JPH02107964A - Automatic apparatus for calculating reticular cell - Google Patents

Abstract

PURPOSE:To enable automatic and accurate execution of reticular cell calculating inspection by determining the density width of a prescribed part of high density in a histogram of each image of a red blood cell. CONSTITUTION:A light from a light source 1 is led to a blood sample 3 and the light transmitted therethrough is led to a color TV camera 4 to be subjected to color separation. Color signals thus obtained are converted into digital signals by an A/D converter 5 and stored in a memory 6. Subsequently, a density histogram of an image of a red blood cell extracted from a microscopic image of a sample 3 is determined by a histogram preparation circuit 8 and the density width of a prescribed part of high density in the density histogram of each image of the red blood cell is determined. The value of this density width is subjected to logarithmic transformation and a value thus obtained is extracted as a parameter. When the value obtained by subjecting the density width of the part of high density to the logarithmic transformation is taken for the axis of abscissa, the distributions of the red blood cell and a reticular cell approach regular distributions, and therefore the high value limit of the parameter for determination as the red blood cell can be recognized accurately. By taking as the parameter the value obtained by the logarithmic transformation, accordingly, automatic discrimination of high precision can be realized.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention uses images of stained blood specimens to detect red blood cells,
and an apparatus for identifying reticulocytes and automatically calculating reticulocytes using the results.

[Conventional technology]

New methylene blue
Reticulocytes are young red blood cells in which purplish-blue granular material and string-like material (reticular bodies) are entangled in greenish-yellow red blood cells in fresh blood specimens that have been stained with supravital staining such as . Red blood cells with a nucleus enucleate within the bone marrow and emerge into the peripheral blood, but in the process they necessarily pass through the reticulocyte stage. Increases and decreases in the reticulocyte ratio in peripheral blood are
Hail the increase and decrease of immature red blood cells, that is, the increase and decrease of red blood cell production 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,
Testing is performed visually under a microscope by a laboratory technician, which is cumbersome and causes a lot of eye fatigue, so there is a strong desire to automate the test.

Therefore, it is believed that it is possible to automatically distinguish between reticulocytes and mature red blood cells by determining the density histogram of each red blood cell image and detecting the presence of reticuloid bodies, which characterize reticulocytes, by the presence of high-density parts of the histogram. [51-3
This was proposed in Publication No. 3758, etc. However, the area and density of the image of the reticular body vary depending on the time elapsed since the red blood cell enucleation, and the density histogram of mature red blood cells (hereinafter simply referred to as red blood cells) also depends on the degree of staining, the occurrence of scratches on the red blood cell surface, etc. Depends on it. Therefore, accurate automatic calculation of reticulocytes requires various measures such as how to take parameters for distinguishing between red blood cells and reticulocytes, and the above-mentioned known examples do not disclose anything regarding this.

[Problem to be solved by the invention]

Based on the above facts, the present invention aims to provide an apparatus that automatically and accurately performs a reticulocyte count test.

[Means to solve the problem]

The automatic reticulocyte counting device of the present invention is a device for identifying red blood cells and reticulocytes by determining the density histogram of a red blood cell image extracted from a microscopic image of a blood specimen. The present invention is characterized in that it includes means for determining a concentration range and means for logarithmically converting the value of the concentration range, and that red blood cells and reticulocytes are discriminated using the logarithmically transformed value as a parameter.

According to another feature of the present invention, the width of the high-density portion and the sum of the frequency values of the high-density portion (corresponding to the area of the high-density portion in the image) are logarithmically transformed. Identify red blood cells and reticulocytes as.

[Effect]

In this way, using a logarithmically transformed value as a parameter has the meaning described below. In other words, if we examine the distribution of a large number of red blood cells and reticulocytes by taking the concentration width of the high concentration area as the horizontal axis, we find that the center of distribution for red blood cells is near the zero point on the horizontal axis, and for reticulocytes is located further away. Can be seen. Each distribution has an overlapping portion, and each distribution is not a normal distribution but has a long tail on the side far from the zero point. Therefore, if the value of the concentration range of the high concentration portion itself is used as a parameter, it is difficult to set the high value limit value of the parameter for determining red blood cells, and as a result, highly accurate automatic identification cannot be performed.

On the other hand, if the horizontal axis is the logarithmically transformed value of the concentration width of the high concentration area, the distribution of red blood cells and reticulocytes approaches a normal distribution, so the high value limit of the parameter for determining red blood cells can be accurately identified. . Therefore, highly accurate automatic identification can be achieved by using logarithmically transformed values as parameters.

Other features of the invention will become clear from the description of the embodiments below.

