JP2009002963A - Particle measurement apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a particle measurement apparatus for distinguishing a platelet clump from particles in a sample, and discriminating the platelet clump. <P>SOLUTION: The particle measurement apparatus is constituted of a feature parameter extracting means for extracting feature parameters, including a scattering light signal strength and a scattering light pulse width from the particles in the sample; a distribution chart generating means for generating a distribution chart, based on the extracted scattering light signal intensity and scattering light-pulse width; a discriminating means for discriminating the particles which exist in a predetermined region as the platelet clump. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

The present invention relates to a particle measuring apparatus capable of distinguishing and distinguishing platelet aggregates from particles in a sample.

  Conventionally, counting and analysis of particles such as red blood cells, white blood cells, and platelets has been performed by obtaining electrical or optical characteristic parameters using a flow cytometer. On the other hand, a platelet function test is performed in order to examine a decrease in function or an increase in function of platelets. Platelet function tests include various tests such as platelet adhesion ability and platelet release ability, and platelet aggregation ability tests for examining platelet aggregation are often performed.

  In the platelet agglutination test, the absorbance method is used to measure the degree of platelet aggregation in platelet rich plasma PRP (Platelet Rich Plasma) as a change in absorbance (Journal of Japan Society for Internal Medicine, Vol. 80, No. 6, June 10, 1991) ) Has been used conventionally. In this absorbance method, an aggregation-inducing substance is added to a certain amount of PRP, and the degree of light shielding by the platelet aggregate (absorbance) is measured. A relatively large aggregate (a single platelet aggregates about several hundreds). ).

  On the other hand, the number of platelets may be tens of thousands / μl or less in sudden thrombocytopenia and the like. In addition, platelets are reduced to a level that cannot be counted during chemotherapy or bone marrow transplantation. When platelets decrease, platelet transfusion and administration of hematopoietic agents are required, and the platelet count is used as a criterion for determination. It is important to obtain an accurate platelet count in this way (especially when platelets are low). Appearance of particle components such as dust, bacteria, and bubbles, and electrical noise can be considered as factors that reduce the measurement accuracy of the platelet count (hereinafter collectively referred to as a blank). For example, particle components may be mixed in the sheath liquid immediately after the sheath liquid is exchanged. If these emit a signal similar to the platelet signal and are miscounted, the platelet will show a false high value.

  Regarding the platelet aggregation test, it is known in the clinical setting that platelet function is enhanced in thrombotic diseases such as arteriosclerosis, hyperlipidemia, diabetes, and hypertension, which are adult diseases. Therefore, it is desired to detect a so-called thrombus preparation state before becoming a thrombus early.

  In this thrombus preparation state, weak aggregation of platelets (several aggregations) appears as an initial stage, so a measurement method that can detect weak aggregation is required. Detection of weak aggregation is also desired in the development of antiplatelet agents and safety tests during drug development. In addition, platelet aggregation may occur depending on the specimen even when an anticoagulant is used. When platelet aggregation occurred, platelet count could not be accurately performed, and a false low value of platelet count and a false high value of reticulated platelets were generated.

Accordingly, the present invention has been made in view of the circumstances described above, and to provide a particle measuring apparatus that can determine the platelet clumps from the other grain child.

The present invention, the distribution diagram based on the feature parameter extracting means and the extracted scattered light signal intensity and the scattered light pulse width for extracting feature parameters including scattered light pulse width and the scattered light signal intensity from each particle in the sample and the distribution map creation unit to create a, in the distribution diagram was created, there is provided a particle measuring apparatus and a discrimination means for another platelet clumps and determine the particles present in a given area. Here, the scattered light signal intensity and the scattered light pulse width can be used as the characteristic parameters. In particular, the forward scattered light signal intensity (FSC: Forward Scatter Intensity) and the forward scattered light pulse width (FSW: Forward Scatter Width) are used. Can be used .

According to the present invention, the distribution population of particles distributed on the diagram created distribution based on the feature parameters, it is possible to determine the platelet clumps accurately.

  The present invention will be described in detail below based on the embodiments shown in the drawings. However, this does not limit the present invention. The particles targeted by the present invention are mainly blood cells and cells contained in blood and urine, but microorganisms such as yeast and lactic acid bacteria may be measured. In particular, in the present invention, platelet aggregates and platelets contained in blood are measured.

