JPS6040954A - Automatic calculator of reticulate erythrocytes - Google Patents

Abstract

PURPOSE:To obtain an automatic calculator of erythrocytes of high identification rates of erythrocytes, reticulate erythrocytes, pseudo-reticulate erythrocytes, by developing means of obtaining density hystograms of erythrocytes of colored blood specimen and reticulate erythrocytes and of obtaining the specified new parameters. CONSTITUTION:A ray of transmitted light from a source 1 of a blood specimen 3 subjected to supratital staining is color-analyzed into red R, green G, blue B by color TV4, A/D convertor 5 and their digital signals are delivered to IC memories 6a, 6b, 6c. A density hystogram of total blood cells is made by hystogram forming circuit 8- labelling circuit 11 and a density f corresponding to alpha% of the max. frequency HT is obtained. A hystogram of each blood cell is made by planimeter circuit 7a, periphery length meter circuit 7b, parameter extracting circuit 15 of labelled image. By obtaining density d of each max. frequency H0 corresponding to alpha% and the max. density e, logarithmic values of density difference Dde, shaded area Sde, difrence Dfe of said f and the max. density e and area Sfe from area f up to area e respectively. Thus, log Dfe, log Sfe are obtained for accurate and rapid calculation of the reticulate erythrocytes.

Description

Detailed Description of the Invention [Field of Application of the Invention J The present invention relates to an apparatus for identifying red blood cells and reticulocytes using color information of a stained blood specimen and automatically calculating reticulocytes using the results. It is something.

[Background of the invention]

New methylene blue
In a fresh blood specimen subjected to supravital staining with blue, etc., reticulocytes are immature red blood cells in which purplish-blue granular and string-like substances are entangled in greenish-yellow red blood cells. Nucleated erythroblasts enucleate within the bone marrow and emerge into the peripheral blood, but in the process they inevitably pass through the reticulocyte stage. An increase or decrease in the reticulocyte ratio in peripheral blood results in an increase or decrease in immature red blood cells, that is, an increase or decrease in erythropoietic ability. Therefore, reticulocyte count is an important parameter to know erythropoiesis.

This reticulocyte counting test is a test in which collected blood is subjected to supravital staining, smeared on a slide glass, dried, and then the number of reticulocytes per 1000 red blood cells is counted under a microscope. This test is similar to the white blood cell classification test,
Automation of the inspection is strongly desired, as inspection technicians perform the inspection visually under a microscope, which is cumbersome and causes a lot of eye fatigue.

[Purpose of the invention]

Based on the above facts, the present invention aims to provide an apparatus for automating a reticulocyte count test.

[Summary of the invention]

The automatic reticulocyte counting device of the present invention separates the transmitted light of a stained blood specimen into a plurality of single colors.

In an apparatus for collecting extracted red blood cells and reticulocytes using this separated information, there is a means for calculating the concentration histogram of each blood cell described above, and a frequency value of a predetermined cent of the maximum value of this histogram. means for calculating the concentration value of the high concentration area having the concentration, means for calculating the sum of the frequencies of the concentration or higher, means for calculating the difference between the maximum concentration value of the blood cells and the concentration value of the high concentration area, and the above-mentioned It is characterized by comprising means for logarithmically converting the total number of frequency values of and the difference between the density values.

Other features of the invention will become apparent from the following description of the embodiments.

[Embodiments of the invention]

FIG. 1 is a diagram showing the configuration of an embodiment of the present invention. Light from a light source 1 is guided to a blood specimen 3 placed on the stage of a microscope 2, and transmitted light from the blood specimen 3 is transmitted to a color television camera 4.
The color is separated into red (R), green (G), and blue (B) components. These color signals are converted into digital signals by the iA/D converter 5.

Store it in the IC memory 6. For example, the ratio signal is stored in the IC memory 6a, the B signal is stored in the %IO memory 6b, and the G signal is stored in the 10 memo IJ6c.

Generally, in the images obtained from television cameras.

Since there are several to dozens of red blood cells, it is necessary to label each red blood cell in order to process each red blood cell separately. Creation of labeled mask images will now be described.

