Patent Description:
In cell culture and bacterial culture in drug susceptibility testing and the like, a technique for measuring a state of cells, bacteria, and the like is required. As an example, a technique is known in which a culture state is detected from below a culture container by a camera or the like, and the number of cells and the number of bacteria are calculated based on a feature amount of the culture state.

As a method of measuring an amount of cells, the number of cells, and the like, there is a method in which transmission light of culture solution in the culture container or a transmission image of a culture surface is detected, and a concentration of a particulate article is calculated based on a change in intensity of the transmission light or transmission image. This is a method used in so-called turbidity measurement. As shown in <FIG>, when the concentration of the particles increases, the intensity of the transmission light detected by a photodetector such as a photosensor or a camera decreases due to light scattering or/and diffraction or/and light absorption caused by the particles, so that the method is a method of calculating the number of particles based on the change in the intensity of the transmission light. An example of such a method is described in PTL <NUM>.

Such a method has advantages that when the particles are present at a high concentration, the intensity is reduced at many locations due to light scattering and absorption caused by the particles, a sufficient change in intensity occurs as a whole, which facilitates the calculation of the number and concentration of the particles.

As another method, there is a method for identifying individual particles based on a feature amount of the particulate article and counting the particles individually. For example, there is a particle counting method in which the particles are detected by a camera or the like capable of sufficiently and finely measuring sizes of the particles, the particles are identified based on a contrast of a transmission image, and the particles are counted individually. An example of such a method is described in PTL <NUM>.

The particle counting method has an advantage that, since the particles are identified and counted in units of one particle, even when the concentration is low or the number of particles is small, a change in the number of particles can be detected with high sensitivity and the particles can be detected with high sensitivity and high accuracy. Thus, for example, a slight change in the number of particles can be detected with high accuracy, and a sign of proliferation of cells during culture can be detected at an earlier stage.

An apparatus for estimating the quantity of particles collected by a suction extractor is described in <CIT>, where a threshold is used in order to segment image data into relevant particle-associated pixels and irrelevant pixels associated with a capturing surface. A further method for estimating the number of blood cells, which is related to the present invention, is disclosed in <CIT>.

However, in the related art, there is a problem that the number of particles which can be accurately recognized has a narrow range.

For example, in the method using the change in the intensity of the transmission light as in PTL <NUM>, when the number of particles is small, an amount of light scattering and absorption caused by the particles is small with respect to the whole, and the change in transmission light intensity is small. Therefore, it is difficult to accurately calculate the number of particles and the concentration.

Meanwhile, for example in the particle counting method as in PTL <NUM>, when the number of particles increases, contact between the particles and overlap between the particles stochastically increases, which makes it difficult to individually identify the particles. In normal, a distribution of the particles is represented by a Poisson distribution, and a simple count loss model is known (<FIG>). That is, when an amount of particles present in a certain area increases, the efficiency of individually counting the particles decreases. Further, when the number of particles increases, more particles overlap in a light-transmission direction. As a result, the intensity of the transmission light decreases as a whole by multiply scattering the transmission light or the like, and the obtained image becomes unclear, so that the particle identification ability in image processing is reduced, and count loss further occurs (<FIG>). For such a reason, in a case where the particles are counted, when the amount of particles present in a certain area increases, a particle count value apparently decreases (for example, <FIG>), and it is difficult to accurately calculate the number of particles and the particle concentration. In particular, even though the number of particles increases, it is easy to cause erroneous recognition that the number of particles decreases. There is such a problem that is unavoidable in principle when particles are individually identified and counted.

The invention is made in view of such a situation, and an object of the invention is to provide a particle quantitative measurement device according to which the number of particles that can be accurately recognized in a particulate sample has a wider range.

The particle quantitative measurement device according to the invention is defined in Claim <NUM>. Further advantageous features are set out in the dependent claims.

The present description includes disclosure contents of <CIT>, which is the basis for the priority of the present application.

According to the particle quantitative measurement device of the invention, the number of particles that can be accurately recognized in the particulate sample has a wider range.

Embodiments of the invention will be described below with reference to accompanying drawings. Although the drawings show specific embodiments in accordance with the principles of the invention, the drawings are shown for the purpose of understanding the invention, and are not to be used for limiting interpretation of the invention.

