is improved by organizing the results of particle analysis of the entire analysis sample.

[0065]

The function of each of the above technical means is as follows.
According to the configuration of the present invention, a particle sample suspended in a liquid is caused to flow into a flow cell, particles passing through a particle detection region in the flow cell are detected, and a still image of a detection particle passing through an imaging region in the flow cell is detected. The morphological classification of particles was performed by image analysis of the captured particle image, and the particle count of one image was associated with the particles present in the static particle image. It can be confirmed whether the particles are detection particles. Therefore, even if particles smaller than the particle detection level are simultaneously present in the still particle image, they can be excluded from the detected particles, and the number of particles subjected to the whole image analysis processing can be made to match the counted number of one image particle.

In general, when the principle of particle detection of the particle detection system is different from the principle of image processing of a still particle image, the characteristic amount of the image processing includes the particle size characteristic amount by light scattering from the detected particles. Since there is a feature amount of image processing close to the particle detection principle of the particle detection system, it is possible to easily associate the particle detection of the particle detection system with the particles in the still particle image.

When the detection target level of the particle detection signal is equal to or higher than a predetermined detection level, characteristic values such as the particle area or diameter of the particles present in one still particle image are calculated by image processing, and these values are calculated. By sorting in order from the largest, and by taking out only the number of image particles equal to or higher than a certain signal level detected by the particle detection system from the largest sorted value, that is, by taking out only one image count value,
The detection particles detected by the particle detection system can correspond to the particles present in the still particle image.

The particles in the static particle image associated with the detected particles detected by the particle detection system can be classified and identified by image processing and pattern recognition processing. Of the particles present in the sample can be known.

Further, since the particle detection system is provided with a means for counting all particles larger than a certain detection signal level in the sample regardless of whether or not a still particle image is captured, the particle due to the dead time generated in image capture is provided. Particles can be prevented from being counted down in image processing.

From the total particles in the sample and the individual particle abundance which can be known from the result of the image processing, the number of particles for each type in the sample, the particle concentration per unit volume, and the number of particles in terms of a unit microscope field of view It becomes possible to know. To have a means to prevent erroneous counting of particle detection signal by flash lamp light for taking a still particle image,
The influence of the flash lamp emission can be prevented.

Further, as described above, it is possible to perform a separate frame identification process on a small particle smaller than a certain detection level in the particle detection system in the still particle image, so that a particle smaller than the particle detection level present in the image is obtained. Can also be classified and analyzed. Among these small particles, those that are particles to be subjected to regular identification processing, those that are not particles to be subjected to regular identification processing but provide important information to the presence of the particles, and the like are included. It is possible to calculate the abundance ratio of the processed particles, and to know the particle concentration per unit volume of the analyzed particles and the number of particles converted into a microscope visual field from the sample volume corresponding to all the static particle images that have been analyzed. .

In the separate frame identification process, the measurement accuracy is determined based on the total number of processed particles of the separate frame identification particles, and the fact is reported to that effect. If the measurement accuracy can no longer be guaranteed, a report to that effect is made, and errors due to the separate frame identification processing can be reduced. Further, the separate frame identification processing includes:
Since the classification result can be output as it is, if there is information where it is more important that at least one specific particle is present, an analysis result can be obtained even for an exception particle.

In the case where the particles identified by the above-described separate frame identification processing include the particles identified by the regular identification processing, the particle concentration per unit volume is calculated and incorporated into the result of the identification processing. Can be improved. Also in this case, since the analysis accuracy in the separate frame identification process is determined to be reduced and incorporated into the normal identification process result, it is possible to prevent the measurement accuracy from being reduced by incorporating the result of the separate frame identification process.

Note that these criteria are set before particle analysis in advance, and if the measurement result in the separate frame identification processing does not meet the above criteria, the operator can be notified. You can judge. Since the stationary particle image may contain particles important for sample analysis, the information cannot be discarded.However, by performing the above-described separate frame identification processing, these particles are surely identified. The analysis can be performed without deteriorating the overall analysis accuracy.

Further, even when there are a plurality of analysis modes having different ranges of the particles to be analyzed, the normal identification processing and the separate frame identification processing can be performed for each analysis mode. For example, if a certain type of particle is analyzed so as to be able to perform another classification classification target particle in the first analysis mode, and to perform normal classification processing in the second analysis mode, the classification identification result of each analysis mode after the analysis is completed Can be combined into a particle analysis result of the entire analysis sample to improve the analysis accuracy.