Bu! m(I! Read from the component BIO memory 6b and create a concentration histogram as shown in FIG. 2 in the histogram creation circuit 8. As can be seen from this diagram, each blood cell component BL
Since the background component HG is distributed in the high concentration area and the background component HG is distributed in the low concentration area, the boundary value between the two, that is, the concentration value S(g)' corresponding to the valley part V of the concentration histogram above, is Use as a threshold for separation. Reference numeral 9 denotes a threshold detection circuit that detects this concentration threshold 8 (g)'<.

Next, the above-mentioned B image component is read out from the IO memory 6b, and a binarized image is created in the comparison circuit 10 by setting the above-mentioned density value S(g)' to the threshold level.

For example, a binarized pattern in which the blood cell portion is 1 m and the other portions are “0” is created and stored in the IC memory 6d. This binarized pattern is read out again from the IO memo 176d, and each blood cell image is labeled using the labeling circuit 11 and stored in the IC memory 6d again. In this way, once the labeled mask image is labeled on each zero-plane spherical image stored in the IC memory 6d, the process described below is repeated for each label. 1 until the total number of processed blood cells reaches the number set at the beginning.
For example, the stage 0 microscope repeats inputting one image by changing the field of view of the stage of microscope 2 until it reaches 1000 pieces.
Stage control circuit 24 provides stage movement and automatic focus adjustment.

Next, a process for identifying red blood cells and reticulocytes will be explained. First, extraction of feature parameters necessary for identifying red blood cells and reticulocytes will be explained.

This extraction of feature parameters is performed using the mask image, 0 image, and clear image stored in the IC memory 6. That is, the labeled mask image is read out from the IO memo 176d, and the area calculation circuit 7a.

From the circumference length calculation circuit 7bl, the area S1 of each blood cell
Increase the circumference. Next, from the above mask image and the 0 image stored in the IO memory 6C.

A density histogram of the whole blood cell image within the screen is created by the histogram creation circuit 8. This thick JJjhi
An example of the gram is shown in FIG. From this density histogram, the maximum value HI3 of the density histogram is first determined, and then the high density side density value fK corresponding to the frequency value of α percent of the maximum value HT is determined by the L threshold density detection circuit 12.

Next, from the mask image 10 memory 6d, the G image 410
The density histogram is read out from the memory 6c and using the histogram creation circuit 8 for each blood cell unit in the 0 image as shown in FIGS. 4(A) and 4(B). FIG. 4(A) shows a concentration histogram of red blood cells, and FIG. 4(B) shows a concentration histogram of reticulocytes. As can be seen from both figures, the histograms of the high density portions of the two are clearly different, and by utilizing this property, it is possible to distinguish between the two. Using this density histogram, the maximum value Ho% of the density histogram is determined by the threshold density detection circuit 13, and then the density value d on the high density side corresponding to the frequency value of α percent of the maximum value HO, and the maximum density value Add e. Here, the value of α is a statistically determined value, and varies depending on the staining conditions of the blood specimen, but good results have been obtained with a value of 20 to 30 degrees. Similarly, 17 images are read from the IC memory 6a, a density histogram is created for each blood cell image in the R image, and a density value corresponding to α percent of the maximum histogram and a maximum density value are calculated. GIIfi
The portion having a higher density than the above density value d determined from the I image corresponds to the reticulum of reticulocytes in the actual blood cell image. Therefore, with this density value di at the threshold level, 10 memories 6
Using the 0 image of C, the comparator circuit 14 detects the reticular body, considers the part darker than dark #d to be the reticular part, sets this part as "1", other parts as 0', and stores it in the IC memory. 6
Store in b. Next, the mesh orientation *V obtained in this way is read again from the ■0 memo IJ 6 b, and the perimeter length calculation circuit 7b calculates the mesh perimeter LR.

Feature parameters other than the above are obtained by the parameter extraction circuit 15.
Demeru. A detailed explanatory diagram of this parameter extraction circuit 15 is shown in FIG. 030 is an adder that calculates the feature parameters using the maximum density value e, and the density histogram H(g)'i
=.

The amount can be added and requested from the density value d to the maximum density value e. On the other hand, 31 is a subtracter that calculates J) ae=ed. 32 and 33 are logarithmic converters, each having a value of 10g8de. 8de
corresponds to the area from the density value d to the maximum density value e of the density histogram for each blood cell image shown in Figures 4 (A) and 4 (B), that is, the area of the reticular body (area of the shaded area).
pdn is the density difference between the highest density value e and the density value d.

Similarly, an adder 34 adds the density histogram H(g) from the density value f to the highest density value e.