A device according to a first embodiment will be described with reference to <FIG> is a schematic configuration diagram showing a configuration of an observation device <NUM> according to the first embodiment. The observation device <NUM> is a device for observing a particulate sample. The particulate sample means, for example, a sample including particles, cells, or bacteria. The meaning of the "particles" is not particularly limited, and may be defined as, for example, particles including organisms such as cells and bacteria, and may be non-organisms such as latex particles and polystyrene beads.

As shown in <FIG>, the observation device <NUM> includes, as main components, an illumination optical system <NUM>, a sample container <NUM>, a pedestal <NUM>, an XY stage <NUM>, an objective lens <NUM>, an objective lens actuator <NUM>, an imaging camera <NUM>, and a computer <NUM>.

The illumination optical system <NUM> uniformly illuminates the particulate sample. For example, when the particulate sample is placed on a bottom surface of the sample container <NUM>, the bottom surface of the sample container <NUM> is uniformly illuminated. The illumination optical system <NUM> is constituted by an optical system such as Kohler illumination.

The sample container <NUM> includes a storage portion capable of holding the particulate sample. The particulate sample can be provided as, for example, one or more sample liquids. As the sample container <NUM>, for example, a petri dish, a dish, or a microliter plate is used. The sample container <NUM> holds a biological particulate sample such as cells or bacteria inside the sample container <NUM> or in a well. The sample container <NUM> can be used for operations such as cell culture and bacteria culture, and in particular, can be used for identification culture and culture for drug sensitivity testing.

The pedestal <NUM> can hold the sample container <NUM>. The pedestal <NUM> preferably has a structure in which an upper surface and a lower surface of a measurement sample surface (that is, upstream or downstream in an optical path) in the sample container <NUM> transmit light. As a structure that transmits light, a transparent member may be used, and a void having no shielding structure or the like may be used.

The XY stage <NUM> can move, in an X direction and a Y direction, the pedestal <NUM> on which the sample container <NUM> is placed. The XY stage <NUM> may include a heater or the like (not shown) that adjusts a temperature of the sample container <NUM>. As the heater, for example, a transparent glass heater can be placed on a bottom surface or a periphery of the XY stage <NUM>. In addition, the entire optical system may be surrounded by a heat-insulating material, and an internal temperature may be adjusted by the heater.

The objective lens <NUM> is held on the objective lens actuator <NUM>. The objective lens actuator <NUM> is an actuator that moves the objective lens <NUM> in a Z direction (an illumination optical axis direction), and can move a focal position of the objective lens <NUM> in a depth direction of the sample container <NUM>. The imaging camera <NUM> can be focused on the measurement sample surface of the sample container <NUM> by an operation of the objective lens <NUM>.

The imaging camera <NUM> functions as an image acquisition unit that acquires an image (a sample image) representing the particulate sample. Such a configuration is suitable for a case where the particles have translucency. In the present embodiment, the sample image is a transmission image, that is, an image formed by light transmitted through the particulate sample. The imaging camera <NUM> is provided at a focal position of the objective lens <NUM>, that is, at a position where an image of the particulate sample is formed. If the objective lens <NUM> is compatible with an infinity correction optical system, an image forming lens is provided between the imaging camera <NUM> and the objective lens <NUM>. The imaging camera <NUM> images, for example, the sample image as a microscope image. The imaging camera <NUM> has a function of converting the imaged sample image into an electric signal and outputting or transmitting the electric signal. In the present embodiment, the imaged sample image is transmitted to the computer <NUM>.

An optical filter (not shown), such as a color glass filter or an interference filter, may be appropriately inserted between the imaging camera <NUM> and the objective lens <NUM> as necessary.

The computer <NUM> can be configured using a known computer, and includes a arithmetic processing unit that performs various kinds of arithmetic processing and controls, and a storage unit that stores information. The storage unit may include a temporary, volatile, or a transient storage medium such as a semiconductor memory device, may include a non-transitory, non-volatile, or non-transient storage medium such as a hard disk, or may include both of the storage mediums. In addition, the computer <NUM> may include an input device (a mouse, a keyboard, or the like) that receives an input from a user and a display device (a display or the like) that displays a measurement result. In the present embodiment, the computer <NUM> functions as a data processing unit that performs arithmetic processing related to the sample image, and executes a data processing step for performing the arithmetic processing related to the sample image.