[0076]

Embodiments of the present invention will now be described with reference to FIGS.
This will be described with reference to FIGS. FIG. 1 is a schematic explanatory view showing the configuration of a flow-type particle image analyzer used in a flow-type particle image analysis method according to an embodiment of the present invention.
FIG. 3 is a block diagram showing a configuration of a particle number analysis unit in the embodiment of FIG. 1, FIG. 3 is an explanatory diagram showing a relationship between a particle detection position and a still particle image capturing position in the embodiment of FIG.
FIG. 4 is a diagram showing the signal timing relationship of one image particle counting in the embodiment of FIG. 1, and FIG. 5 is a flowchart for explaining the processing contents of the particle number analysis unit of FIG.

In FIG. 1, 100 is a flow cell, 10
1 is an image capturing means, 102 is a particle detecting means, 103 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, and 6 is a coupling lens. Image position, 7 is a projection lens, 8 is a TV camera, 11 is a field stop, 12 is an aperture stop, 14 is a small reflecting mirror, 15 is a semiconductor laser, 17 is a collimator lens, 18 is a cylindrical lens, 19 is a reflecting mirror, 20 Is the beam splitter, 2
1 is an aperture, 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, 5
0 is a display unit.

Referring to FIG. 1, a flow type particle image analyzer used in the flow type particle image analysis method according to the present embodiment includes a flow cell 100 to which a sample liquid in which particles are suspended is supplied, and a flow cell 100 in the flow cell 100. The apparatus includes an image capturing unit 101 for capturing an image of a sample liquid, a particle detecting unit 102 for detecting particles from a captured image, and a particle analyzing unit 103 for analyzing the detected particles.

The flow cell 100 includes the sample liquid 11
0, the sheath liquid 111 is supplied, and the sample liquid 110 is supplied.
Forms a flow surrounded by the sheath liquid 111. Then, the sample liquid flow 110 becomes a stable steady flow having a flat cross section perpendicular to the microscope optical axis 9 (described later) of the image capturing means 101 (described later), that is, a so-called sheath flow. It flows downward from above the paper surface. The flow rate of the sample liquid flow 110 is controlled by a central control unit 29 (described later).

The image pickup means 101 has a function as a microscope, and is a flash lamp 1 which is a pulse light source.
A flash lamp driving circuit 1a for emitting the flash lamp 1, a field lens 2 for making the pulse light beam from the flash lamp 1 parallel, and a parallel pulse light beam 10 from the field lens 2 for passing the sample liquid flow 110 in the flow cell 100. Condenser lens 3 to be focused on the sample and sample liquid flow 110 in flow cell 100
The microscope objective lens 5 that condenses the pulsed light beam irradiated on the lens and forms an image at the image forming position 6, and the particle image at the image forming position 6 projected through the projection lens 7 are captured by the so-called interlace method and are electric signals. A TV camera 8 for converting an image data signal, a field stop 11 for limiting the width of the pulse light beam 10 and an aperture stop 12 are provided. T above
As the V camera 8, a CCD camera or the like having little afterimage is generally used.

The particle detecting means 102 is a semiconductor laser 15 serving as a detection light source for emitting a laser beam as detection light.
A collimator lens 16 for converting the laser beam from the semiconductor laser 15 into a parallel laser beam 14, a cylindrical lens 17 for focusing only one direction of the laser beam 14 from the collimator lens 16, and a cylindrical lens 1.
7 is located between the microscope condenser lens 3 and the flow cell 100, and reflects the laser beam 14 from the mirror 18 upstream of the image capturing area on the sample liquid flow 110. And a micro-reflecting mirror 19 for guiding it to the vicinity.

Further, the laser beam 1 by the particles
The microscope objective lens 5 for condensing the scattered light of No. 4 (this microscope objective lens 5 is shared with the microscope objective lens 5 for image formation of the image pickup means 101) and the microscope objective lens 5 Beam splitter 2 that reflects scattered light
0 and the scattered light from the beam splitter 20
A light detection circuit 22 that receives light via the light detection circuit 1 and outputs an electric signal based on the intensity thereof; a flash lamp light emission control circuit 23 that operates the flash lamp drive circuit 1a based on the electric signal from the light detection circuit 22; Is provided.