8te=ΣH(g) 〇On the other hand.

35 is a subtracter, which calculates D(e=e f 036 and 3
7 is a logarithmic converter, respectively jog S te and jo
Enter gDr@.

38 is the square root of the sum of squares between S de and Dd, that is, D...-/Tko]]=7 This is an arithmetic circuit that calculates . This parameter is.

When performing Heilmeyer's classification of reticulocytes.

The important parameter is 0. Heilmeier's classification described above distinguishes reticulocytes into 5 types based on differences in the density of linear granules.

Type 4, type 3, type 2, and type 1 from those with sparse reticular formations.

It is expressed as type 0 (Masafumi Komiya, “Illustrated Guide to How to Look at Blood Cells” Nanzando).

The purpose of the logarithmic transformation of /< parameters Sde, Dde, 8fa, and Dte'2 in the parameter extraction circuit 15 is as follows. Here, the parameter log
S de and log l) dei will be explained as an example. The distribution of the parameters 8 ds and Ddε for red blood cells before conversion is concentrated near the origin, so the center of the distribution is close to the origin and has a long tail toward the reticulocyte region. In this state, the above ・(rameta Sde and 1)
When red blood cells and reticulocytes are distinguished using the magnitude relationship of da, red blood cells located in a boundary area where red blood cells and reticulocytes coexist may be determined to be reticulocytes. Therefore, the parameter jo obtained by logarithmically transforming the above parameters 8 de and Dds
The relationship between g8a* and log D de and red blood cells and reticulocytes is shown in FIG. 6. In FIG. 6, the above parameter jog D da f is defined on the horizontal axis, and the above parameter jOgsda% is defined on the vertical axis, and the straight line L defined by these parameters 1ogJ)ds and jogsaa is defined.
The upper side of R (discrimination function) is the area where reticulocytes exist, and the area below the straight line LR is the area where red blood cells exist. Therefore, the R-specific circuit 16 identifies blood cell images having values larger than the above-mentioned straight line LR as reticulocytes, and blood cell images having a smaller value as red blood cells. In this way the parameter 5de
When I and Dde are transformed logarithmically.

The distribution center of red blood cells moves away from the origin and approaches the shape of a normal distribution, and the probability of red blood cells occurring is 100 times greater than that of reticulocytes, so all red blood cells in the boundary area where red blood cells and reticulocytes coexist are identified as red blood cells. be done. Therefore, the accuracy of identifying red blood cells can be 99.9% or higher. For reticulocytes, the identification accuracy is about 90%.

Also, from the mi histogram of the whole blood cell image within one screen, the concentration value f4 of the α percent point is determined.

Feature parameters log 8 te and log D te
The reasons for the request are as follows. For types 3 to 4 of the Heilmeier classification above, the manufacturing histogram H(g)
is as shown in FIG. 7(A). On the other hand, as in types 0 to 2 of Heilmeier's classification, when the concentration of the reticulocyte reticulum is high and the area is large, 1 [histogram H (g
) is shown in Figure 6(B). Therefore, the concentration value of the α percent point calculated from the concentration histogram I-i (g) in blood cell image units is as follows.

It is set at point d in FIGS. 6(A) and (B). Therefore, the above-mentioned parameters log S dg and jogDd
e may not be able to extract the features of reticulocytes.

That is, the above parameters 10g S da and Dd
When red blood cells and reticulocytes are identified using eK, there is a possibility that some of the red blood cells that are determined to be red blood cells may include reticulocytes of Heilmeyer classification θ~2, making it difficult to accurately distinguish between red blood cells and reticulocytes. Sometimes there isn't. to solve this problem.

The above problem is solved by determining the concentration value f% of the α percent point from the concentration histogram of a whole blood cell image within one screen and using the averaged concentration value over one screen. In other words, Figure 4 (
As can be seen from FIG. 7(B) showing the concentration histogram of reticulocytes of Heilmeyer classification 0 to 2, the histograms of the concentration region higher than the above concentration value f are clearly different, and the parameter jog S cm and log D
By using t@, it is possible to distinguish between the two.

The above parameters log S to and log Dr
The relationship between e, red blood cells, and reticulocytes of Illumaia classification θ~2 types is shown in Figure 8. Here, in FIG. 8, the above parameter jog S te is defined on the horizontal axis.

The above parameter jogDte is set on the vertical axis.