In the present embodiment, cells or bacteria, which are biological particulate samples, are used as a target to be observed by the observation device <NUM>. The cells or bacteria are cultured in a <NUM>-well microliter plate, and changes over time are measured.

<FIG> shows an example of the changes over time in an image (a transmission light image) when the particulate sample is cultured at a constant temperature state. <FIG> is an image immediately after start of the observation, <FIG> is an image after about <NUM> hours from the start of the observation, and <FIG> is an image after <NUM> hours from the start of the observation. <FIG> shows a state in which the particulate sample grows with culture time. After about <NUM> hours (<FIG>), the particulate sample can be individually identified, but after <NUM> hours (<FIG>, the particles are close to each other or overlap with each other, and a contour of each particle becomes unclear. In addition, brightness decreases as a whole, and it is difficult to identify each particle. As a result, in the particle counting method as in PTL <NUM>, it is difficult to identify the particles, which makes it difficult to accurately count the particles.

<FIG> shows an enlarged image of one particle in the particulate sample and property analysis diagrams of the image. <FIG> is the enlarged image in which one particle is placed in the vicinity of the center, and includes a region R1 where the particle is present, an upper left region R2, an upper right region R3, a lower left region R4, and a lower right region R5.

<FIG> is a brightness profile at a diagonal line D extending from the upper left to the lower right in <FIG>. A horizontal axis represents a distance from an upper left vertex, and a vertical axis represents brightness of pixels. An increase in brightness corresponding to the particle in the vicinity of the center of the image is observed, and a decrease in brightness corresponding to a periphery (edge) of the particle is observed at both ends of the brightness increase portion.

<FIG> is a histogram showing the brightness profile of each of the regions R1 to R5. A horizontal axis represents the brightness, and a vertical axis represents the number of pixels having the brightness. In this histogram, the brightness of each pixel is represented by <NUM> levels of <NUM> to <NUM>, <NUM> is the lowest brightness (dark) and <NUM> is the highest brightness (bright). Only a histogram H1 of the region R1 is indicated by a thick line, and histograms of the regions R2 to R5 are indicated by thin lines. In <FIG>, the histograms of the regions R2 to R5 are not particularly and individually specified (the histograms are not essential in the description of the present embodiment).

Due to the presence of the particle, the brightness in the vicinity of the center of the particle is high and the brightness of the peripheral portion of the particle is low due to a diffraction phenomenon of light or the like. Therefore, the histogram H1 of the region R1 is wider than histograms H2 of the regions R2 to R5. That is, the histogram H1 of the region R1 has a portion H1a formed of low-brightness pixels having brightness of about <NUM> to <NUM> and a portion H1b formed of high-brightness pixels having brightness of about <NUM> to <NUM>.

As described above, a feature occurs that the center portion of the particle has high brightness, and the periphery of the particle has low brightness when the particle is present. By using the feature, particles can be identified using a known method. In the particle counting method in related art, such a method is used.

However, the inventors have found that the presence of particles can be simply confirmed by extracting a portion having lower brightness as compared with a portion where no particle is present. An example of an operation of the observation device <NUM> based on the principle will be described below.

<FIG> is a flowchart showing an example of an operation of the observation device <NUM>, and includes a data processing process to be performed on the sample image. The flowchart represents a method according to the first embodiment. First, in the observation device <NUM>, predetermined variables are set based on a reference image (step S1). For example, as the reference image, an image including a portion where no particle is present is prepared.

As the reference image, any image can be used, for example, an image of a region, in a sample image, where no particle is present may be used, a sample image in a case where the number of particles is <NUM> may be used, or a sample image in a case where the number of particles can be regarded as <NUM> may be used. When such a reference image is used, a reference suitable for the sample image can be set. In addition, alternatively, another image that is freely created may be used. The reference image may be corrected in advance for sensitivity unevenness or the like.

In addition, as the reference image, a fixed reference image may be used for different sample images, or an appropriate reference image may be selected or imaged for each sample image. In particular, a different reference image may be imaged every time for each particulate sample.

The observation device <NUM> sets brightness of the reference image as reference brightness M. Any method for defining the reference brightness M based on the reference image can be used, and for example, the reference brightness M can be defined for each pixel of the reference image. As a more specific example, brightness of a pixel at a certain position in the reference image may be set as the reference brightness M when a pixel at the same position in the sample image is evaluated. In addition, only one reference brightness M can be defined for the entire reference image, and for example, an average value of brightness of all pixels in the reference image is set as the reference brightness M, and the reference brightness M may be compared with the brightness of each pixel in the sample image.