The particle analyzing means 103 converts the image data signal transferred from the TV camera 8 into a digital signal, and stores the data based on the signal from the AD converter 24 at a predetermined address. An image memory 25, an image processing control circuit 26 that controls writing and reading of data in the image memory, and a feature extraction circuit 27 that performs image processing based on signals from the image memory 25 to perform particle count and classification. , Identification circuit 28
, A central control unit 29, a particle number analysis unit 40 for determining the number of particles in the sample liquid 110, and a display unit 50 for displaying the number and classification of particles as a result of the image processing.

The central controller 29 controls the TV camera 8
, The conditions of the sample liquid flow in the flow cell 100, the control of the image processing control circuit 26, the storage of the image processing results from the identification circuit 28, the particle number analysis unit 40
, And display on the display unit 50.

Next, the configuration of the particle number analysis unit 40 will be described with reference to FIG. In the figure, the same reference numerals as those in FIG. 1 denote the same parts, and a detailed description thereof will be omitted. FIG.
, 41 is a total particle counting circuit, 42 is a one image particle counting circuit, 43 is a one image particle detection timing generation circuit,
0 is a display unit, 61 is a feature parameter data unit corresponding to one image, 62 is a particle classification result unit, 63 is a total image count unit, 6
4 is an analysis condition setting unit, 65 is a parameter data size sorting unit, 66 is a corresponding unit, 67 is a particle classification result corresponding unit, 6
8 is a normal classification result part, 69 is a separate frame classification result part, and 70 is a classification result totaling part.

In FIG. 2, a total particle counting circuit 41 constituting the particle number analysis section 40 receives a particle detection signal from the light detection circuit 22 and counts the total number of detected particles during the entire analysis time. Has become. Also, one image particle counting circuit 4
2 counts particles larger than the particle detection level present in the image captured by the flash lamp emission after particle detection. The one-image particle detection timing circuit 43 generates a timing for counting the number of particles for one-image particle detection based on the control signal from the particle detection signal 22 and the control signal from the image processing control circuit 26.

Further, the sample analysis condition is set in the analysis condition setting unit 64, the total image count is set in the all image counting unit 63, that is, the count amount is increased by one for each image processing, and the particle classification result unit 62 is set in the particle classification result unit 62. As a result of the particle classification of the particle image subjected to the one image processing, the central control unit 29 sets parameter data to be associated with the detected particles in the feature amount of each classified particle in the one-image-corresponding feature parameter data unit 61. It has become.

Further, in the parameter data size sorting section 65, the feature parameters set in the one-image feature parameter data section 61 are arranged in the order of larger data values. In the data correspondence unit 66, of the parameter data arranged in the descending order, only the number set in the one image particle counting circuit from the larger one is determined as the detection particle, and the rest is determined as other than the detection particle. .

Based on the result of the data correspondence unit 66,
The particle classification result set in the particle classification result section 62 is selected by the particle classification result correspondence section 67 into a target particle of the regular identification processing and a target particle of the separate frame identification processing. Set it in the section 69.

Finally, a 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.

The operation of the flow type particle image analysis method having the above-described configuration and the flow type particle image analysis apparatus used therein will be described. First, the image pickup means 101 and the particle detection means 10
2 will be described. The semiconductor laser 15 constantly oscillates continuously, and always observes that the particles in the sample 110 pass through the detection region. The laser light flux 14 from the semiconductor laser 15 is converted into a parallel laser light flux by a collimator lens 16, and focused in one direction of the light flux 14 by a cylindrical lens 17.

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 a particle detection position where the laser beam 14 is focused by the cylindrical lens 17, and is a position near the upstream side of the image capturing area on the sample liquid flow 110. When a particle to be analyzed crosses the laser beam 14, the laser beam 14 is scattered.

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 the intensity. further,
The light detection circuit 22 determines whether or not the electric signal for particle detection is equal to or higher than a predetermined signal level. If the detected electric signal is equal to or higher than a predetermined signal level, it is determined that the image processing target particle has passed, and the detection signal is transmitted to the flash lamp emission control circuit 23 and the particle number analysis unit 40.