[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.
Lead to red (R), green (G).

Color separation into blue (B) components. These color signals are converted into digital signals by the A/D converter 5 and stored in the IC memory 6. For example, the R signal is stored in the IC memory 6a, the B signal in the IC memory 6b, and the G signal in the IC memory 6c.

Generally, since several to ten or so red blood cells are present in an image obtained from a television camera, it is necessary to label each red blood cell in order to process each red blood cell individually. Creation of labeled mask images will now be described.

The B image component is read out from the IC memory 6b and the histogram creation circuit 8 creates a density histogram as shown in FIG. As can be seen from this figure, each blood cell component BL is distributed in the high concentration area, and the background component BG is distributed in the low concentration area, so the boundary value between the two, that is, the concentration value S (g ) is the threshold for separating the blood cell part from the background part. 9 is a threshold detection circuit for detecting this concentration threshold S (g).

Next, the above B image component is read out from the IC memory 6b, and a binarized image is created in the comparator circuit 10 with the above density value S (g) set as a threshold level. For example, a binary pattern is created in which the blood cell part is "1" and the other parts are ti Oy+, and is stored in the IC memory 6d. This binarized pattern is read out from the IC memory 6d again, each blood cell image is labeled using the labeling circuit 11, and then stored in the IC memory 6d again. The labeled mask image is thus stored in the IC memory 6d.

Once each blood cell image is labeled, the process described below is repeated for each label. Then, until the total number of processed blood cells reaches the number set at the beginning, for example, 10
Change the field of view of the stage of microscope 2 until there are 00 pieces,
Repeat image input. The microscope stage is moved and automatically focused by a stage control circuit 24.

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 feature parameter extraction is performed using the mask image, 0 image, and R image stored in the IC memory 6. That is,
The labeled mask image is read out from the IC memory 6d, and the area S2 and perimeter length of each of the 11 widths are determined by the area calculation circuit 7a and the perimeter calculation circuit 7b. Next, from the above mask image and the 0 image stored in the IC memory 6c.

A density histogram of the whole blood cell image within the screen is created by the histogram creation circuit 8. An example of this density histogram is shown in FIG. First, the maximum value HT of the density histogram is determined from this density histogram, and then a high density value f corresponding to a frequency value of α percent of the maximum value HT is determined by the threshold density rest circuit 12.

Next, the mask image is transferred from the IC memory 6d, and the 0 image is transferred to the IC memory 6d.
Reading from the memory 6c and using the histogram creation circuit 8, a density histogram for each blood cell in the 0 image as shown in FIGS. 4(A) and 4(B) is determined. Fourth
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 Find e. Here, the above value of α is a value determined statistically, and varies depending on the staining conditions of the blood specimen, but is between 20 and 3.
Good results were obtained with a value of 0%. Similarly, R image is 1,
A density histogram is read out from the C 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 determined. A portion with a higher density than the above density value d determined from the 0 image corresponds to the reticulum of reticulocytes in an actual blood cell image. Therefore, by setting this concentration value d to the threshold level, IC
Using the 0 image in the memory 6c, the comparator circuit 14 detects a reticular body, considers the part darker than the density d to be the reticular part, sets this part to 111'', and sets the other parts to "0".
The data is stored in the IC memory 6b. Next, the reticular body image thus obtained is read out again from the IC memory 6b, and the reticular body perimeter LR is determined by the perimeter calculation circuit 7b.

Feature parameters other than the above are obtained by the parameter extraction circuit 15.
Find it with A detailed explanatory diagram of this parameter extraction circuit 15 is shown in FIG. Characteristic parameters are determined using the concentration histogram H(g) of the G image acid content or R image component in each blood cell image unit, the previously determined concentration values d and f, and the maximum concentration value e.

30 is an adder which adds the density histogram H(g) from the density value d to the highest density value e to obtain Sae"ΣH(g). On the other hand, 31 is a subtracter which adds Dea=e
- Find d. 32 and 33 are logarithmic converters, each with Q
ogSde and LogDa. seek. Sde is the fourth
It is the area from the density value d to the highest density value e (slanted part) of the density histogram for each blood cell image shown in Figures (A) and 4(B), and is the area that is comparable to the density of the reticular body in each blood cell image. Corresponds to the area of the high concentration part. Dde is the density difference between the highest density value e and the density value d, that is, the density width of the high density portion.

Similarly, 34 is an adder that adds the density histogram H(g) from the density value f to the maximum density e, and 5ze=ΣH(
g). On the other hand, 35 is a subtracter, Di
Find e=ef. 36 and 37 are logarithmic converters;
Find QogSze and QogDte, respectively.