The observation device <NUM> calculates a standard deviation σ of the brightness of pixels in the reference image. Further, the observation device <NUM> acquires a threshold parameter k to be described later. The value of k may be stored in advance by the observation device <NUM> or may be input via any input device.

Next, the observation device <NUM> images a sample by the imaging camera <NUM> and acquires an imaged sample image (step S2, an acquisition step). Here, image correction of various known types may be performed on the sample image.

Then, in the sample image, a portion having low brightness as compared with the reference brightness M is extracted (step S3, an extraction step). In the present embodiment, the brightness is not directly compared with the reference brightness M, but is compared with a threshold brightness Th calculated according to the reference brightness M. The threshold brightness Th is determined to be a value smaller than the reference brightness M.

According to the present invention, the threshold brightness Th is calculated by using the reference brightness M and the standard deviation σ of the brightness of the pixels in the reference image. For example, the threshold brightness Th is calculated as Th = M - kσ. Here, k is the threshold parameter described above and is a real positive number. A value of k is, for example, within a range of <NUM> ≤ k ≤ <NUM>, and for example, k = <NUM>. Then, the observation device <NUM> extracts a pixel for which I < Th = M - kσ is satisfied for brightness of I of the pixel from pixels of the sample image as a low brightness pixel.

When particles (for example, cells) grow and are concentrated such that the particles overlap with each other as in <FIG>, a region where the particles are not present is substantially not found, and the intensity of the entire pixels decreases. Therefore, the contrast also decreases, and it is difficult to identify the particles by the particle counting method in related art.

Thus, in the present embodiment, the particles are quantitatively measured using a calibration curve as follows. As a result, even when the particles are concentrated and the particles overlap with each other, the count loss as described in relation to <FIG> is prevented, and the processing is performed such that the amount of the particulate sample to be calculated increases as the actual amount of the particulate sample increases.

<FIG> are histograms in which brightness distribution of all pixels of a certain particulate sample are displayed side by side at each time from the transmission image obtained at each culture time. The horizontal axis represents the brightness, and the vertical axis represents the number of pixels. In <FIG>, a position where the threshold brightness is Th = M - kσ when k = <NUM> is indicated by a broken line.

"m" in the figures represents a unit of time, and represents "minute" in this example. For example, <FIG> shows a brightness distribution of an image in a case where the culture time is <NUM> minutes (that is, an image imaged at a time point when <NUM> minutes have elapsed from the start of culture) and a brightness distribution of an image in a case where the culture time is <NUM> minutes (that is, an image imaged at a time point when <NUM> minutes have elapsed from the start of culture).

An image at the start of culture was used as a reference image, and a brightness distribution of each image obtained by subtracting the reference image from the image at each time (that is, a distribution of values obtained by subtracting the reference brightness from the brightness of the pixels) was displayed. Therefore, the brightness values are distributed in both positive and negative directions. The subtraction was performed in units of pixels. The similar processing can be performed without subtracting the reference image, and in this case, a value of the horizontal axis in the figures changes.

It can be seen from <FIG> that, when the culture time is <NUM> minutes or less, a peak is found in the vicinity of brightness <NUM> and the particles grow without substantially overlapping with each other. When the culture time exceeds <NUM> minutes, a peak position is shifted in a negative direction as shown in <FIG>. That is, due to overlap of the particles or the like, a transmission intensity of the pixel at a position where the particle is present decreases. Further, as time elapses, the brightness of the transmission image decreases. In such a state, the particles cannot be accurately counted even when a particle identification method in related art such as the particle counting method is used, and as a result, the count loss increases, and the number of particles reduces.

Thus, in the present embodiment, as described in step S3, by performing processing of extracting the region having low brightness as compared with the reference brightness M, and setting the region as a particle present region, the influence of the count loss described above is reduced.