In the flash lamp emission control circuit 23 receiving the detection signal, the particles to be subjected to image processing
When the flash lamp 1 emits light and an image is taken when a predetermined position of the image capturing area of the V camera 8 is reached,
The flash lamp emission timing is determined based on the particle detection signal delayed by a predetermined delay time and the timing of the field signal of the TV camera 8, and a flash emission signal is generated and sent to the drive circuit 1a.

The predetermined delay time is determined by the distance between the particle detection position and the image capturing area, the flow rate of the sample liquid 110, and the like. This delay time is a very short time because the distance between the particle detection position and the image capturing area is small, and the detection and analysis accuracy of the particles are not affected by the flow rate of the sample liquid or the particle concentration.

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

The pulse light beam applied to the sample liquid flow 110 in the flow cell 100 is condensed by the microscope objective lens 5 and forms an image at an image forming position 6. The image at the image forming position 6 is projected on the imaging surface of the TV camera 8 by the projection lens 7, and is converted into an image data signal by an interlace method. Thus, the sample liquid 1
This means that a still particle image of the particles in 10 has been captured.
The imaging conditions in the TV camera 8 are determined by the central control unit 29.
Is set in advance, and according to the imaging conditions, the TV
The imaging operation of the camera 8 is controlled.

The particle analyzing means 103 will be described. An image data signal imaged by the TV camera 8 and converted by an interlace method is converted into a digital signal by an AD converter 24, and data based on this is controlled by an image processing control circuit 26 and stored in a predetermined memory of an image memory 25. Is stored at the address.

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

The process of classifying and identifying particles is automatically carried out by the usual pattern recognition process. The image processing result, the measurement conditions, and the information on the number of images subjected to the image processing are
The data is sent from the central control unit 29 to the particle number analysis unit 40.
In the particle number analysis unit 40, the central control unit 2
9. Based on the particle detection signal from the light detection 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 checked, and the final classification and identification result of the particle image is determined. Summarization is performed.

Next, the particle number analysis unit 40 will be described with reference to FIG. The total particle counting circuit 41 counts a signal having a certain detection level or more that has passed through the particle detection system during the entire measurement time of the particle detection signal from the light detection circuit 22, and obtains the total number of detected particles at the end of the measurement.

On the other hand, the one-image particle counting circuit 42 counts particles having a detection level higher than the detection level in one image picked up by the emission of the flash lamp 1 after detecting the particles.
The time timing signal of the particle counting for detecting one image particle at that time is transmitted from the one image particle detection timing generation circuit 43 based on the control signal from the image processing control circuit 26.

Here, with reference to FIG. 3, the relationship between the particle detection position in one image particle counting and the still particle image capturing position will be described. As shown in FIG. 3, Tg is the time required for particles to flow from the B end to the A end of the image, and Te is
The time required for the particles to flow from the flash lamp emission position P to the end A of the static particle image capturing field of view, and To is the time required for the particles to flow from the end B of the image to the flash lamp emission position P, Tg = Te + To

When the particles in the measurement sample pass through the particle detection position and the particle detection signal is equal to or higher than a predetermined level, if a particle is detected, a detection pulse signal is output. After the output of the detection pulse signal, the flash lamp 1 is caused to emit light after a lapse of a delay time Td until the particles reach a predetermined position in the still particle image capturing area.

The light emission conditions of the flash lamp 1 are as follows. After the capture of the still particle image by the previous particle detection is completed, that is, the flash lamp 1 emission is disabled after the flash lamp 1 emission, and the image signal is transferred by one two-field read signal to the one still particle image. Then, the flash lamp 1 is set in a light emitting enabled state again. This state is the lamp emission ready signal,
It is created by the flash lamp control circuit 23.

When the signal level of the lamp emission ready signal is "
If “on”, the flash lamp 1 is in a ready-to-emit state, and if “off”, it is in a light-inhibited state.In FIG. However, particles that have passed before the y-particle detection may be present downstream of the flow.

The particles exist at the position of the time interval Te before passing through the y particles, and correspond to, for example, the x particles. During the time Te during which the sample particles flow from the point P to the end A, the lamp ready signal is still “o”.
This is because the "n" state may not be achieved. This is conspicuous when the particle concentration in the sample is high. The section where such a state occurs is indicated by the hatched section of the lamp emission ready signal in FIG. Indicated.