38 is the square root of the sum of the squares of Sde and D++e, that is, D
, .=JWπi了劉J. This parameter is an important parameter when performing Heilmayr classification of reticulocytes.

Heilmeier's classification described above distinguishes reticulocytes into 5 types based on the density of linear granules: type 4, type 3, type 2, and type 1 from the sparseness of the reticulocytes.

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

Note that in the parameter extraction circuit 15 described above, the parameter S
The purpose of logarithmically transforming ae, Daey, Sxe, and Die is as follows. Here, the parameter fio
This will be explained using gsde and QogDae as examples.

The distribution of the parameters Sde and D++e for red blood cells before conversion is concentrated near the origin, so the center of the distribution is located near the origin and shows a long tail toward the reticulocyte region. In this state, if red blood cells and reticulocytes are distinguished using the magnitude relationship of the parameters Sde and Dde described above, red blood cells located in a boundary area where red blood cells and reticulocytes coexist may be determined to be reticulocytes. Therefore, the above parameter Sa
Parameter Q og S obtained by logarithmically transforming e and Dae
The relationship between ae and QogDae and red blood cells and reticulocytes is shown in FIG. 6. In other words, as shown in FIG. 6, the above parameter QogDae is set on the vertical axis, the above parameter QogSae is set on the horizontal axis, and it is determined in advance whether each blood cell is a red blood cell or a reticulocyte by observation under a microscope by a laboratory technician. The parameters fiogD, +e and Qo are successively determined by the apparatus shown in FIG.
When g S de is determined and the position of each blood cell is plotted on this plane, as shown in the example of FIG. 6, red blood cells and reticulocytes show a clearly separated distribution although there is some overlap.

It can be assumed that this classification does not change for any specimen unless the conditions of the optical system of the apparatus or the conditions of staining the specimen are changed. Therefore, a discriminant function is determined from this distribution, and this is set in the discriminator circuit 16 in Fig. 1. After that, automatic identification becomes possible.

Generally, the parameter Q og S ds indicates that red blood cells and reticulocytes tend to partially overlap. On the other hand, with the parameter QogDae, the overlap between red blood cells and reticulocytes is small. This fact is shown in Figure 6. Therefore, as shown in Fig. 6, the discriminant function is defined as the straight line LR in the figure, and QogDae
=Ko. Only the parameter QogDae is used to discriminate between red blood cells and reticulocytes. QogDd
The upper side of the straight line LR (function according to fi) of e"Ko is the area where reticulocytes exist, and the lower side of the straight line LR is the area where red blood cells exist. Therefore, this straight line RL is set in the identification circuit 16, and the identification circuit It is possible to automatically identify red blood cells or reticulocytes by comparing the straight line RL with the value of the characteristic parameter QozDae of each blood cell image.

The setting of Ko is, for example, by presetting a large number of samples of red blood cells and reticulocytes, taking into account the average value 2 variance of the values of Qog'Dtre, and the a priori probability of red blood cells and reticulocytes.
Furthermore, it is assumed that the distribution of the parameters is a normal distribution, and can be determined statistically. In this way, the parameters Sde and D
When ae is transformed logarithmically, the center of the distribution of red blood cells moves away from the origin and approaches the shape of a normal distribution. Since the probability of red blood cells occurring is 100 times greater than that of reticulocytes, all red blood cells in the boundary area where red blood cells and reticulocytes coexist are identified as red blood cells. Therefore, the accuracy of identifying red blood cells can be 99.9% or higher. For reticulocytes, the identification accuracy is about 90%.

The reason why the characteristic parameters QogSie and QogD□8 are determined by determining the concentration value f at the α percent point from the concentration histogram of the whole blood cell image within one screen is as follows. For types 3 and 4 of the Heilmeier classification mentioned above, the concentration histogram H(g) is shown in Figure 7 (
A). On the other hand, Heilmeier's classification 0-2
As shown in Figure 7, when the concentration of the reticulocyte reticulum becomes high and the area becomes large, the concentration histogram H(g) becomes as shown in Figure 7 (
B).