  In order to classify the type of blood cell or cell, the intracellular granule or nucleic acid may be reacted with a specific fluorescent reagent, and the fluorescence intensity may be measured. Here, in particular, fluorescent reagents that can be used for discriminating platelets from red blood cells or white blood cells include auramine O, acridine orange, propidium iodide, ethidium bromide, Hoechst 33342, pyronin Y, rhodamine 123, and the like. If such a fluorescent reagent is used, the fluorescence intensity of platelet aggregates contained in blood can be preferably measured.

  The characteristic parameter extracting means is means for extracting the characteristic parameters of the particles electrically or optically. For example, from a sheath flow cell that wraps and flows a sample liquid containing particles in a sheath liquid, a light irradiation means that irradiates light to the trickled sample liquid, a detection means that detects light scattered by the particles and outputs an electrical signal Become.

  A sheath flow cell is a device that can form a thin sample liquid flow by hydrodynamic effect by wrapping a sample liquid containing particles in a sheath liquid and using a known one for this. Can do.

  As the light irradiation means, a continuous light source that continuously emits light, such as a laser, a halogen lamp, or a tungsten lamp, can be used. As the detection means, those based on electrical detection, those based on optical detection, and the like can be used. For example, light detection means (for example, a photodiode, a phototransistor, or a photomultiplier tube) that optically detects particles illuminated by the light irradiation means and outputs an electric signal indicating the intensity of light such as scattered light or fluorescence of the particles. ) Can be used.

  The electrical signal output from the detection means is A / D converted for later counting processing and stored as a particle characteristic parameter in a storage means such as a RAM.

  Moreover, you may provide the imaging | photography means which image | photographs the image of particle | grains as needed. As the photographing means, a video camera for photographing a two-dimensional image is usually used, but an image intensifier for amplifying a weak fluorescent image may be used. Further, the image intensifier may be provided with shutter means.

  In addition, it is preferable to provide a light source for irradiating the sample stream with light for particle imaging, which includes a continuous light source such as a laser, a halogen lamp or a tungsten lamp, or a pulsed laser ( For example, an intermittent light source that irradiates light intermittently, such as Spectra-Physics, Inc. (7000 series) or a multi-strobe (eg, Ebara Laboratory, DSX series) can be used.

  When a continuous light source is used for particle photography, it is usually preferable to use it as an intermittent light source in combination with an optical shutter. And as an optical shutter, a well-known acousto-optic effect element (acounsto-optic modulator) or an electro-optic effect element (electro-optic modulator) etc. can be used. It is preferable that the light source and the imaging unit are disposed with the sheath flow cell interposed therebetween, light is irradiated from the light source so as to be orthogonal to the sample flow, and the imaging unit is disposed on the optical axis.

Also, distribution map creation means, determine by hand stage of the invention, CPU, ROM, RAM, a timer, can be constituted by a microcomputer comprising a I / O interface or the like, the function of each means, CPU is stored in the RAM or the like It is executed by operating according to the program procedure.

  The distribution map creating means creates a distribution map based on two feature parameters, and this distribution map is usually called a two-dimensional scattergram. For example, a two-dimensional scattergram is created by plotting the points of each particle with the forward scattered light signal intensity (FSC) on the vertical axis and the fluorescence intensity (FL) on the horizontal axis.

  Further, it can be created by taking the forward scattered light signal intensity (FSC) on the vertical axis and the forward scattered light pulse width (FSW) on the horizontal axis. In general, the forward scattered light signal intensity (FSC) represents the particle size, the forward scattered light pulse width (FSW) represents the particle length, and the fluorescence intensity (FL) represents the RNA content of the particle.

  When a distribution map is created, a distribution group is generally formed for each type of particle. However, the distribution map can be divided into regions for each particle distribution group using statistical techniques. . The “two-dimensional distribution fractionation method” described in JP-A-1-308964 can be used.

  In addition, the distribution group of particles fractionated by the fractionating means may show a distribution having a specific shape, and for example, may be distributed almost in line on a specific straight line or curve. When a distribution group of a certain particle is distributed almost in alignment on a specific curve on the distribution map, this curve is called an estimation curve or a discrimination curve. For example, as will be described later, the platelet population represented by the forward scattered light signal intensity (FSC) and the forward scattered light pulse width (FSW) is aligned on an estimated curve expressed as a function of sin2x.

  The discriminating means can discriminate a particle present in a region separated by a predetermined distance from the distribution group aligned on the estimated curve as a specific particle of interest in some cases, and not in other cases. It can be distinguished from unique particles.