If the time from when the lamp emission ready signal becomes “on” until the lamp emission becomes greater than Te,
Particles on the downstream side, for example, x particles, flow off the image,
The hatched portion disappears. The time interval for one image particle count is the time T, at which the particles pass through the static particle image capture area.
g.

The timing of each signal is shown with reference to FIG. It is assumed that a detection signal is generated from three particles x, y, and z in the particle detection signal. In order to make the flash lamp 1 emit light with good timing, a particle detection delay Td signal which is delayed by the time Td after the detection of the particles may be transmitted. Here, the lamp emission ready signal is "on" or "off".
It is determined whether the lamp should emit light depending on the state.

In FIG. 4, since the still particle image was being captured in the immediately preceding frame, the lamp emission ready signal was not "on" from the beginning, and the lamp emission ready signal was changed to "ready" by the vertical synchronization signal. on ”.
Therefore, the flash lamp does not emit light with the particle x, but emits light with the next y particle, and the lamp emission ready signal becomes “of
f ".

As described above, unless the flash lamp 1 emits light, the counting of the number of particles in one image cannot be started. However, then the x-particles are present downstream of the flow and are in the particle image capture area. In order to count the number of particles in one image, Te is calculated from the flash lamp emission.
It is necessary to start counting before time.

For that purpose, the particle detection delay Td signal is further delayed by Te time, and the particle detection delay Td + Te
Output a signal. The one image particle counter may count between the To + Te signals from the flash lamp emission to the time Tg. Tg is the time the particles flow between the ends of the image. After the elapse of the Tg time, one image particle count data is transferred to a required location.

[0113] Referring again to FIG.
If particles that pass for the time To after the flash lamp emits light at the point P are counted, x particles are not counted, and the number of particles present in one correct image cannot be known. One image particle count must be performed for Tg time.

However, after the particles are detected and the flash lamp 1 emits light, the particles from the end A of the image surface to the point P of the flash lamp light emission are not counted until they flow too much. Therefore, a correct one image particle counting operation is performed by generating a particle detection delay Td + Te signal further delayed by Te time than the particle detection delay Td signal and setting one image particle counting time to Tg = Te + To. The above is only one example of timing.

Referring again to FIG. 2, the flow type particle image analysis will be described. The data of the sample analysis conditions set in the analysis condition setting unit 64 is determined by the processing contents in the classification result totaling unit 70. Basically, analysis sample volume, analysis time, sample volume equivalent to one static particle image,
Data such as sample volume and analysis mode equivalent to one field of view of a microscope is required, but there are some items that may be omitted from the above-mentioned data depending on the content of classification processing.

The total image counting section 63 increments the count by one every time one image processing is performed, and stores the total number of images processed within the analysis time at the end of the analysis. The classification result of the particle classification of the still particle image obtained by performing image processing for one image is as follows.
8 and set in the particle classification result section 62 via the central control section 29. At the same time, the feature amount closest to the detection principle of the particle detection system among the feature amounts used for the classification and identification of the classified particles is set in the one-image corresponding feature parameter data unit 61. This is necessary for associating the one image particle count value by the detection system with the captured still particle image.

Next, the feature parameter data corresponding to the detected particles of all the particles present in the still particle image set in the one-image-corresponding feature parameter data section 61 are sorted in descending order by the parameter data size sorting section 65. Be aligned. Of course, if the detection principle of the particle detection system can correspond to the size relationship such as the size of the particles, the sorting is performed according to the size relationship. However, the sorting method may be used according to another principle.

In the corresponding unit 66, of the parameter data arranged in the descending order in the parameter data size rearranging unit 65, only the number set in the image particle counting circuit 42 from the larger one is detected particles and the detected particles It is divided into the rest except for.

On the basis of the corresponding classification result of the corresponding unit 66, the particle classification result set in the particle classification result unit 62 is converted by the next particle classification result corresponding unit 67 into particles for regular identification processing having a certain signal level or higher. And the other frame identification processing target particles having a signal level or less are set, and the respective results are set in the normal classification result section 68 and the separate frame classification result section 69.

In the normal classification result section 68 and the separate frame classification result section 69, an operation of adding the result to the classification and classification performed so far is performed. When adding, the regular classification result part 6
In step 8, a plurality of types of particles are added for each type. Similarly, for the separate frame classification result unit 69, the addition is performed for each of a plurality of types of the separate frame classification particles. At the end of the analysis, a cumulative total is obtained for each type of all particles subjected to image processing.