The computer <NUM> recognizes or quantitatively measures the particulate sample based on the region formed of the extracted low-brightness pixel (low-brightness region) (step S4, a recognition step or quantitative measurement step). That is, the computer <NUM> functions as an extraction unit that extracts a low-brightness pixel, and functions as a particle recognition unit or a particle identification unit that recognizes the particulate sample. As a result, the observation device <NUM> recognizes the particulate sample based on the extracted low-brightness pixel. As a more specific example, it can be determined that the particulate sample is present in the low-brightness region. In this way, the observation device <NUM> functions as a particle recognition device.

In addition, the observation device <NUM> can quantitatively measure the particulate sample based on the extracted low-brightness pixel. That is, the computer <NUM> functions as a particle quantitative measurement unit that recognizes and quantitatively measures the particulate sample. Here, the "quantitative measurement" includes, in addition to accurately measuring of the number of particles, and calculating of the approximate number of particles, calculating the concentration of particles, and the like. In this way, the observation device <NUM> functions as a particle quantitative measurement device.

When the quantitative measurement is performed, a specific method can be optionally designed, and for example, it is possible to calculate the number of low-brightness pixels and quantitatively measure the particulate sample based on this number. Further, for example, it is possible to calculate a ratio of the number of low-brightness pixels to a total number of pixels of the sample image and quantitatively measure the particulate sample based on the ratio.

The observation device <NUM> can calculate the number of particles, the concentration, and the like using the calibration curve or the like obtained in advance. For example, the computer <NUM> may function as a calibration curve acquisition unit that acquires a calibration curve which associates a value related to the low-brightness pixel with the amount of the particulate sample. The "value related to the low-brightness pixel" can be the number of low-brightness pixels, the ratio of the low-brightness pixels in the sample image, or the like. Such a calibration curve may be stored by the computer <NUM> in advance.

In addition, for example, the observation device <NUM> (or the particle quantitative measurement unit described above) may quantitatively measure the particulate sample based on the calibration curve and the value related to the low-brightness pixel in the sample image. As a more specific example, it is possible to use a function that gives the amount of the particulate sample in accordance with the number of the low-brightness pixels or a function that gives the amount of the particulate sample in accordance with the ratio of the low-brightness pixels.

As a more specific example, the relation between the culture time and the amount of the particulate sample (for example, the number of cells) is obtained by a separate experiment or the like, and the relation between the culture time and the number of low-brightness pixels is obtained based on information shown in <FIG>. Based on these two types of relations, it is possible to create the calibration curve that associates the number of the low-brightness pixels with the amount of the particulate sample. As described above, based on the calibration curve and the number of low-brightness pixels in a desired sample image, it is possible to determine the amount of the particulate sample in the sample image.

Next, the observation device <NUM> determines whether or not to end the measurement (step S5). For example, it is determined whether or not a measurement time designated in advance has elapsed, or it is determined to end the acquisition of the sample image when a predetermined end operation is received from a user of the observation device.

When the measurement is not ended, the observation device waits for a predetermined time designated in advance (step S6), and the processing returns to step S2. In this case, the acquisition of the sample image is repeated after the predetermined time. In this way, the observation device according to the present embodiment can acquire sample images at predetermined time intervals and acquire time-lapse information. For example, the imaging camera <NUM> acquires sample images at the predetermined time intervals. In this way, change over time in the amount of the particulate sample can be monitored.

As described above, for example, when the number of particles is small, the particles are present substantially without overlapping with each other. The low-brightness region per particle is substantially the same for each particle, and the low-brightness region in the entire image increases substantially in proportion to the increase in the number of particles, so that it is possible to perform the quantitative measurement more accurately than the method in related art using the change in the intensity of the transmission light as in PTL <NUM>. On the other hand, when the number of particles is large, overlap of the particles occurs, the brightness distribution of the region corresponding to the particle image is shifted to a smaller side, the entire region becomes dark, and the ratio of the low-brightness region in the particle present region increases. Therefore, as the number of particles increases and more particles overlap with each other, the number of pixels determined to have low brightness in the particle present region increases, so that it is possible to perform the quantitative measurement more accurately than the method in related art using the particle counting method as in PTL <NUM>.

<FIG> is a graph showing a change, with respect to the culture time, in the number of pixels in a low-brightness region (the particle present region) in the examples shown in <FIG>. When the culture time is short, as described with reference to <FIG> and <FIG>, pixels whose brightness I is less than the threshold brightness Th are extracted, and the change in the number of particles can be detected with high sensitivity.