These parameters jog S re value and log D t
The upper side of the straight line WLR' (discriminant function) determined by the e value is the area where reticulocytes exist, and the area below the straight line LkL' is the area where red cells exist. Therefore, blood cells having a value larger than the above-mentioned straight line L' are identified as reticulocytes, and blood cells having a smaller value are identified as red blood cells.

Similarly, the parameter extraction circuit 15 is used to extract characteristic parameters for the 8 images, but since the amount of characteristic parameters obtained from the 8 images is almost the same as that for the 0 image, various parameters are not necessary. Parameter 8
You only need to look at aa and Dae.

Next, using the above food feature parameters.

The discrimination circuit 16 in FIG. 1 classifies the cells into red blood cells, reticulocytes, and others, and the cells are counted using counters 18°, 19.20°, respectively. This operation is repeated until the total number of red blood cells and reticulocytes reaches a predetermined number. Next, identification circuit 1
Those determined to be reticulocytes in step 6 are also reclassified by the Heilmeyer classification identification circuit 17, and the results are counted by predetermined counters 21, 22, and 23.

25 is a control circuit for controlling the entire automatic reticulocyte counting device according to the present invention.

Reticulocyte identification in the identification circuit 16 is performed by multi-stage identification branching logic. In the first step, the size and size of connected red blood cells are determined using parameters such as the area S and circumference of each blood cell image. Remove trash, etc. Next, in the second step, red blood cells and reticulocytes are identified by a secondary discrimination function using feature parameters of jog8d@ and logDae from the 0 image. That is, as shown in Figure 6,
Based on the secondary discrimination function L)L, blood cells whose parameters log8ae and jogl)ds are larger than the above function are determined to be reticulocytes, and blood cells whose values are smaller than the above function are determined to be red blood cells. Among those determined to be red blood cells in the '2nd stage, 1
As shown in M7 diagram (B), No. Illumaia classification O~
Because it may contain type 2 reticulocytes,
At step 1, the characteristic parameters of jogsra and logDte are used to detect the above-mentioned reticulocytes and count them into type 0 to 2 of the WoI-Illmaia classification. On the other hand, for those determined to be reticulocytes in the second stage, the reticular body area Sde, perimeter LR, fa degree difference pde, etc.
Feature parameters from the 0 and R images are used to distinguish between reticulocytes and pseudoreticulocytes, which are red blood cells with tower-like wrinkles. The characteristics of the pseudoreticulocyte described above are that although it looks like a reticulocyte, the area of the reticulum is relatively small;
They have a simple shape, that is, a short circumference, and a higher concentration than reticulocytes. The characteristics of Kowara are
All the above feature parameters Sdg, LR and Dde
The fourth step is to distinguish between reticulocytes and pseudoreticulocytes using these parameters. Blood cells other than reticulocytes are classified as other and excluded from processing.

Those determined to be reticulocytes in the fourth stage are primary.

The Heilmayr classification identification circuit 17 performs the Heilmayr classification. That is, the parameter S obtained from the 0 image
The relationship between dg and Dda and reticulocytes according to No. Illumaia classification is shown in Figure 9.

Here, in FIG. 9, the above parameter S da is defined on the horizontal axis, and the above parameter Dae is defined on the vertical axis. As is clear from FIG. reticulocytes HO~2 of Heilmayr classification O~2 are far from the origin n...Illmayr classification 3
The reticulocyte type H3 is located between the above-mentioned H4 and Ho~2. therefore.

Feature parameter p1@-8d corresponding to the distance from the origin
By using a2+ D da2 , it becomes possible to classify I. Illmaia based on the magnitude relationship of this parameter DII. That is, for the distances L1 and L2 from the origin determined by the value of the parameter Dl above.

1) Reticulocytes with +a≦Ll are Heilmayr type 4 (H4), and reticulocytes with Ll (DI-≦L2 are...Illmayr type 3 (Hri, DI-':>L)
2 reticulocytes are classified as] Illumaia classification type 0 to 2 (H
It is determined that a to 2). This result is.

They are counted by counters 21, 22, and 23, respectively.

In this embodiment, reticulocytes of Heilmeier classification O-2 are very small, totaling less than 0.1% in normal people, so subclassification into θ-2 is not performed.

The above identification circuit 16 and Heilmayr classification identification circuit 17
can be easily constructed using known circuit components, but can also be identified using a computer.