When the analysis of one sample is completed, the total number of particles equal to or higher than the particle detection level of the analysis sample is counted by the total particle counting circuit 41, the total number of processed images is counted by the total image number counting section 63, Is stored in the normal classification result section 68, and the classification cumulative number of the particle to be subjected to another frame identification processing is stored in the separate frame classification result section 69.

Each of the stored data described above is organized as a final result by the classification result totaling unit 70 based on the data, and the result is returned to the central control unit 29 and output to the display unit 50 as necessary. Is displayed.

The sequence 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. The analysis conditions to be set include the division between the normal classification and the separate frame classification, the analysis time, the amount of analysis, the classification type, the analysis mode, and the like.

In step 2, whether a series of processes of particle detection and image processing has been completed for the entire sample,
It is determined whether or not the processing has been completed, and the process proceeds to step 3. In step 3, particle detection, particle image capturing, particle feature extraction, and particle classification identification are executed for one still particle image, and the process proceeds to step 4.

In step 4, the particle classification result and the corresponding feature parameter data for one still particle image are
The results are stored in the particle classification result section 62 and the one-image-corresponding feature parameter section, respectively, and the process proceeds to step 5. In step 5, the magnitude sorting of the corresponding feature parameter data is performed by the parameter sorting section 65, and the process proceeds to step 6.

In step 6, the one image particle counting value is set in the one image particle counting circuit 42, and the flow advances to step 7. In Step 7, the correspondence between the one image particle count value and the image processing target particle is processed by the corresponding unit 66 based on the set one image particle count value, and the process proceeds to Step 8. In step 8, the normal classification result unit 68 performs an addition process on the analysis result of the particles to be subjected to normal identification processing for each particle type, and proceeds to step 9.

In step 9, the analysis result of the particles to be subjected to the separate frame identification processing is added to the separate frame classification result section 69 for each particle type in the separate frame classification result section 69, and the process returns to step 2. Step 2 is repeated until the series of particle detection and image processing is completed. When all the samples have been analyzed, the process proceeds to step 11.

In step 11, the classification result totaling section 70 sets the total number of particles at or above the particle detection level for the analysis sample by the total particle counting circuit 41 and the total number of images processed by the total image counting section 63. Then, the process proceeds to Step 12. In step 12, the data stored in the normal classification result section 68 for the cumulative total of the particles to be subjected to the normal identification processing and the data stored in the separate frame classification result section 69 for the total cumulative number of the particles to be processed in the other frame are read out, and the classification results are totaled. The unit 70 performs the totalizing process of the normal identification process and the separate frame identification process, determines the number of particles in the analysis sample, and
Proceed to.

In step 13, since the analysis error may be expected to 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. Proceed to step 14.

In step 14, the existence ratio of each particle is calculated from the number of each particle in the result of the normal identification processing and the result of the separate frame identification processing. 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 results of the separate frame identification processing, first, the total volume of the image-processed sample is calculated from the total number of processed images and the sample volume equivalent to one still particle image. The number of particles in the total volume of the analysis sample is determined.

Based on the results of the normal discrimination processing and the separate frame discrimination processing, particles that are present in both of them are sorted into one particle type, and particles not to be analyzed are discarded. Based on these analysis results, calculation of particle concentration in the sample, calculation of the number of particles converted into visual field,
Proceed to step 15. In step 15, the analysis result is output and the processing ends.

The validity / invalidity of the data is determined based on the number of particles subjected to the separate frame identification processing and the number of sample volume particles subjected to image processing. These criteria need to be set in advance before the analysis, but may be performed, for example, by setting the analysis conditions at the start of the analysis. When analyzing a sample in a plurality of analysis modes, the operation is performed in each analysis mode, and the analysis data of each mode is integrated at the final stage to obtain a final result.

A means for preventing erroneous counting of the particle detection signal by the flash lamp light for photographing a still particle image is required. 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. Conversely, it is also possible to detect the flash lamp light and correct the erroneous count by a method of subtracting the number of times of lamp emission at the end of the analysis. This is performed on the counting results of the number of one image particle and the total number of particles.