  For example, the discriminating means obtains the frequency distribution of the platelet particle population distributed around the estimated curve, and the counting means is a region (this is the second distance above the estimated curve average distance + 2 × standard deviation distance). Count the particles present in the area). As will be described later, the particles present in the second region counted for the platelet distribution group are groups that have a long pulse width for the pulse height of the signal and do not ride on the estimated curve. Become. This has been confirmed by image capturing of particles by the image capturing means.

  The part of the optical system in one embodiment of the particle measuring apparatus of the present invention is shown in FIG. In this embodiment, two light sources are provided: a continuously generated laser light source 21 for detecting scattered light and fluorescence and a pulse light source 8 for capturing a particle image. Lights L1 and L2 from the two light sources 21 and 8 are irradiated so as to intersect 90 ° so as to be orthogonal to the sheath flow cell 5 (in FIG. 1, the sample flow flows in a direction perpendicular to the paper surface).

  Further, in this embodiment, the pulsed light for capturing the particle image is from the irradiation position of the continuously generated laser light source 21 (for example, about 0.5 mm) with respect to the sample flow in the sheath flow cell 5 as shown in FIG. ) I radiate to the downstream side. By shifting such an irradiation position, it is possible to capture a clear particle image that is not affected by scattered light or fluorescence by cells.

  The continuously generated laser light is irradiated to the sample flow that is narrowed down by the condenser lens 24 and guided to the sheath flow cell 5. When particles flow into this irradiation region, scattered light and fluorescence from the particles are collected by the condenser lens 25, and the scattered light is reflected by the dichroic mirror 26 and received by the photodiode 27. The green and red fluorescences are reflected by the dichroic mirror 28 and the mirror 29, respectively, and received and multiplied by the photomultiplier tubes 30 and 31, respectively.

  The scattered light intensity signal S1 and the fluorescence intensity signals S2 and S3 detected by the photodiode 27 and the photomultiplier tubes 30 and 31 are passed to a signal processing device (not shown), and the height, area, Information such as width is obtained as A / D converted data. For example, four characteristic parameters of scattered light intensity a, signal pulse width b, green fluorescence intensity c, and red fluorescence intensity d are obtained by the signal processing device. Then, using these parameters, the type of each particle is identified in real time.

  The capturing of the particle image by the video camera can be performed by selecting only the types of particles (for example, platelets) designated as the object to be captured in advance. That is, it is compared in real time whether the characteristic parameter of a certain particle matches the characteristic parameter of the type of particle to be imaged, and if it is determined that the particle is to be imaged, the particle is A light emission trigger signal for imaging is supplied to the pulse light source 8.

  The pulsed light source 8 is a type of light source that emits light for a moment (about several tens of nanoseconds) in response to a light emission trigger signal Ts. be able to. As shown in FIG. 2, the pulsed light is guided to the sheath flow cell 5 by the optical fiber 22, is narrowed down by the condenser lens 23, and is irradiated to the sample flow. By irradiating through the optical fiber 22, the coherency of the pulsed light is reduced and a particle image with few diffraction fringes can be taken. The pulsed light transmitted through the sample flow is imaged on the light receiving surface of the video camera 10 by the projection lens 24, and a transmitted light image of the cell is captured. The video signal Vs from the video camera 10 is transferred to the image processing device 11 and stored and stored as a digital image.

  The image processing apparatus 11 analyzes the stored particle image, and calculates image parameters such as edge extraction, area, equivalent circle diameter, and circularity of the particle image. In the present invention, the pulse light source 8, the video camera 10, and the image processing device 11 take an image of particles, and whether or not the measured particles are platelets, divided red blood cells, or divided white blood cells, and dust etc. This is an ancillary device used for confirming whether the particles are unnecessary particles, and does not directly contribute to creation of a two-dimensional scattergram or fractionation processing of each particle region.

  By combining the parameters of characteristic parameters a to d such as scattered light intensity and fluorescence intensity, a two-dimensional scattergram is created and displayed, and a predetermined particle fraction and the number of particles are calculated. The above is the outline of the configuration of the particle measuring apparatus of the present invention.

  Next, an embodiment in which the particle measuring apparatus of the present invention is applied to a blood sample containing platelets to detect and count platelet aggregates will be described. In this case, the target particles are platelet aggregates, and the non-target particles are unaggregated platelets (including reticulated platelets), for example. FIG. 3 shows a flowchart for detecting platelet aggregation according to the present invention. It consists mainly of the following four processes from S1 to S4.