Further, even in the case where the culture time is long and the particles are concentrated or overlap with each other, the calculation result of the number of particles shows a substantially monotonic increase. When the culture time is longer than <NUM> minutes, the particles are present over the entire image, the number of low-brightness pixels is saturated, but unlike that shown in <FIG>, the number of low-brightness pixels does not decrease. Therefore, even if the number of particles increases beyond a measurement limit, an erroneous measurement value is not obtained.

In this way, according to the particle recognition device, the particle quantitative measurement device, a particle recognition method, the particle quantitative measurement method related to the observation device <NUM> of the present embodiment, accurate quantitative measurement can be performed in a wide range obtained by combining countable ranges of the two methods in related art, so that the range of the number of particles within which the particles in the particulate sample can be accurately recognized becomes wider.

The observation device <NUM> can be applied to culture monitoring of cells or bacteria, identification of cells or bacteria, drug susceptibility testing of cells or bacteria, and the like, and can perform accurate measurement or testing over a wide range of the number of particles.

For example, the sensitivity is high when the amount of the particulate sample is small, a minute change in the number of particles can be accurately detected, and even when the amount of the particulate sample is large and the particulate samples overlap with each other, the change in the number of particles can be detected without count loss.

The observation device <NUM> according to the present embodiment is not limited to the application of the culture monitoring or testing of cells or bacteria, and is also applicable to, for example, measurement of aggregates in an immunoreaction measurement using an aggregation reaction of fine particles. It is also possible to reduce a phenomenon such as a prozone.

According to the observation device <NUM>, a region extraction processing can be easily performed just by setting the threshold parameter k, and the cost can be reduced. The processing speed can also be increased.

In a second embodiment, a logical sum of the low-brightness region according to the first embodiment and a region extracted by another method is obtained.

<FIG> shows an example of logical sum calculation performed by an observation device according to the second embodiment. A colored portion in <FIG> represents a low-brightness region determined by the observation device <NUM> according to the first embodiment. <FIG> roughly corresponds to <FIG>, and a peripheral portion of a particle is extracted as a low-brightness region, but a central portion of the particle is not a low-brightness region since the brightness is high.

On the other hand, a colored portion in <FIG> represents a particle present region detected using a method other than that of the first embodiment. For example, in an observation device according to a modification, the computer <NUM> functions as an individual identification unit that extracts an individual identification region from a sample image by individually identifying particles based on the sample image. Any method for individually identifying the particles may be used, and for example, a known contrast method may be used, or other methods may be used.

A colored portion in <FIG> represents a logical sum region obtained by a logical sum of the low-brightness region formed of low-brightness pixels shown in <FIG> and the individual identification region shown in <FIG>. The observation device (or the particle quantitative measurement unit described above) according to the modification quantitatively measures the particulate sample based on the logical sum region shown in <FIG>.

For example, the observation device (or the particle quantitative measurement unit described above) according to the second embodiment can quantitatively measure the particulate sample based on:.

When the quantitative measurement is performed based on the number of the pixels in the logical sum region, for example, it is possible to calculate a ratio of the number of pixels in the logical sum region to the total number of pixels of the sample image and quantitatively measure the particulate sample based on the ratio.

The observation device according to the second embodiment can calculate the number of particles, the concentration, and the like using the calibration curve or the like obtained in advance. For example, the computer <NUM> may function as a calibration curve acquisition unit that acquires a calibration curve which associates a value related to the pixel in the logical sum region with the amount of the particulate sample. As described above, the "value related to the pixel in the logical sum region" can be the average value of the brightness of the pixels in the logical sum region, the integration value of the brightness of the pixels in the logical sum region, the total number of pixels in the logical sum region, and the like.

In addition, for example, the observation device according to the second embodiment may quantitatively measure the particulate sample based on the calibration curve and the value related to the pixel in the logical sum region in the sample image. As a more specific example, it is possible to use a function that gives the amount of the particulate sample in accordance with the number of the pixels in the logical sum region or a function that gives the amount of the particulate sample in accordance with the ratio of the pixels in the logical sum region.

In this way, according to a particle recognition device, a particle quantitative measurement device, a particle recognition method, and a particle quantitative measurement method related to the observation device of the second embodiment, a particle present region can be extracted using a known method in addition to the method in the first embodiment, so that there is a possibility that more accurate recognition or quantitative measurement can be performed depending on properties of the particulate sample. In particular, when the number of particles is small, the number of pixels extracted in the logical sum region for each particle increases, so that there is a possibility that a change in the number of particles when the number of particles is small can be detected with higher sensitivity.