The automatic reticulocyte counting device according to the present invention has been described above, but by using a blood sample that has undergone the pretreatment described below, it is possible to reduce pseudo reticulocytes that have been a problem in the reticulocyte counting process. A detailed explanation will be given of the pseudo reticulocyte, which enables more accurate reticulocyte calculation.

Reticulocyte count was performed using new methylene blue (Newmet).
In this case, a fresh blood specimen that has been stained with supravital staining such as hylene blue is examined under a high-magnification optical microscope using an oil immersion objective.

Depending on the drying conditions of the blood specimen mentioned above and the length of time elapsed in the autopsy examination, wrinkle-like deformation may occur near the center of each red blood cell. Examine the specimen with an oil immersion objective lens in this manner. In this case, the oil cannot penetrate into the wrinkled areas of the red blood cells, and the microscopic image of the red blood cells looks very similar to the reticulocyte image. (Such red blood cells are called pseudo-reticulocytes.) These pseudo-reticulocytes are placed in the above-mentioned automatic reticulocyte counting device and calculated using characteristic parameters such as the area of the reticulum (8 da1), the circumference La, and the concentration difference Ddll. Red blood cells and pseudoreticulocyte pseudoreticulocytes are distinguished, but the frequency of pseudoreticulocyte occurrence is as low as a few per 1,000 red blood cells, and as high as 100 or more, whereas in people with normal reticulocytes, The occurrence rate is very small at 3 to 11 per 1000 red blood cells, and it is clear that the presence of pseudoreticulocytes has a great influence on the calculation results. The stained blood specimen is immersed in an organic solvent for a predetermined period of time.

By applying oil for microscope oil immersion lenses to the blood sample, wrinkle-like deformation caused in the red blood cells is removed.

An embodiment of this blood sample pretreatment device is shown in FIG.

In FIG. 10, 101 is a cassette case for storing a blood specimen 102, and 102 is stained with supravital staining. 103 is organic solvent xylene, 104 is oil for microscope oil immersion lenses (for example, US L!ilR, 1', 0ARGILF
i LABORATORIE8゜INO, TY of company 31
PlaA), 105 is a tube that guides the front 104 to the blood sample 102, 106 is a lever that moves the blood sample 102 to the position where the oil is dripped, 107 is a drive unit for the lever 106, and 10B is a tube that guides the blood sample 102 after dropping the oil. This is the mechanism that returns the cassette to the cassette position. Blood specimen 102 stored in cassette 101 is first immersed in xylene solution 103 for 1 to 10 minutes. The soaking time is adjusted depending on the drying conditions and elapsed time after dyeing. however,
If soaked for more than 10 minutes, the stained red blood cells will begin to decolorize, so it is undesirable to soak for too long. Next, in the step of pulling up the blood specimen from the xylene solution, the lever 106
Push out the blood specimen one by one from the cassette l-101, apply oil 104 to the surface of this blood specimen, and apply it to the tube 105.
Drop an appropriate amount of oil onto the surface of the blood sample and smear it evenly on the surface of the blood specimen. Note that smearing can also be done by pushing the oil through a slit-like hole or by applying the oil with a brush. Of course. At this time, the above xylene 1
It is desirable to drop the oil as soon as possible before the 03 has completely evaporated from the surface of the blood specimen. After dropping, the blood specimen is reset into the cassette 101.

Next, move the cassette (101re) upward by one blood specimen and repeat the above operation. Power, Sera) 10
Since the lowest blood sample 102 in No. 1 is immersed in the xylene solution 103 for the longest time, it is desirable that this time not exceed 10 minutes.

After performing the above operations,! The initial pseudoreticulocytes penetrate into the wrinkled deformed part, and the stains on the blood specimen due to oil are removed, and when the oil for oil immersion lenses is dropped, this oil It can fully penetrate into wrinkle-like deformed areas and eliminate most of the pseudoreticulocytes. If pseudo reticulocytes remain in the blood specimen, repeating the above procedure one more time will completely remove the pseudo reticulocytes. For the second operation, you may repeat the above operation while leaving the oil for the oil immersion lens smeared in the first time. You can also examine it with a microscope.

Since the oil itself does not evaporate, the wrinkle-like deformation will remain in a state where it has been eliminated even if it is left for a long time, so it may be examined under a microscope after an hour.

