JPS60133897A - Automatic and continuous identification, classification and counting method of fine particles - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention provides a method for automatically collecting microparticles of very fine size, particularly, but not limited to, microorganisms of animal and plant origin. Identification, sorting (sort out), counting, and 1q results (for example, milk or blood)
The present invention relates to new methods for use in a variety of fields, such as biological analysis and automatic control of bacterial contamination, such as automatic pilots in industrial effluent or drinking water treatment plants. This new method is characterized by accuracy, rapidity and low cost.

[Prior Art 1] Various methods aiming at similar results are already known. For example, in the case of human blood analysis, the usual method is to first prepare each sample by hand through various mechanical and chemical treatments, and then examine each sample under a microscope to identify the various cellular elements being investigated. Analyze and count. This method takes about 1 hexagram of time and is troublesome, and it takes about 20 to 30 hours.
There is a misunderstanding of %.

Similarly, in order to analyze and count bacteria in liquid, A
The pelleter first collects a sample of the liquid and then inoculates each in a culture medium specifically designed for bacterial detection (this process is time-consuming and can result in time delays of several hours).
, and finally analyze and count the m++ bacteria in comparison with known forms and shapes. This also has the same limitations and inconveniences as above.

U Problems to be Solved by the Present Invention In general, automation of the above type has hitherto been able to rapidly and continuously produce the desired results, while avoiding human error. And there is no iterative method.

The present invention aims to provide such an automatic and iterative method.

[Means for solving the problem j] To achieve this object, according to the invention, a series of operations whose principles are individually known for other applications are combined.

The first operation consists of preparing a series of samples, placing them automatically and sequentially on successive platforms and sorting them on the track of a video system that records each lean pull in succession.

The second operation consists of recording the image given by the microscope through a particular optical element by means of a video camera, as described above.

The third operation is to decompose the image of each sample by a matrix layout that includes a number of rows and columns, and to analyze each sample by conventional techniques, so that a predetermined but clearly identified area is determined for each sample. , that is, defining a "pixel".

The fourth operation consists of performing the above-mentioned image analysis by computer according to the illuminance level of each pixel, and the analysis results of each sample are systematically processed and then digitized on a printer so that they can be used at any time. Not only can you see the analysis results, but you can also follow the progress of the results over time.

Processing these various operations requires a large number of observations.

Observation in the first operation Each non-pule undergoes physical and chemical treatment before being placed on the table. This preparation is similar to the normal processing of objects, but differs in several respects.

For example, in the prior art, the "coloring" process is only intended to reveal the full extent of cells, such as bacteria, regardless of their growth status or whether they are alive or dead.

However, in the present invention, the coloring is intended to indicate the growth state of a given cell and to visualize them without coloring artifacts.

Similarly, after coloring, the shrimp is treated with fluorescent light (usually ultraviolet (UV) light from above) and the re-emitted light is filtered to classify all cells, including the nucleus, and immunofluorescently treat them. By doing so, it is possible to specifically extract the information.

After R, each sample was pulverized.
Automatically arrange the shape of a rated drop of a constant thickness and a constant diameter on a continuous table by blowing or blowing.
l-le. iX h 6 (0 drops spread regularly and homogeneously on the table and are continuously observed by a camera. For example,
The platform may be a horizontal glass disk rotating about an axis, so that the drops are arranged in concentric circles and probed along these successive circles, or probed radially in circles. It has become. Alternatively, the platform may consist of a continuous tape that is unwound from a supply coil and wound around a receiving coil, and the entire unit may be housed in a disposable cassette 1. In this latter case, there is no need to clean the stand as is the case with discs. Generally, the number of drops is expressed in 1 mm3, and the statistical results are selected as sample values for the next process. After each 81st harvest, the platform undergoes an automatically controlled cleaning process.

Observation of the second operation The use of a microscope with special optical elements and the use of a video camera are combined, and the images taken with the video camera are sent to a minicomputer in view of the third operation. Since a microscope transmits information continuously, it is necessary to keep it in focus at all times. For this purpose, a micrometer screw is placed between the optical element and the samble table to provide constant mechanical focusing. The microscope is preferably illuminated with direct ultraviolet light. This is because observations are photographed by a camera.
This is because cameras can receive more light than the human eye.