The flow-type particle image analysis method and the flow-type particle image analysis apparatus according to one embodiment of the present invention can be used for biological samples and cells suspended in liquid, blood cell components such as red blood cells and white blood cells in blood, or urine. It is effective for the classification and analysis of urine sediment components present in it. In particular, when counting the number of particles in urine sediment components and classifying particles in urine, the number of particles present in each sample may differ by more than a few orders of magnitude. Is
This is effective in obtaining information on the number of particles in the sample liquid.

However, in the urine sediment component, the types and sizes of particles are extremely varied, and it is not possible to accurately correspond the detection particles of the particle detection system to the particles in the still particle image. By example, accurate particle counting, particle classification,
Particle concentration can be analyzed. Note that, in the above embodiment, a case was described in which a still image of particles contained in the sample liquid continuously flowing in the flow cell was captured and the image analysis was performed. It can also be applied to particle image analysis of slide specimens.

In addition, the case where the laser beam from the semiconductor laser is used as the detection light and the laser beam scattered by the particles is used for the particle detection of the particle detection system has been described. It is possible to use transmitted light, a method of detecting particles by a one-dimensional image sensor, and a method of detecting particles by a change in electric resistance due to passage of the particles.

[00137] Further, in the case where the time required 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 as the particle detection, size information in the flow direction of the particles is reflected. Can be.
However, when associating the particle detection of the particle detection system with the captured still particle image, the particle diameter information or the projection information is appropriate. In addition, the correspondence between the particle detection and the still particle image is obtained by recording the time relationship of the particle detection and making it correspond to the positional relationship on the still particle image. It is possible to judge which particles of the particles correspond to each other and to carry out accurately.

In the above description, the case where the correspondence between the number of detected particles and one still particle image is performed for each image has been described. However, data necessary for corresponding the number of particles for each still particle image is taken. Investigating the correspondence may be arranged after the completion. Although the interlaced scanning has been described as a TV camera imaging method in image capturing, the operation and effect of the present invention are not changed even in the non-interlaced scanning method.

[0139]

As described above in detail, according to the present invention, a particle detection system and an image pickup system are provided, respectively, and the flow of the flow differs between the principle of the particle detection system and the principle of particle image identification of the image pickup system. In the particle-type image analysis method and the flow-type particle image analyzer, the detected particles correspond to the particles in the stationary particle image, and particles other than the detection particles of the particle detection system are present in the stationary particle image of the image capturing system. Even so, it is possible to correctly know information on the number of particles, the particle abundance ratio, the particle density, and the concentration of each type of particles in a sample, and to provide a flow type particle image analysis method and a flow type particle image analysis device with good analysis accuracy. be able to.

[Brief description of the drawings]

FIG. 1 is a schematic explanatory view showing the configuration of a flow-type particle image analyzer 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 number analysis unit in the embodiment of FIG.

FIG. 3 is an explanatory diagram showing a relationship between a particle detection position and a still particle image capturing position in the embodiment of FIG.

FIG. 4 is a diagram showing a signal timing relationship of one image particle counting in the embodiment of FIG. 1;

FIG. 5 is a flowchart illustrating processing performed by a particle number analysis unit in FIG. 2;

FIG. 6 is a schematic explanatory diagram of particle detection and still particle image capture in the related art.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 Flash lamp 1a Flash lamp drive circuit 2 Field lens 3 Microscope condenser lens 5 Microscope objective lens 6 Image formation position 7 Projection lens 8 TV camera 11 Field stop 12 Opening stop 14 Microreflector 15 Semiconductor laser 17 Collimator lens 18 Cylindrical lens 19 Reflection Mirror 20 Beam splitter 21 Aperture 22 Light detection circuit 23 Flash lamp lighting control circuit 24 A / D 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 Total particle counting circuit 42 1 image particle counting circuit 43 1 image particle detection timing generation circuit 50 display section 61 1 image corresponding feature parameter data section 62 particle classification result section 63 total image number counting section 64 analysis condition setting section 65 parameter data Data sorting section 66 Corresponding section 67 Particle classification result corresponding section 68 Normal classification result section 69 Separate frame classification result section 70 Classification result totaling section 100 Flow cell 101 Image capturing means 102 Particle analyzing means 103 Particle detecting means 110 Sample flow 111 Sheath liquid Td Particle detection delay time Te Time required for a particle image to flow from flash lamp emission position P to end A Tg Time required for a particle image to flow from end B to end A of the image To Particle image is a flash lamp from end B of the image Time required to flow to light emitting position P