In the above explanation, the case where xylene was used as an organic solvent was explained;

Reacts with glass, biological materials, staining dyes, etc. and causes denaturation.

An organic solvent can be used that has the same effects as xylene, such as not causing dissolution or corrosion, and (1) volatilization and being easily removable.

As explained above, the present pretreatment method can be used even for blood samples in which the frequency of occurrence of pseudoreticulocytes is 100 or more per 1000 red blood cells.

The frequency of occurrence of pseudoreticulocytes can be extremely reduced to 1 or less per 1000 red blood cells, and an extremely large effect can be expected in reticulocyte calculation processing.

I pulled it out. The blood sample pretreatment device shown in FIG.

Incorporated into the automatic reticulocyte counting device shown in Figure 1.

It is also possible to configure a fully automatic reticulocyte counting device by adding an autoloader that has the function of automatically setting a blood specimen treated for wrinkled red blood cells under a microscope.

〔Effect of the invention〕

The present invention has been described above using one embodiment. The effects of the present invention will be described below.

1. Introducing new characteristic parameters in reticulocyte calculation 1. Red blood cells and reticulocytes.

The identification rate of pseudoreticulocytes was improved. Especially log8 do
, log D da, and jog S re ,
The introduction of log Dra increases the identification rate of reticulocytes and.

It is effective in reducing the rate of misidentification of red blood cells as reticulocytes.

2. The second feature of the present invention is the feature parameter DI that enables Heilmeier's classification to further subdivide reticulocytes.
-f is introduced and Heilmeier's classification is performed.

3. The present invention enables automation and high precision of reticulocyte calculation,
This makes it easier to speed up the process, and it also greatly reduces the labor required for inspection work performed under a microscope by inspection technicians.

4. The present invention provides a comprehensive blood image automatic analyzer that can be easily incorporated into a conventional blood image automatic analyzer 1%, a white blood cell morphology tester, a red blood cell morphology analyzer, and a reticulocyte counting device. be able to.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of an embodiment of an automatic reticulocyte counting device according to the present invention, and FIGS. 2, 3, and 4 (
A), Figure 4 (B), Figure 6, Figure 7 (A), Part 7 (
B), Figures 8 and 9. 2 is a diagram for explaining the operation of the configuration shown in FIG. 1. FIG. FIG. 5 is a diagram showing the configuration of an embodiment of the main part shown in FIG. 1, and FIG. 10 is a diagram showing an embodiment of the blood sample pretreatment device. Figure 2 Figure 5 Figure θ Obstacle gs〆e Figure 5 Figure 7 (A (3) ￥″J 8 Figure High q Continuation of figure 1 page 0 Inventor Akira Hashi Tsume Higashikoi Uo Research Institute, Kokubunji City 0 Invention Author: Shinji Yamamoto Kokubunji City Higashikoi Uo Research Institute, Kubo 1-28 Hata Hitachi, Ltd. Naka, Kubo 1-28 Hata Hitachi, Ltd. Intermediate Procedural Amendment (Method) % Formula % Title of Invention Assisting the person who makes the automatic reticulocyte counting device correction III'l To the patent applicant Name: '5101 Certification Meeting] 1. A brief explanation of one aspect of the Ministry of Specification subject to amendment by the agent. Column hli
jlE n Contents Page 24, line 16 of the specification “Part 7 U
Correct the month to "Figure 7 β month.

Claims (1)

[Claims] (1) An apparatus that separates the transmitted light of a stained blood specimen into a plurality of single colors and collects extracted red blood cells and reticulocytes using the separated information, Means for calculating the concentration histogram of each blood cell, means for calculating the concentration value of a high concentration portion having a frequency value of a predetermined percentage of the maximum value of this histogram, and means for calculating the sum of frequencies equal to or higher than this concentration. , means for calculating the difference between the maximum concentration value of the blood cells and the concentration value of the high concentration portion, and means for logarithmically converting the sum of the frequency values and the difference between the concentration values. Automatic reticulocyte counting device 02 characterized by the ability to identify
, in the automatic reticulocyte counting device according to item 1. The means for calculating the concentration value of the high concentration part includes means for calculating a concentration histogram of whole blood cells within one screen, and the concentration value of the high concentration part having a frequency value of a predetermined percentage of the maximum value of this ai histogram. An automatic reticulocyte counting device comprising: