JPH08210970A - Particle coagulating pattern judging device - Google Patents

Abstract

PURPOSE: To suppress the influence of a disturbance and improve the judging precision by determining a plurality of characteristic quantities including a peak height from a two-dimensional image data including the center position of a coagulating pattern, and measuring the coagulating pattern of particle on the basis of this. CONSTITUTION: The reverse image data of a well 2a area corresponding to concentration is determined from the image data of a microplate 2, the center position (i, j) of a particle coagulated body formed in the well 2a is determined, one or more of straight lines passing it are selected, the concentration profile in each straight line is determined on the basis of the reverse image data. A characteristic quantity (height H, area A, dispersion V, half-value width W) is determined every concentration profile, and when the range (maximum value- minimum value) exceeds a set value, a data abnormality is judged to eliminate it from class judgment. When the range is within the set value, the average value of the characteristic quantity is determined, and of which class it is conformed to the coagulating pattern is judged to classify it.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a particle agglomeration pattern determination device that takes an image of a particle agglomerate formed in a container and determines the agglomeration pattern of the particle agglomerate based on the image data.

[0002]

2. Description of the Related Art Conventionally, a particle aggregation pattern determination method has been widely used for determination of blood type and detection of antigens and antibodies in blood. According to this method, the blood to be tested is placed in the well (reaction container) of the microplate, the blood and the like are aggregated based on the immunological reaction, and the blood type is determined from the aggregation pattern. By the way, since these judgments were made by visual inspection by inspectors,
There were individual differences among the inspectors, and there was a problem that the same inspector lacked reproducibility. Therefore, the above visual judgment is performed by a device, the judgment result is made objective, the measurement accuracy is improved, and the labor of the judgment is reduced by automation. Those techniques are disclosed in, for example, Japanese Patent Laid-Open Nos. 60-135748, 61-215948, 62-105031, and 63-58237. According to these techniques, a well (including aggregates) of a microplate is imaged by a television camera, predetermined image processing is performed on the image data, and the aggregation pattern is determined from the processing result.

[0003]

However, the automatic determination technique according to the above proposal has the following problems.

Since the aggregate is not always formed at the center of the well of the microplate, it is necessary to determine the center of the aggregate for each well. In particular, when the bottom surface of the well has a rounded shape as shown in FIG. 2, aggregates are often formed around a position displaced from the center of the container. Further, as one of the automatic determination techniques, for example, there is one that binarizes the image data, obtains the area of the aggregate from the binarized data, and determines the aggregation pattern. It is easy to cause misjudgment. For example, when the agglutination reaction is positive, the aggregate spreads thinly in the well of the microplate, the difference between the brightness of the aggregate and the brightness of the periphery (that is, the well) is small, and the binarization level slightly increases or decreases. Only by doing so, the area value of the aggregate obtained as described above greatly changes. Further, the binarization process is closely related to the illumination light amount, and when the illumination light amount changes, the area value of the aggregate changes as in the above. In addition to the above-described determination method, there is also a method of determining the contour of the aggregate based on the degree of change in grayscale of the image, and performing pattern determination from the contour.
This method is easily affected by disturbance, and it is difficult to accurately extract the contour.

As described above, the automatic determination technique (method) according to the above-mentioned proposal has the above-mentioned problems, it is difficult to make an accurate determination, and the blood type is as reliable as a skilled inspector. Can not be judged.

The present invention has been made to solve the above problems, and an object thereof is to provide a highly reliable particle agglomeration pattern determination apparatus.

[0007]

According to a first aspect of the invention, a two-dimensional image of a container of a liquid to be inspected, an illuminating means for illuminating the container, and a particle aggregate formed in the container is taken. 2 of the image pickup means and the particle aggregate obtained by the image pickup means
At least based on the center position calculating means for obtaining the center position of the aggregation pattern of the particle aggregates based on the two-dimensional image data, and the density information of the two-dimensional image data including the center position obtained by the center position calculating means. Feature amount calculating means for obtaining a plurality of feature amounts of the two-dimensional image data including a peak height, and aggregation for determining an aggregation pattern of the particle aggregate based on the plurality of feature amounts obtained by the feature amount calculating means A pattern determination means is provided.

According to the invention of claim 2, the container has a rounded bottom.

According to a third aspect of the present invention, the characteristic amount calculating means selects at least one straight line including the center position on the image surface of the particle aggregate, and obtains a density profile on each straight line. For each concentration profile,
A feature amount of a plurality of density profiles including at least the peak height is obtained.