Continuation of the front page (58) Field surveyed (Int.Cl. ⁶ , DB name) G01N 15/14 G01N 15/02 G01N 33/48-33/52 G01N 33/58-33/98

Claims (13)

(57) [Claims] A detection step of flowing a liquid sample to be suspended into a flow cell and counting the number of particles in the sample by a particle detection system; and a still image of particles passing through an imaging region in the flow cell. Imaging by;
An analysis step of performing morphological classification of particles in the image by image analysis means, wherein the analysis step comprises: a single image particle count value by the particle detection system; and the static particle image. Each of the particles in the extraction step of corresponding by the particle image identification feature amount, and either the particle distribution or particle classification in the liquid sample from the correspondence and the total particle count value by the particle detection system of the detection step. line
Flow type particle image analysis method characterized by comprising a power sale particle analysis phase. 2. The extracting step includes using, as the particle image identification feature, any one of particle size information, color information, light scattering information, or fluorescence intensity information, or a combination of a plurality of these. The flow-type particle image analysis method according to claim 1, wherein: 3. The particle analysis step analyzes the remaining particles in the still particle image, which could not correspond to one image particle count value of the particle detection system, as a separate frame, and at the end of the analysis, the particles corresponding to one image particle count value. 3. The flow-type particle image analysis method according to claim 1, wherein the processing result of the separate frame is added to the analysis result. 4. The flow type particle image analysis according to claim 3 , wherein in the particle analysis step, processing is performed when the number of images to be processed is small and there is not enough volume to be processed to perform another frame processing. Method. 5. A particle analysis stage, if there is not enough processing target volume to process the number of images to perform fewer separate frame process, according to claim 3, characterized in that the previously inputted set the evaluation procedure
Or 4 either flow type particle image analysis method according. 6. The method according to claim 1, wherein the detecting step corrects an erroneous count caused by emission of a pulse light source for imaging a still particle image with respect to the count value of one image particle and the count value of the total number of particles by the particle detection system. Item 6. The flow type particle image analysis method according to any one of Items 1 to 5 . 7. A particle detection system for flowing a liquid sample to be suspended into a flow cell and counting particles in the sample,
In a flow-type particle image analyzer, comprising: a particle imaging system that captures a still image of particles passing through an imaging region in the flow cell; and an analysis unit that performs morphological classification of particles in the still particle image. Is an image particle count value of the particle detection system, an extraction unit that makes each particle in the still particle image correspond to each other by a particle image identification feature amount, and the correspondence relationship and all particles counted by the particle detection system A particle analyzer for performing either particle distribution or particle classification in the sample based on the number of particles, or a particle analyzer. 8. The analysis object, urine, especially flow type particle image analyzing apparatus according to claim 7, characterized in that the urine sediment components present in the urine. 9. The method according to claim 1, wherein the particle analyzer has a plurality of measurement modes for the same measurement sample, analyzes each measurement mode, and integrates all data after the analysis of all the measurement modes is completed. 9. The flow-type particle image analysis method according to claim 7, wherein: 10. The apparatus according to claim 1, wherein the particle analysis unit calculates the number of particles or a particle ratio per sample volume corresponding to all the image-processed images, and further obtains the number of particles per total sample volume. Claim 7
10. The flow-type particle image analyzer according to any one of items 9 to 9 . 11. The particle analyzer may claim, characterized in particle concentration per unit volume in the particle count or measuring sample, the measuring sample or by being configured to calculate the number of particles of microscopic field terms, 7 11. The flow type particle image analyzer according to any one of items 10 to 10 . 12. The particle analysis unit analyzes the remaining particles in the still particle image, which could not correspond to one image particle count value of the particle detection system, as a separate frame, and sets a particle corresponding to one image particle count value at the end of the analysis. 8. The method according to claim 7 , wherein the processing result of the separate frame is added to the analysis result.
12. The flow-type particle image analyzer according to any one of items 11 to 11 . 13. The particle analysis unit is configured to display or output that there is a problem in the separate frame processing when the number of processed images is small and the processing target volume is small enough to perform the separate frame processing. The flow-type particle image analyzer according to claim 12 .