Therefore, the concentration value of the α percent point obtained from the concentration histogram H(g) in blood cell image units is set at point d in FIGS. 7(A) and 7(B). Therefore, there is a possibility that the characteristics of reticulocytes cannot be extracted using the parameters Q og S de and R, ogDde described above. In other words, when red blood cells and reticulocytes are identified using the above parameters QogSde and QogDde, there is a possibility that some of the red blood cells that are determined to be red blood cells include reticulocytes of Heilmeyer classification O-2. , red blood cells and reticulocytes may not be accurately distinguished. In order to solve this problem, the concentration value f at the α percent point is determined from the concentration histogram of multiple blood cell images, for example, a whole blood cell image within one screen, and the averaged concentration value for one screen is used.
Solve the above problem. In other words, Heilmayr classification 0~
Since type 2 reticulocytes have a dense reticulum, the frequency of the high concentration portion of the concentration histogram is generally higher than that of other red blood cells. Therefore, the fourth line showing the concentration histogram of red blood cells
As can be seen by comparing Figure (A) with Figure 7 (B), which shows the concentration histogram of Heilmeier classification O to type 2 reticulocytes, the concentration area is higher than the concentration value f determined from the histogram of the multiple blood images described above. The histograms of are clearly different and the parameters Q og S xe and IlogDie
By using , it is possible to distinguish between the two.

The above parameters Q og Sie and QOgDle
Figure 8 shows the relationship between red blood cells and reticulocytes of Heilmeyer classification O-2. Here, Figure 8 shows
The above parameter QogSie is set on the horizontal axis, and the above parameter ρogDxe is set on the vertical axis. Parameter QogSze in reticulocytes of Heilmeier classification type 0 to 2
, +2ogDze becomes a very large value, and its distribution is far from that of the corresponding parameter of red blood cells. In this case, reticulocytes and red blood cells of Heilmeyer classification types 0 to 2 can be distinguished using only the single parameter flogsie or QogDte. As shown in Figure 8, QogSi
If we define the straight line LR' corresponding to e=Kx as the discriminant function, then
If the parameter nog Sie of an individual blood cell is smaller than Kz, it is a red blood cell, and if it is larger, it is a reticulocyte. Also, Qo
If the straight line LR' corresponding to gDze=Kz is defined as a discriminant function, if the parameter QogDie of an individual blood cell is smaller than 2, it is a red blood cell, and if it is larger, it is a reticulocyte. For reticulocytes and red blood cells of Heilmeier classification type 0 to 2, the parameter Q
Since the distributions of ogDie and Q og S le are sufficiently separated, even parameters before logarithmic transformation, such as I)ze or Sze, are sufficient to distinguish these two categories.

Similarly, the parameter extraction circuit 15 is used to extract feature parameters for the R image, but since the amount of feature parameters obtained from the R image is almost the same as that for the 0 image, the amount of feature parameters obtained from the 0 image is the same. By introducing the characteristic parameters, red blood cells and reticulocytes can be distinguished.

Next, using the various characteristic parameters described above, the discrimination circuit 16 in FIG. 1 classifies the cells into red blood cells, reticulocytes, and others, and the cells are counted by counters 18.degree. and 19.20, respectively. This operation is repeated until the total number of red blood cells and reticulocytes reaches a predetermined number. Next, those determined to be reticulocytes by the identification circuit 16 are also reclassified by the Heilmeyer classification identification circuit 17, and the results are sent to a predetermined counter 21.
．． Count at 22.23.

25 is a control circuit that controls the entire automatic reticulocyte counting device in this embodiment.

Reticulocyte identification in the identification circuit 16 is performed by multi-stage identification branch logic. First, in the first step, connected red blood cells, large and small dust, etc. are removed using the area S and perimeter L parameters of each blood cell image. Next, in the second step, red blood cells and reticulocytes are identified from the 0 image using a discrimination function using the feature parameter of QogDaa. That is, as shown in FIG. 6, the discriminant function LR is determined and Qog
Blood cells whose Dae is larger than the function LR are determined to be reticulocytes, and blood cells whose Dae is smaller than the above function LR are determined to be red blood cells. Among those determined to be red blood cells in the second stage are those shown in Figure 7 (B).
In the third stage, Qog
Using the feature parameters of Sxe or QogDie,
The above-mentioned reticulocytes are detected and counted as Heilmeier classification O-2 types. On the other hand, for those determined to be reticulocytes in the second stage, the characteristic parameters from the 0 and R images, such as the reticulum area Sae, perimeter LR, and density difference Dde, are used to distinguish between reticulocytes and red blood cells. Distinguish from pseudoreticulocytes, which have wrinkled scars. Although the pseudo reticulocytes appear as reticulocytes, the reticulocytes have a relatively small area, a simple shape, that is, a short circumference, and a large concentration compared to reticulocytes. These features are all expressed in the above feature parameters Sae, LR and I)++e, and the fourth step is to use these parameters to distinguish between reticulocytes and pseudoreticulocytes. Blood cells other than reticulocytes are classified as other and excluded from processing.