The images of the four numbers taken by the viewing camera in the third operation are sent to a computer and analyzed by complex but well known techniques. It is well known that there is no need to elaborate further on this technique, but it is worth noting that when the image is sent to computer storage,
ition) ), it is first necessary to determine the minimum value of the illuminance of the pixel corresponding to the target microscopic child ().
, once the threshold for the sample is established, the contour of each microscopic particle can be known. However, it is through this technique that the present invention fundamentally differs from conventional manual techniques, which were limited to visual comparisons with a series of predetermined shapes. This difference does not result in a 3a difference in accuracy and working speed. Traditional simple visual comparisons are slow, inaccurate, and error-prone.
It had some flaws.

The next step in this operation is the classification of the microscopic particles thus defined according to their size and shape. Since such classification is done for great performances and the results of great performances are synoptically processed, the information received at any given time or through chronological development is extremely accurate. .

Of course, general techniques for image or shape analysis (1-Pattern Recognition J
(pattern recognition)
is quite well known, and any other sub-operations known in the art can be used in this technique. For example, pre-processing the elephant to take into account the removal of [background noise] in order to obtain a clearer image;
etonizing), and all of these techniques can be used depending on each case.

[Examples] The present invention will be explained in more detail by giving two examples of the method of the present invention.

Example 1 In this example, the method of the invention is used to examine bacteria in milk.

In this example, the first operation consists of coloring the sample with orange acridine at pH 6.

Colorless fat droplets can be observed in the drops.
bules), living white blood cells that are similar in shape to fat droplets or differ from them in being colored;
Rod-shaped bacteria and circular shells that appear and can be revived with or without ribonucleic acid (RNA) and deoxyribonucleic acid (DNA) during the "staining" process. In this way we can know the total number of microorganisms and the number of white blood cells, which are
Identified by shrimp fluorescence by illuminating from above at V<400μ, it can be filtered (after spectral re-emission as small as 600 nanoμ).

Next, butyrate []stridium (clost) in bacteria
ridium butyricum ) and fluorescent bacteria (p.
seudomonas fluorescens).

Both can be extracted by IC immunofluorescence using serum corresponding to the bacteria to be investigated and a specific coloring.

Next, perform the following operations on the sample at a constant low temperature.

(a) Add 1% cyanide and 10% orange acridine to the sample and add droplets of reaction product to step 2 and step 3.
Both microorganisms can be obtained by applying

By repeating the procedure (ω without coloring), only fluorescent bacteria can be obtained.

(C) Only Clostridium butyrate can be obtained by repeating operation (a) by adding both orange acridine and the specific colored surface Φ.

Regardless of which of the above operations (a), mount, or (c) is selected, the image obtained by the microscope can be captured by a camera, and the image is captured via the computer's storage device, with the horizontal row x vertical row being 512 x 512. It is sent in the form of a rectangular notch representing 262.144 points or pixels. The depth of each point, ie, the amount of light received, is expressed by a number, ie, a "bit." In this case, 6 bits are used and they represent 64 levels, from zero for white to 63 for black. The image of the illuminated object appears clearly on the black background (parasites also appear clearly).

When starting an analysis operation, a threshold value of illuminance is determined, and the areas above this point are counted as target elements.

Once this "sizing" is established, one can proceed to the "contouring" process, i.e. determining the outline of each object, and then itemizing each object on a chart according to the coordinates of each outline. "maximum length" and "shape element j
It is possible to establish a predetermined number of parameters such as ``long'' to ``hard''.
Objects corresponding to the selected parameters are then selected on the chart, and the results are finally statistically processed in terms of performance and residual droplets. In this case, the analysis of the -drop takes 1.5 seconds and the recognition error rate is 5%.

These numbers must be considered in the context of other data such as the duration of each stage and currently accepted D error limits. This is because they result from complete automation of operations.
[Because it is a thing.

Example 2 Blood Analysis In the case of blood analysis, the method of the invention not only provides rapidity and accuracy in the evaluation of the cells themselves, namely red and white blood cells, but also automatically assesses the parasites contained within them. can be detected and counted.