The third embodiment is obtained by adding processing related to noise and outlier removal for a sample image to the first embodiment or the second embodiment.

<FIG> is a flowchart showing an example of an operation of an observation device according to the third embodiment, and includes a data processing process for the sample image. The flowchart represents a method according to the third embodiment. In the processing in <FIG>, first, the observation device acquires a sample image (step S11). The processing can be, for example, the same as that of step S2 in <FIG>.

Next, the observation device determines whether or not to end the acquisition of the sample image (step S12). For example, it is determined whether or not a measurement time designated in advance has elapsed, or it is determined to end the acquisition of the sample image when a predetermined end operation is received from a user of the observation device.

When the acquisition of the sample image is not ended, the observation device waits for a predetermined time designated in advance (step S13), and the processing returns to step S11. In this case, the acquisition of the sample image is repeated after the predetermined time. In this way, the observation device according to the third embodiment can acquire sample images at predetermined time intervals and acquire time-lapse information. For example, the imaging camera <NUM> acquires sample images at the predetermined time intervals. Thus, change over time in the amount of the particulate sample can be monitored.

In step S12, when the acquisition of the sample image is ended, a quantitative measurement operation is started for the acquired sample images. That is, as to be described below, the computer <NUM> functions as a particle quantitative measurement unit and quantitatively measures the particulate sample for each sample image.

An example of processing in steps S14 to S18 in <FIG> will be described with reference to <FIG> each represent an example of an image in each step according to the third embodiment. First, the computer <NUM> performs shading correction and the like on each sample image (step S14). In this way, as shown in <FIG>, sensitivity unevenness, light source unevenness, distortion and the like are corrected.

Next, the computer <NUM> selects a reference image (step S15). For example, the reference image is selected in the same manner as the first embodiment. In S15, a variation of the image in the reference image (for example, a variation of brightness values of pixels) is calculated. According to the present invention, a standard deviation σ of the brightness of the pixels in the reference image is calculated.

Next, the computer <NUM> subtracts the reference image from each sample image (step S16). For example, relative brightness Ir = I - M is calculated by subtracting the reference brightness M from the brightness I of each pixel. In this way, an image as shown in <FIG> is obtained.

Next, the computer <NUM> extracts a low-brightness region based on the image obtained by the subtraction (step S17). For example, a pixel for which Ir < This satisfied for the relative brightness Ir is extracted as the low-brightness pixel. Here, for example, Th is defined as Th = -kσ. In this way, an image as shown in <FIG> is obtained. Here, although the low-brightness pixel is formally extracted based on the relative brightness Ir, the result is substantially the same as the result of extracting a pixel for which I < M - kσ is satisfied for the brightness I of each pixel.

In the third embodiment, a value of a threshold parameter k can be optionally selected, and may be set to, for example, k = <NUM>. As a specific method for extracting the low-brightness region, binarization processing may be performed. That is, the low-brightness pixel may be represented by bit <NUM>, and other pixels (high-brightness pixels) may be represented by bit <NUM>.

Next, the computer <NUM> performs processing related to the noise and outlier removal on the low-brightness region (step S18). The noise and outlier removal is to eliminate an influence of a pixel considered to be a noise or a pixel considered to be an outlier, a known method can be used, and a specific example will be described below. For example, in step S17, a pixel that is not a low-brightness pixel (that is, a high-brightness pixel) and is surrounded by the low-brightness pixels is determined to be a noise, and is extracted as a low-brightness pixel regardless of the result of step S17. In this way, an image as shown in <FIG> is obtained.

Here, a specific definition of the "pixel surrounded by low-brightness pixels" can be appropriately designed by a person skilled in the art, for example, a condition may be set that a total of four pixels on upper, lower, left, and right sides of the pixel are all low-brightness pixels, a condition may be set that a total of eight pixels including pixels in oblique directions are all low-brightness pixels, or other conditions may be used.

Next, the computer <NUM> recognizes or quantitatively measures the particulate sample based on the low-brightness region (step S19). The recognition or quantitative measurement can be performed in the same manner as step S4 of the first embodiment.