Those determined to be reticulocytes in the fourth stage are then subjected to Heilmayr classification by a Heilmayr classification identification circuit 17. That is, the relationship between the parameters Sde and Dde obtained from the 0 image and the reticulocytes according to Heilmeyer classification is as shown in FIG. 9. Here, in FIG. 9, the above-mentioned parameter Sae is set on the horizontal axis, and the above-mentioned parameter Dde is set on the vertical axis.
has been established. As is clear from the figure, reticulocytes H4 of Heilmeyer type 4 are close to the origin, reticulocytes Ha-z of Heilmeyer types O to 2 are far from the origin, and reticulocytes H8 of Heilmeyer type 3 are far from the origin. H4 and Ho
~2. Therefore, the feature parameter DIs-8ae" + D a corresponding to the distance from the origin
e", it becomes possible to perform Heilmeier classification based on the magnitude relationship of this parameter Dos. In other words, for the distance LX and H2 from the origin determined by the value of the parameter Dss above, Dis≦L1. Reticulocytes are Heilmeyer type 4 (H4), and L 1 <
It is determined that reticulocytes for which D is ≦L2 are Heilmeyer type 3 (H8), and reticulocytes for which D s s > H2 are Heilmeyer types 0-2 (Ho-2). This result is sent to counters 21 and 22, respectively.
, 23.

In this example, since the reticulocytes of Heilmeier classification types 0 to 2 are extremely small, totaling 0.1% or less in normal people, subclassification into types 0 to 2 is not performed.

The above identification circuit 16 and Heilmayr classification identification circuit 17
can be easily constructed using known circuit components, but it is also possible to identify it using a combination circuit.

〔Effect of the invention〕

As described above, according to the present invention, the identification rate of red blood cells and reticulocytes is improved by introducing new characteristic parameters in reticulocyte calculation. In particular, the introduction of logarithmically transformed parameters is effective in increasing the identification rate of reticulocytes and reducing the rate of misidentifying parameters as reticulocytes.

Therefore, according to the present invention, it is possible to easily automate, increase precision, and increase speed of reticulocyte counting, and furthermore, it is possible to greatly save the labor required for conventional inspection work under a microscope by a laboratory technician.

The present invention can be easily applied to conventional blood image automatic analyzers, especially white blood cell morphology testing devices and red blood cell morphology testing devices.
A comprehensive automatic blood image analyzer including a reticulocyte counting device can be provided.

[Brief explanation of the drawing]

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), Figure 7 (
B), FIG. 8 is a diagram for explaining the operation of the configuration shown in FIG. 1, and FIG. 5 is a block diagram showing the configuration of an embodiment of the main part shown in FIG. 1. 1...Light source, 2...Microscope, 3...Blood specimen, 4
...Color TV camera, 6...Image memory, 7...
- Histogram creation circuit, 12... Threshold density detection circuit, 13... Threshold density detection circuit, circuit. 15... Diagram without parameter extraction

Claims (1)

[Scope of Claims] 1. In an apparatus for distinguishing between red blood cells and reticulocytes from a density histogram of a red blood cell image extracted from a blood sample image, means for determining the density width of a predetermined high density portion of the histogram of each red blood cell image. and a means for converting the value of the concentration range into a logarithm, and distinguishing between red blood cells and reticulocytes using the logarithmically converted value as a parameter. 2. The means for determining the concentration width is to determine the concentration width of the high concentration portion from the difference between the concentration value of the high concentration portion having a frequency value of a predetermined percentage of the maximum value of the histogram of each blood cell image and the maximum concentration value. An automatic reticulocyte counting device according to claim 1, characterized in that: 3. The means for determining the density range is to calculate the density value of a high density area having a frequency value of a predetermined percentage of the maximum value of the density histogram of a plurality of red blood cell images in one screen, and the maximum density of the density histogram of each red blood cell image. 2. The automatic reticulocyte counting device according to claim 1, wherein the concentration width of the high concentration portion is determined based on the difference. 4. The density value of a high density part having a frequency of a predetermined percentage of the maximum value of the density histogram of multiple red blood cell images in one screen is defined as a slice level, and the frequency of the part of the density histogram of each red blood cell image that is equal to or higher than the slice level The automatic reticulocyte counting device according to claim 1, wherein the automatic reticulocyte counting device calculates the sum of . 5. Detect scratched and wrinkled pseudo reticulocytes from the sum of the above concentration range and frequency, and the circumference of the portion of the red blood cell image with a predetermined concentration or higher, and distinguish the pseudo reticulocytes from red blood cells and reticulocytes. The automatic reticulocyte counting device according to claim 1, wherein reticulocytes are excluded from being identified as red blood cells.