Further, according to the invention of claim 4, the feature quantity calculating means uses, as the feature quantity, the area, the variance, the average width, and the half width of the density profile for each density profile, in addition to the peak height. Any one or more is further requested.

Further, in the invention of claim 5, the aggregation pattern determination means statistically processes the plurality of characteristic amounts to determine the aggregation pattern of the particle aggregate.

According to a sixth aspect of the present invention, the center position calculating means obtains concentration profiles on predetermined two straight lines intersecting each other on the image of the particle aggregate, and obtains the barycentric position of each concentration profile, The central position of the particle aggregate is determined based on those positions.

[0013]

According to the present invention, the center position of the aggregation pattern of the particle aggregate is obtained based on the two-dimensional image data of the particle aggregate, and the density of the two-dimensional image data including the obtained center position is obtained. A plurality of feature quantities including at least the peak height are obtained based on the information, and the agglomeration pattern of the particles is determined based on this, so the concentration information of the particle agglomerates is sufficiently reflected in the feature quantity, and the influence of disturbance Can be suppressed. Also, the setting of "binarization level" becomes unnecessary. Further, even when the center of aggregation of particles does not coincide with the center of the container, the peak height of the concentration can be accurately obtained, so that the accuracy of determination can be improved.

FIG. 2 is a block diagram of an aggregation pattern determination device 1 to which an embodiment of the present invention can be applied. This device 1
It has a microplate base 3 for mounting and positioning the microplate 2. The microplate 2 has wells 2a, which are arranged in a matrix state and have a rounded bottom, on the upper surface thereof, and are mounted on the microplate base 3 by a transport mechanism (not shown). In addition, a lighting device 5 is provided below the microplate base 3.
Is provided, and the light emitted from the illumination device 5 is transmitted through the microplate base 3 and the microplate 2 and projected onto the television camera 4 provided above the microplate base 3. Then, the entire microplate 2 is imaged by the television camera 4 and input to the A / D converter 6 as a video signal V, converted into a digital signal Z of 8 bits, that is, 256 gradations according to the luminance, and then, as shown in FIG. As shown in, the image data is recorded in the image memory 7 as an image data ID (X, Y) of 480 pixels vertically and 512 pixels horizontally. Also,
The image memory 7 is connected to the CPU 8 and the CPU
The image data ID (X, Y) is read out in 8 and various calculations (which will be described in detail later) are performed, and the aggregation pattern is determined based on the calculation result. The determination result is output to the printer 9, a display (not shown), or the like. Further, the image data recorded in the image memory 7 is converted into a video signal V ′ by the D / A converter 10 and given to the monitor television 11 so that the image of the microplate 2 is displayed on the monitor television 11. Is configured.

Next, an embodiment of the present invention will be described with reference to FIG.

(1) Imaging of Sample In the present embodiment, first, the microplate 2 is mounted on the microplate base 3 by the transport mechanism. Then, the image of the microplate 2 is captured by the TV camera 4 while the illumination device 5 is turned on, and the image data ID
Record (X, Y) in the image memory 7.

Thereafter, the agglutination pattern of each well is sequentially processed according to the following procedure based on the image data.

(2) Calculation of center position Inverted image data (data corresponding to the density) D (X, Y) of the well region is obtained according to the following equation and recorded in another region of the image memory 7.

[0019]

[Equation 1] Here, the well region is a circular, absent, or square region including the inside of the well centering on the expected well center position. Further, ID _MAX is the maximum value of the image data ID (X, Y) and corresponds to the image data of the background of the aggregate (that is, the periphery of the aggregate).

Then, based on the inverted image data D (X, Y), the central position (i,
j) is calculated (step S1, first step). The following is one example of the method. As shown in FIG. 4, the expected center position of the aggregate (x ₀ , y ₀ )
The density profiles Z ′ _x and Z ′ _y on straight lines L ₁ and L ₂ that pass through the above and are parallel to the horizontal axis (X axis) and the vertical axis (Y axis) are determined. Since the microplate 2 is positioned on the microplate base 3 and the formation position of the aggregate in the well can be predicted to some extent, the positions x ₀ and y ₀ can be set in advance.

[0021] Furthermore, they of the concentration profile Z _'x,
The barycentric position (x _g , y _g ) in the horizontal axis and vertical axis directions is obtained from Z ′ _y . Specifically, the positions x _g and y _g are respectively _calculated according to the following equations.