  Pretreatment S1: A 200 μL whole blood sample is aspirated into the measuring apparatus of the present invention, and 10 μL of this is used for this measurement. The sample used for the measurement is immediately mixed with 40 μL of 2.6% auramine O reagent solution (95.9% ethylene glycol) and 1950 μL of diluent (buffer). By this mixing, platelets are fluorescently stained within a very short time.

  Detection process S2: 2.8 μL of the sample liquid mixed and fluorescently stained in the pretreatment S1 is irradiated with an argon ion laser beam of 488 nm in a sheath flow cell. Laser light scattered by each particle in the sample liquid is photoelectrically converted by a photodiode and a photomultiplier, and forward scattered light intensity (FSC) and fluorescence intensity (FL) are measured. The photoelectrically converted electrical signal is sampled and held at the peak value, further A / D converted, and provided as a characteristic parameter to the signal processing apparatus. The forward scattered light pulse width FSW is obtained by analyzing the time information of the FSC.

  Analysis processing S3: First scattergram A with the fluorescence intensity FL on the X axis and the forward scattered light intensity FSC on the Y axis, the forward scattered light pulse width FSW on the X axis, and the forward scattered light intensity FSC on the Y axis The second scattergram B taken is created, each particle population is fractionated on these scattergrams, and the number of dots in the fractionated area is counted to calculate the aggregation rate and the like. This analysis process will be described later.

  Output process S4: The result such as the aggregation rate calculated in the analysis process S3 and the scattergram are output to a display or printing apparatus. The above is the flowchart of the present invention. Four processes (S1 to S4) are repeated for each sample, and the platelet aggregation rate and the like are obtained for each sample. These four processes can be realized by a microcomputer in the signal processing apparatus.

  FIG. 4 shows a flowchart of the analysis process S3. In the signal processing apparatus, a first scattergram A having the fluorescence intensity FL as the X axis and the forward scattered light intensity FSC as the Y axis is developed and created on the RAM (step S5). FIG. 5 shows an example of the first scattergram A. In step S6, the first scattergram A is divided into two areas, the red blood cell area and the platelet area. Here, as a method of fractionation, the “two-dimensional distribution fractionation method” described in Japanese Patent Application No. 1-308964 can be used. According to this method, it is possible to draw a separation line that separates the red blood cell region present in the upper left of the first scattergram A and the platelet region present in the lower portion. The platelet aggregate that is the target particle is present in the lower region.

  In step S7, a second scattergram B with the forward scattered light pulse width FSW as the X axis and the forward scattered light intensity FSC as the Y axis for the particles in the platelet region existing in the lower part of the first scattergram A is Created by expanding on RAM.

  FIG. 6 shows an example of the second scattergram B showing the distribution of particles in the platelet region shown in FIG. According to this figure, it can be seen that the distribution of most particles (here, unaggregated platelets) in the platelet region is aligned on almost one curve. This curve is called an estimated curve. In step S8, this estimated curve is obtained approximately. That is, a function of a curve corresponding to the particle distribution shape is obtained using the least square method. Here, the estimated curve is approximated as a sine square function. Generally, an electric signal detected when a particle passes through a flow cell is taken out as a pulse waveform because it is known that this waveform can be approximated to a sine square function.

When the height of the pulse waveform is Y and the width is X, the relationship between the pulse height and the pulse width is expressed by the following equation.
Y = αsin2X ………… (1)
Here, α is a constant and indicates the peak value (Height) of the pulse height. At one point X1 (0 <X1 <π / 2) on a certain X axis, the pulse width Width is expressed by the following equation.
Width = π-2X1 (2)

At this time, at the discreet position (Thresh) in X1,
Thresh = αsin2X1 (3)
Therefore, the following equation is derived.

図面番号：000003

Drawing number: 000003

  Therefore, the peak value Height of the pulse height is expressed by the following equation.

図面番号：000004

Drawing No.:000004

  On the other hand, in the second scattergram B, the Y-axis forward scattered light intensity FSC corresponds to the pulse height Height, and the X-axis forward scattered light pulse width FSW corresponds to the pulse width Width. The estimated curve shown in FIG. 6 is drawn based on the equation (5). According to FIG. 6, the particle distribution in the platelet region is substantially along the vicinity of this estimated curve, but it can be seen that the particle distribution is farther from this estimated curve as the forward scattered light pulse width FSW increases.