As described above, according to a particle recognition device, a particle quantitative measurement device, a particle recognition method, and a particle quantitative measurement method related to the observation device of the third embodiment, in addition to an effect of the first embodiment or the second embodiment, more accurate quantitative measurement can be performed by the noise removal.

The following modifications can be made on the first to third embodiments.

In the first to third embodiments, in addition to the low-brightness pixel, a high-brightness pixel may be extracted. For example, among the pixels of the sample image, a pixel for which I > M + kσ is satisfied may be extracted as the high-brightness pixel. In that case, the particulate sample can be recognized based on both the extracted low-brightness pixel and high-brightness pixel. For example, it can be determined that the particle is present in a region in which any one of the low-brightness pixel and the high-brightness pixel is present.

In the first to third embodiments, a method may be used in which a correction value based on the brightness of the pixel is determined in advance for each pixel, the each pixel is multiplied by the correction value and a low-brightness pixel is extracted based on the result. A method may also be applicable in which the average number of pixels per particle is acquired in advance, the number of the low-brightness pixels is divided by the average number, and the number of particles is calculated. These methods also have the same effects as that of each of the embodiments described above.

In the first to third embodiments, for a saturated low-brightness region, the amount of the particulate sample may be corrected based on an amount of decrease in the brightness. Even in an image or region in which the low-brightness pixels are saturated (that is, an image or region in which the low-brightness pixels no longer increases even if the amount of the particulate sample increases), as shown in <FIG> and <FIG>, if the amount of the particulate sample increases, the brightness decreases.

As a specific method of such a modification, a function for correcting the amount of particulate sample according to a statistical amount of the brightness of the pixels in a certain image or a specific region of the image may be used. The statistical amount of the brightness of the pixels may be, for example, a value of peak brightness in a brightness histogram as in <FIG>, or an average value of the brightness of the pixels. For example, it is possible to use a function for correcting the amount of particulate sample such that the amount of particulate sample increases as the peak brightness decreases. Since the decrease in brightness is information that more particles overlap with each other, the correction is performed based on the amount of decrease in brightness, thus the measurement range of the amount of particulate sample can be further widened, and a wider dynamic range can be obtained.

In the first to third embodiments, a format of the sample image can be optionally designed by a person skilled in the art. For example, an <NUM>-bit grayscale image may be used, or a <NUM>-bit grayscale image may be used. In addition, a color image may be used after the color image is converted into a grayscale image.

In the first to third embodiments, a transmission light image is used as the sample image, but the sample image is not limited to the transmission image. For example, a phase difference image, a differential interference image, or the like that can be acquired by a known means can also be used as the sample image. In this case, the processing is the same.

In the first to third embodiments, the particulate sample has translucency, but a particulate sample that does not have translucency may be used. In addition, as the particulate sample, for example, particles, cells, or bacteria are used, and other samples may also be used.

In the first to third embodiments, image correction may be performed on the sample image. For example, image processing such as the shading correction, the sensitivity unevenness correction, the noise removal, and smoothing may be performed. In addition, the light source unevenness, detector sensitivity unevenness, and the like may be corrected. When these corrections are performed, it is possible to perform a more accurate quantitative measurement.

In the first to third embodiments, the optical system of the observation device does not need to have a configuration as in <FIG>, and may have any configuration as long as the image acquisition unit that acquires the sample image can be provided. In addition, the sample image may be acquired via a communication network, a portable storage medium, or the like without including the optical system.

In the first to third embodiments, the observation device may not quantitatively measure the particulate sample. For example, the observation device may be a device that identifies the particle present region and outputs a position, a shape, or the like of the region.

Claim 1:
A particle quantitative measurement device, comprising:
an image acquisition unit (<NUM>) configured to acquire a sample image representing a particulate sample as a sample including particles; and
a data processing unit (<NUM>) configured to perform arithmetic processing on the sample image, wherein
the data processing unit includes:
an extraction unit configured to extract low-brightness pixels, for which I < M - kσ is satisfied for brightness I, from pixels of the sample image, wherein M represents a reference brightness for a reference image, k represents a real positive number, and σ represents a standard deviation for the brightness of pixels in the reference image, and
a particle quantitative measurement unit configured to quantitatively measure the number of particles in the particulate sample based on the number of the extracted pixels.