[0022]

[Equation 2]

(Equation 3) Here, in order to avoid the influence of the outer edge of the well, the value (2
value (n) so that n) is sufficiently smaller than the diameter of the well
Set. Further, the above operation may be repeated to obtain the center position (x _g , y _g ) more accurately. After that, after calculating the positions x _g and y _g , the X coordinate x _g and the Y coordinate y _g of the center of gravity may be rounded to an integer, and the values may be set as the central position (i, j) of the aggregate.

[0023] Note that in this embodiment, to determine the position x _g based on the density profile Z _'x on the straight line L _1, in the vicinity of the position (x _0, y _0), and the horizontal axis (X axis) obtain the position x _g for each straight line based on each concentration profile on a plurality of parallel straight lines, it may be calculated center position x _g by statistically processing the values (for example, obtaining an average value processing) , Thereby further improving the accuracy of the center position x _g . The same applies to the center position y _g .

(3) Derivation of Density Profile Next, at least one straight line passing through the center position (i, j) obtained in step S1 on the aggregate image surface is selected and recorded in the image memory 7. The density profile on each straight line is obtained based on the reversed image data D (X, Y) (FIG. 5) (step S2, second step). In this embodiment, as shown in FIG. 6, the angles passing through the center position (i, j) and the horizontal axis (X axis) are 0 ° and 4 respectively.
4 straight lines L ₀ , L ₄₅ , which are 5 °, 90 °, 135 °,
Select L ₉₀ and L _135, and select each straight line L ₀ , L ₄₅ , L ₉₀ and L
We are looking for a concentration profile at ₁₃₅ . Specifically, the reverse image data D (i + k, j) in the image memory 7 (however,
k = ..., -2, -1,0,1,2, ...) to obtain the density profile on the straight line L ₀ . FIG. 7 is a diagram showing the result. Further, when the inverted straight line data D (i + k, jk) in the image memory 7 is accessed, the density profile on the straight line L ₄₅ can be obtained. FIG. 8 is a diagram showing the result. Further, the concentration profiles on the straight lines L ₉₀ and L ₁₃₅ can be obtained in the same manner as above. 7 and 8, the horizontal pitches of the two data are equal, but actually the horizontal pitch of the data in FIG. 8 is the root 2 (square root of 2) times in FIG. 8, and from these density profiles, It is necessary to perform a predetermined correction when obtaining a feature amount described later. Note that this will be described later in detail.

(4) Calculation of feature quantity Next, for each density profile obtained in step S2, the feature quantity of the density profile is obtained (step S3, third step). In this embodiment, the peak height, area, variance, or average width is calculated for each concentration profile as follows as a feature amount.

(4-1) Peak Height H The peak height H ₀ is calculated based on the concentration profile shown in FIG. Further, H ₄₅ is obtained based on the concentration profile shown in FIG. 8, and the peak heights H ₉₀ and H ₁₃₅ are obtained in the same manner.

(4-2) Area S Areas S ₀ , S ₄₅ , S ₉₀ and S ₁₃₅ are calculated according to the following equation.

[0028]

[Equation 4]

(Equation 5)

(Equation 6)

(Equation 7) However, similarly to the above, in order to avoid the influence of the outer edge of the well 2a, the value (2m) is set to be sufficiently smaller than the diameter of the well 2a. Note that the coefficients in equations (5) and (7) (=
Route 2) is a correction coefficient for correcting the lateral pitch.

(4-3) Dispersion V Dispersions V ₀ , V ₄₅ , V ₉₀ and V ₁₃₅ are calculated according to the following equation.

[0030]

(Equation 8)

[Equation 9]

[Equation 10]

[Equation 11] The coefficient (= 2) in equations (9) and (11) is a correction coefficient for correcting the lateral pitch.

(4-4) Average width W Average widths W ₀ , W ₄₅ , W ₉₀ and W ₁₃₅ are calculated according to the following equation.

[0032]

(Equation 12)

(Equation 13)

[Equation 14]

(Equation 15) In addition to these, there are full width at half maximum, (third moment, fourth moment) and the like. However, as a result of actual verification, it was found that the above four feature amounts are sufficient from the viewpoint of processing speed and accuracy.

Then, the range (= maximum value-minimum value) of each feature amount obtained as described above is obtained, and it is determined whether or not the range exceeds a preset value. If it is determined that the value exceeds the set value, it is determined as "data abnormality", and the class determination described below is not performed for that sample. A case in which the “data abnormality” is determined in this way is, for example, a case where the microplate 2 vibrates during the agglutination reaction and the agglomerate has an asymmetric shape. On the other hand, when the range of each feature amount is within the set value,
The average value H ⁻ , S ⁻ , V ⁻ , of each feature amount according to the following equation
W ^- the seek.