  This indicates that platelets without aggregation have a symmetrical shape that is almost circular, and thus the detected electrical signal has a symmetrical pulse waveform that follows this estimated curve. On the other hand, the agglomerates have a large size and an asymmetric shape, so that the pulse width of the electric signal becomes large. Therefore, as shown in FIG. 6, as the pulse width increases, the particle distribution tends to shift to the right of the estimated curve, and the agglomerates exist in a region deviated from this curve. Thus, the presence of agglomerates in particles having a large pulse width has also been confirmed by imaging of particles in conjunction with the detection process S2.

  Next, in step S9, the frequency distribution of the platelet distribution group distributed near the estimated curve is calculated. That is, the dots of the distribution group on the estimated curve of scattergram B are accumulated, and a frequency distribution is created by plotting the distance from the estimated curve on the horizontal axis and the number of dots on the vertical axis. From this frequency distribution, the average value (AV) and standard deviation (SD) of this distribution group are obtained.

  In step S10, a threshold for determining agglomerates is determined. The threshold value may be input by the user, or may be set in advance in the RAM or the like. Here, based on the frequency distribution, a point of average value + standard deviation × 2 (AV + 2SD) is set as a threshold value, and it is considered that an aggregate exceeding this is included.

  In step S11, dots present in the area exceeding AV + 2SD are counted. The number of dots counted here is defined as the number of aggregates $ Agg. In step S12, platelet aggregation rate Agg%, aggregation concentration Agg #, and the like are calculated.

Assuming that the total number of dots in the platelet region obtained by the scattergram A is $ PLT, the aggregation rate Agg% is obtained by the following equation.
Agg% = $ Agg / $ PLT × 100 (6)
Platelet count PLT # (number / μL) in whole blood is obtained from the following equation.
PLT # = $ PLT ÷ analytical sample volume ÷ dilution ratio …… (7)
Further, the aggregation concentration Agg # is obtained from the following equation.
Agg # = PLT # × Agg% ÷ 100 (8)

In the scattergram B shown in FIG. 6, each numerical value is obtained as follows.
$ Agg = 18
$ PLT = 1059
Agg% = 1.7 (%)
That is, the aggregate of platelets is about 1.7% of the total platelet count, and if PLT # = 250 × 10 3 (number / μL), about 4250 (number / μL) are present.

  In the scattergram of FIG. 6, it is known that the agglomerates are larger toward the right of the X axis. As shown in FIG. 6, two threshold curves are set to divide the aggregate region into three equal parts, and each region is divided into a large aggregate L-Agg, a medium aggregate M-Agg, and a small aggregate S-Agg. It is defined as In addition, the aggregation rates of these three regions are L-Agg%, M-Agg%, and S-Agg%, respectively.

  By obtaining the ratio of the number of particles in each region to the number of particles (Agg #) contained in the aggregate region, L-Agg% = 0.0%, M-Agg% = 0.7 in the specimen of FIG. %, S-Agg% = 99.3%. The region of the small agglomerate S-Agg is a region where about 2 to 4 agglomerates are present according to image photographing, and can be defined as a so-called weakly agglomerated region. Therefore, it is possible to detect the weak aggregation of platelets by obtaining the number of particles and the aggregation rate in the small aggregate S-Agg region by the same procedure as described above for each specimen.

  Next, an embodiment in which the particle measuring apparatus of the present invention is applied to a blood sample containing platelets to correctly detect and count platelets will be described. In this case, the target particle is platelet (including reticulated platelets), and the non-target particle that inhibits the detection of the target particle is, for example, a blank.

  The laser beam intensity in the flow cytometer exhibits a Gaussian distribution, and is condensed into an elliptical shape having a short diameter (flow direction diameter) of, for example, 8 μm and a long diameter (direction orthogonal to the flow) of, for example, 200 μm at the center of the flow cell. Yes. This is to prevent the irradiation intensity of the laser beam from changing depending on the position of the particles in the sample flow. Incidentally, the laser beam is also applied to the sheath liquid flow outside the sample flow. If dust or bubbles are mixed in the sheath liquid, the optical signal generated by these particles is detected by the photodetector. Since the amount of sheath liquid flowing through the flow cell is overwhelmingly larger than the amount of sample liquid, there is a problem even if a small amount of particle components are mixed. Although it is conceivable to remove particulate components in the sheath liquid using a filter, the fluid system becomes complicated. Therefore, this problem is solved from the aspect of signal processing.