Since parasites are extremely small in size, this would have been impossible to achieve in the past. However, according to the present invention, it has become possible to detect and count, for example, malaria parasites of about 2 microns using OJ.

More specifically, blood analysis according to the method of the present invention is performed as follows.

First, the sample is placed on the table continuously and automatically by crushing or spraying. This is a significant improvement over the traditional method of crushing the blood sample between the table and the glass plate. It is.

Conventional methods destroy or deform cells.

1) By coloring l-16 with orange acridine, uncolored leukocytes without nuclei and containing neither RNA nor DNA, as well as living and variously colored nuclei and various inclusion bodies, contain DNA and inclusion bodies. Regarding the body, white blood cells containing RNA) are distinguished.

On the other hand, if blood parasites are present within red blood cells, such parasites will be revealed by staining since they contain DNA and RNA.

The image is analyzed using the general techniques described above, taking into account the complexity of the "object".

Indeed, if the well-known colored leukocytes can be easily identified and counted by their contours, one can find the inner contours and further components within this contour that form the nucleus or nuclei. It is also possible to do so. Depending on the number of objects detected in this way, different types of multinuclear components can then be identified and numbered.

The same is true for red blood cell parasites, and this is one of the main innovations of the method of the invention. for example,
The malaria parasite is a protrusion, i.e. a cat.
kin) (DNA) with a ring (RNA)
). Therefore, they have very small dimensions (
2 microns), it can be counted automatically and continuously from the beginning along the analysis flow.

Of course, the scope of application of the present invention is actually limitless, as stated at the beginning of this specification, and is not limited to simply counting microscopic particles in a liquid. For example, the present invention can be applied to a variety of fields, such as classification and counting of surface defects in solid materials.

[Effects of the Invention] As is clear from the above, the method of automatically and continuously identifying, classifying, and counting microscopic particles of the present invention can accurately,
It exhibits various excellent effects, such as being able to work quickly, at low cost, and with minimal human error.

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Claims (1)

[Claims] 1) In a first operation, a series of samples are prepared and placed on a successive moving stage, and these samples are placed on a probe path of a video system that records the images of these four non-pulses provided by the microscope. , then in a second operation perform said recording, then in a third operation resolve the image of each sample by means of a matrix layout, and in a fourth operation said image
Automatically and continuously identify microscopic particles characterized by analyzing them using "pattern recognition" technology, finally statistically processing the analysis results, digitizing them, and sending the concrete data to the operator. , how to classify and count. 2) A method for automatically and continuously identifying, classifying, and awarding microscopic particles according to claim 1, wherein the preparation in the first operation is specific coloring after observation with shrimp fluorescence. . 3) A method for automatically and continuously identifying, classifying and counting microscopic particles according to claim 1), which is carried out by repeatedly placing the sample on a table, crushing or spraying. . 4) The microscopic device according to any one of claims 1) to 3), wherein the continuous platform is a glass disk that rotates around a vertical axis and performs continuous cleaning operations. A method for automatically and continuously identifying, classifying, and numbering groups. 5) The continuous base is a continuous tape that is unwound from one coil, used once, and then wound onto the other coil, and the tape is housed in a disposable cassette. A method for automatically and continuously identifying, classifying, and counting microscopic particles according to any one of claims 1) to 3). 6) The microscope is equipped with a special accessory for automatically focusing on the sample at all times, so that the microscopic particles according to any one of claims 1) to 5) are automatically and continuously How to identify, classify and reduce numbers. 7) Claim 1, wherein the microscope is illuminated with ultraviolet light
A method for automatically and continuously identifying, classifying, and numbering the microscopic particles described in any one of items 2) to 5). 8) The microorganism according to any one of claims 1) to 7), wherein the coloring agent used in the first operation is orange acridine and/or specific colored serum. and continuous identification, classification and counting methods. 9) Automatically and continuously identify, classify, and count microscopic particles according to any one of claims 1) to 8) used for bacterial investigation in milk. Method. 10) Automatically and continuously identifying and classifying microscopic particles according to any one of claims 1) to 8), which are used for blood analysis, particularly for investigating malaria A7 protozoa, etc. in blood. and 4 counting methods.