[0034]

[Equation 16]

[Equation 17]

(Equation 18)

[Formula 19] (5) Class determination Next, the values H ⁻ , S ⁻ , and
Based on V ⁻ and W ⁻ , the aggregates are classified into positive and negative aggregation reactions, that is, “−”, “±”, “+”, “+”.
It is determined which of the five classes "+" and "empty" matches the aggregation pattern (step S4, fourth
Process), the agglutination reaction in the well 2a of the microplate 2 is classified into five classes of "-", "±", "+", "++", and "empty".

As a method of the above determination, for example, SRI (=
Stanford Reseach Institute) algorithm, method using discriminant function, method based on Mahalanobis distance calculation, etc. It should be noted that all of these methods are conventionally known. In this embodiment, the SRI algorithm is adopted to make the class determination. The procedure will be briefly described below.

In order to perform the class determination by the SRI algorithm, first, a large number of samples, which have been subjected to the class determination by visual inspection in advance, are prepared prior to the actual inspection, and the aggregation pattern determination device 1 shown in FIG. 2 is used. It is necessary to obtain the feature amounts H _p , S _p , V _p , and W _p (P = 1,2,3, ...) Of those samples by performing the above operation (steps S1 to S3).

Then, the average value and the standard deviation of each feature amount in each class are obtained from the feature amounts H _p , S _p , V _p , and W _p . That is, the average values H _C1 , S _C1 , V _C1 , W _{C1 of} the feature amounts (peak height, area, variance and average width) and the standard deviation σ are based on the feature amounts of the samples corresponding to the class “−”.
_{Find H1} , σ _S1 , σ _V1 , and σ _W1 respectively. Similarly, for other classes “±”, “+”, “++”, and “sky”, the average value and standard deviation of the feature quantities (peak height, area, variance, and average width) in each class are calculated. Ask.

Next, when class determination of an unknown sample, the value ^H obtained in step S3 -, ^S -, V
^-, W ^- and by substituting the following equation, a distance D _C1 from each class,
D _C2 , D _C3 , D _C4 and D _C5 are obtained.

[0039]

(Equation 20)

[Equation 21]

[Equation 22]

(Equation 23)

[Equation 24] In the formulas (21), (22), (23) and (24), H _{C2 to} H _C5 ,
S _{C2 to} S _C5 , V _{C2 to} V _C5 , and W _{C2 to} W _C5 are each class “±”, “+”, “++”, and “empty” feature amount (peak height, area, variance). And average width), and σ _H2 ~ σ _H5 , σ _S2 ~ σ _S5 , σ _V2 ~ σ _V5 , σ _W2 ~ σ _w5
Is the standard deviation of each feature value (peak height, area, variance, and average width) of each class “±”, “+”, “++”, and “empty”.

Then, these distances D _C1 , D _C2 , D _C3 , D
_The class corresponding to the smallest value of _C4 and D _C5 is determined as the class to be inspected.

As described above, the SRI is used in this embodiment.
Although the class determination is performed using the algorithm, it is needless to say that the method is not limited to this, and the method using the above-described discriminant function, the method based on the Mahalanobis distance calculation, or the like may be used. In particular, since the SRI algorithm is established on the assumption that there is no correlation between the feature amounts, the determination accuracy is reduced when the feature amounts have a correlation. On the other hand, the method using the discriminant function and the method based on the Mahalanobis distance calculation can accurately perform the class determination regardless of the correlation between the feature amounts.

Further, in the above embodiment, in step S2 (second step), the four straight lines L ₀ , L ₄₅ , L ₉₀ , L
_{Although 135} is selected and the concentration profile on each of the straight lines L ₀ , L ₄₅ , L ₉₀ , and L ₁₃₅ is obtained, the number of straight lines selected is 4
The number of lines is not limited to one, and only one straight line (for example, straight line L ₀ ) is selected and its concentration profile is obtained.
The pattern determination may be performed as described above. However, in terms of increasing the accuracy of the feature amount, at least 2
It is desirable to select more than one book.

As described above, the aggregation pattern can be determined without binarizing the image data and extracting the contour of the aggregate formed in the well 2a from the image data. Therefore, in this embodiment, it is not necessary to set a "threshold value" for binarization and contour extraction, and the operation is easy. Further, for the same reason as described above, the influence of the change in the light amount of the illumination device 5 is small. Further, in the above-mentioned embodiment, since the contour extraction processing is not performed, there is almost no influence of disturbance. As a result, according to this embodiment, highly reliable aggregation pattern determination can be performed.