  The sheath liquid and the sample liquid form a laminar flow in the flow cell, and the particles passing near the inner wall of the flow cell have a velocity about half the center flow velocity. That is, the flow velocity of the particles in the sheath liquid flow is slower than the flow velocity of the particles in the sample flow. From this, particles mixed in the sheath liquid become a signal having a pulse width larger than the pulse height compared to the particles in the sample liquid, and it is possible to identify both. Shot noise is caused by noise from an electric element or an electric circuit in the apparatus or from the outside. Since these noise signals contain high frequency components, the pulse width is small for the pulse height compared to the particle signal. Therefore, it is possible to identify both.

  Hereinafter, the analysis process will be described. FIG. 7 shows a flowchart of analysis processing in this embodiment. First, a first scattergram C is created (step S5). FIG. 8A shows an example of the first scattergram C. The scattergram C is obtained by measuring blood by intentionally mixing minute particle components in the sheath liquid. A blank area in which a blank may appear from the platelet area is subdivided (step S13). In this case, the blank area is an area close to the origin in the platelet area.

  Next, a second scattergram D is created for the particles in the blank area (step S7). FIG. 8B shows an example of the second scattergram D. Particles in a region away from the estimated curve are identified as blanks and distinguished from populations. Platelets flow at high speed in the center of the flow cell and are symmetrical and follow the estimated curve. On the other hand, since the particles in the sheath liquid flow through the flow cell at a low speed, they are distributed to the right of the estimated curve. If a noise signal with a narrow pulse width exists, it is distributed to the left of the estimation curve. The number of particles determined to be blank is counted and subtracted from the number of particles in the platelet region of the first scattergram C to obtain the number of platelets (step S14). The platelet count was 19.0 [× 10 4 / μl] when the blank was not determined, but was 16.1 [× 10 4 / μl] when the blank was determined.

  This embodiment is equipped with an imaging unit composed of a pulse laser for imaging and a video camera, an imaging control unit for operating the imaging unit, and an image processing unit for image processing of the obtained image, and obtained by a light detection means. A characteristic parameter may be extracted from the obtained particle signal, and the sample flow region may be photographed based on the characteristic parameter value. When the particles are present in the sample flow, the particles are reflected in the image. However, when the particles are present in the sheath liquid, the particle image is not reflected. Therefore, the image processing unit determines the type of particle based on whether or not there is a particle in the captured image.

By installing this particle imaging and image determination function, it is possible to determine particles more accurately. Specifically, when the three characteristic parameters obtained from the particles correspond to the blank area of the scattergram C and the area away from the estimated curve of the scattergram D, based on the detection information. The imaging device is operated to obtain a still image of the sample flow region. The image processing unit determines that the image is a blank in the sheath liquid if no particle image is present in the image, and determines that the image is a platelet (for example, a distorted platelet). Can do. If it is determined that G images of N particles existing in a region distant from the estimated curve of the scattergram D and g particles are determined to be platelets, N · g is included in the region. / Equivalent when there are G platelets. Therefore, if the value is added as the platelet count, the platelet count can be obtained with higher accuracy.

It is a block diagram of the part of the optical system in the measuring apparatus of this invention. It is a block diagram of the flow cell part in the measuring apparatus of this invention. It is a flowchart of the detection of platelet aggregation of this invention. It is a flowchart of analysis processing S3 of this invention. It is one Example of the scattergram regarding this invention. Fig. 6 is a scattergram for the platelet region in Fig. 5. It is a flowchart of the analysis process in the Example of this invention. It is the scattergrams C and D in the Example of this invention.

Explanation of symbols

5 sheath flow cell 8 pulse light source 9 projection lens 10 video camera 11 image processing device 21 continuous light emitting laser 22 optical fiber 23 condenser lens 24 condenser lens 25 condenser lens 26 dichroic mirror 27 photodiode 28 dichroic mirror 29 mirror 30 photomultiplier tube 31 photoelectron Multiplier

Claims (3)

Feature parameter extraction means for extracting feature parameters including scattered light signal intensity and scattered light pulse width from each particle in the sample;
And the distribution map creation unit to create a the distribution diagram based on the extracted scattered light signal intensity and the scattered light pulse width,
In the distribution diagram was created, the particle measuring apparatus and a discrimination means for another platelet clumps and determine the particles present in a given area.   The particle measuring apparatus according to claim 1, wherein the scattered light signal intensity is a forward scattered light signal intensity, and the scattered light pulse width is a forward scattered light pulse width.   The particle measuring apparatus according to claim 1 or 2, wherein the predetermined region is a platelet aggregate region, and the distribution map creating unit creates a distribution map in which the platelet aggregate region is set.

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