Moreover, since the class determination is performed from a plurality of feature quantities using a statistical method, the reliability of the class determination is further enhanced.

Further, the entire microplate 2 is imaged by the television camera 4, and the center position of the aggregate formed in each well 2a is obtained as described above based on the image data (step S1). Even if the aggregate is deviated from the center position of each well 2a, it is not necessary to adjust the position of the microplate 2 to obtain the center position, and the center position of the aggregate can be obtained as it is. .

[0046]

As described above, according to the invention of claim 1, the center position of the aggregation pattern of the particle aggregate is obtained based on the two-dimensional image data of the particle aggregate, and the obtained center position is included. Since a plurality of feature quantities including at least the peak height are obtained based on the density information of the two-dimensional image data, and the agglomeration pattern of the particles is determined based on the feature quantities, the density information of the particle aggregates is sufficient for the feature quantity. It is reflected and the influence of disturbance is suppressed. Further, it is not necessary to set the binarization level, which improves the operability. Moreover, since the center position of the aggregation pattern is obtained based on the two-dimensional image data of the aggregation pattern, the peak height can be accurately obtained even when the center of the aggregation pattern does not coincide with the center of the container, and the reliability of the determination of the aggregation pattern is high. You can improve your sex.

According to the second aspect of the present invention, since the container having a rounded bottom is used, the agglomeration pattern changes relatively greatly in a region where the class determination is delicate, and therefore the reliability of the agglomeration pattern determination is high. The property can be further improved. When the bottom is rounded, the center of the agglomeration pattern tends not to coincide with the center of the container, but the center position of the agglomeration pattern is obtained based on the two-dimensional image data of the agglomeration pattern. Nevertheless, it is possible to make a highly reliable determination.

[Brief description of drawings]

FIG. 1 is a flow chart showing an embodiment of a particle aggregation pattern determination device according to the present invention.

FIG. 2 is a configuration diagram of an aggregation pattern determination device to which an embodiment of the present invention can be applied.

FIG. 3 is a diagram showing image data recorded in an image memory.

FIG. 4 is an explanatory diagram for explaining a method of calculating a center position of an aggregate.

FIG. 5 is a diagram showing image data recorded in an image memory.

FIG. 6 is an explanatory diagram for explaining a method of deriving a density profile.

FIG. 7 is a diagram showing a concentration profile.

FIG. 8 is a diagram showing a concentration profile.

[Explanation of symbols]

 2 ... Microplate 2a ... Well S1 to S4 ... Step

Claims (6)

[Claims] 1. A container for a liquid to be inspected, an illuminating means for illuminating the container, an imaging means for imaging a two-dimensional image of a particle aggregate formed in the container, and particles obtained by the imaging means. Center position calculating means for obtaining the center position of the aggregation pattern of the particle aggregate based on the two-dimensional image data of the aggregate, and density information of the two-dimensional image data including the center position obtained by the center position calculating means. Characteristic amount calculating means for obtaining a plurality of characteristic amounts of the two-dimensional image data including at least a peak height based on the above, and an aggregation pattern of the particle aggregate based on the plurality of characteristic amounts obtained by the characteristic amount calculating means. A particle aggregation pattern determination device comprising an aggregation pattern determination means for determining. 2. The particle aggregation pattern determination device according to claim 1, wherein the container has a rounded bottom. 3. The feature amount calculating means selects at least one straight line including the center position on a two-dimensional image plane of the particle aggregate, obtains a density profile in each straight line, and calculates the density profile for each straight line. The particle aggregation pattern determination device according to claim 1 or 2, wherein the characteristic amount of a plurality of concentration profiles including at least the peak height is obtained. 4. The feature quantity calculating means further includes, as the feature quantity, one or more of an area, a variance, an average width, and a half width of the density profile for each density profile, in addition to the peak height. The particle aggregation pattern determination device according to claim 3, which is to be obtained. 5. The particle aggregation pattern determination device according to claim 4, wherein the aggregation pattern determination means statistically processes the plurality of characteristic amounts to determine an aggregation pattern of the particle aggregate. 6. The center position calculating means obtains density profiles on two predetermined straight lines intersecting each other on the image of the particle aggregate, finds the barycentric position of each density profile, and based on those positions, The particle aggregation pattern determination device according to any one of claims 1 to 4, wherein the central position of the particle aggregate is obtained.