JP2008304298A - Device and method for discriminating germ - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device for discriminating germ capable of measuring size even in the case where the germ has such a crushed shape like a dogleg shape to the vertical direction. <P>SOLUTION: Light is radiated to a sample to emit fluorescence, and the image containing fluorescence from the sample is photographed, and the fluorescence is extracted from the image. The region is repeatedly divided until the area of the region is less than a predetermined area, and the size of the sample is computed based on the length of each dividing segment for dividing the region, and whether or not the sample is a germ is determined based on the size. In this dividing processing, the position of the center of gravity in the region is computed and the periphery of the region is computed. In the peripheral points forming the periphery, the object peripheral points which are positioned from the position of the center of gravity at a distance not more than a predetermined distance are computed and a linear line passing the position of the center of gravity and the object peripheral point is computed. One of the segments which combine two peripheral points on a linear line is computed as a dividing segment, and the region is divided by the dividing segment and whether or not the area of the divided region is less than a predetermined area is determined. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a bacterium discrimination apparatus and a bacterium discrimination method, and can be suitably used to detect a bacterium by determining its shape from its thickness, particularly in fluorescence detection of acid-fast bacteria.

  The agar culture method is the most common method for classifying bacteria. This is an inspection method in which colonies formed by applying a sample to an agar medium and culturing for a predetermined time are classified by an observer using a microscope, with or without staining. However, since this agar culture method is basically performed manually, it is troublesome and it takes time to determine the type of bacteria for culturing.

  Therefore, there is a fluorescence observation method in which acid-fast bacteria are observed without culturing. This is an inspection method in which a specimen is subjected to a fluorescent staining process so that only bacteria are fluorescent, and the fluorescence is observed by an observer using optical means such as a falling-down fluorescent microscope. In recent years, in order to maintain the accuracy of the test even when mycobacteria are not evenly distributed in the sputum, instead of smearing directly on the slide glass, the sputum collected by equalizing the sputum with a centrifuge A method of collecting bacteria is mainly used in which the material is smeared on a slide glass and observed.

  In addition, there is a method in which each field of view is automatically photographed using photographing means such as a camera, and bacteria are automatically detected from the photographed image, instead of being observed by an observer with optical means. As a detection method, the acid-fast bacilli has a characteristic compared to the background color, and since the acid-fast bacilli are gonococci, they have a capsule shape. Bacteria are identified by judging. Since the shape of bacteria has the characteristic that the thickness is constant, the shape is determined by calculating the degree of dispersion of the thickness.

  However, the bacteria may be bent when the collection method is centrifuged, and it is necessary to consider the bending. The following methods are known as methods for measuring the thickness in consideration of such bending.

  For example, an image of a long object is taken, and the outline of the object and the longitudinal direction of the circumscribed rectangle are calculated. At the point where the straight line equally dividing the circumscribed rectangle at one side portion in the longitudinal direction and the outline intersect, the point on the outline that minimizes the distance is calculated, and the average of the distance is calculated as the thickness. (For example, refer to Patent Document 1.)

JP-A-11-194014

  However, in the conventional configuration, in the case where the shape of the bacteria is a shape that is collapsed in the vertical direction of the character “ku”, the overlapping of the regions in the longitudinal direction, it is not possible to calculate the one side portion in the longitudinal direction, Thickness may not be measured.

  The present invention has been made in view of the above circumstances, and is a bacteria discrimination apparatus capable of measuring the thickness even when the shape of the bacteria is a shape that collapses in the vertical direction of the character An object is to provide a method for distinguishing bacteria.

In order to achieve the above object, a first bacteria discrimination device of the present invention includes an optical unit that irradiates light to fluoresce a sample, an imaging unit that captures an image including fluorescence from the sample, and the image. A region extracting unit that extracts a fluorescent region, a dividing unit that repeats the division of the region until the area in the region becomes less than a predetermined area, and a length of each dividing line segment for dividing the region. A thickness calculating unit that calculates the thickness of the sample, and a bacteria determination unit that determines whether or not the sample is a bacteria based on the thickness, and the dividing unit includes: Of the contour points that calculate the position of the center of gravity of the region, the contour calculation unit that calculates the contour of the region, and the contour points that form the contour, the distance from the center of gravity is equal to or less than a predetermined distance A target contour point calculation unit for calculating a contour point and the position of the center of gravity; A straight line calculation unit that calculates a straight line passing through the target contour point, a line segment calculation unit that calculates one of the line segments connecting the two contour points on the straight line as the divided line segment, A region dividing unit that divides the region by the dividing line segment and an area determining unit that determines whether the area of the divided region is less than the predetermined area are used.

  Moreover, the 2nd bacteria discrimination | determination apparatus of this invention is set as the structure which complete | finishes the repetition of the said division | segmentation, when the said division part repeats and the division line segment calculated repeatedly crosses another division line segment.

  Moreover, the 3rd bacteria discrimination | determination apparatus of this invention is set as the structure which complete | finishes the said division | segmentation repetition, when the division | segmentation part shows the frequency | count of repeating the said division | segmentation larger than predetermined number of times.

  Moreover, the 4th bacteria discrimination | determination apparatus of this invention is set as the structure which the said target contour point calculation part calculates the shortest contour point nearest to the position of the said gravity center as said target contour point among the said contour points.

  Further, in the fifth bacteria discrimination apparatus of the present invention, the target contour point calculation unit calculates a plurality of contour points that have a distance from the position of the center of gravity of the contour points that is equal to or less than a predetermined distance. The contour point having the shortest line segment length is calculated as the target contour point.

  In addition, in the sixth bacteria discrimination apparatus of the present invention, when the dividing line segment calculated repeatedly intersects with another dividing line segment, the line segment calculating unit calculates the center of gravity of the area where the dividing line segment is calculated. A line segment that passes through the position and is perpendicular to the dividing line segment is used as the dividing line segment.

  Further, in the seventh bacteria discrimination device of the present invention, the bacteria determination unit excludes the maximum value and the minimum value of the thickness of the specimen calculated by the thickness calculation unit, and based on the thickness. In this configuration, it is determined whether or not the specimen is bacteria.

  Moreover, the 8th bacteria discrimination | determination apparatus of this invention is set as the structure which the said bacteria determination part determines with the said specimen being bacteria, when the dispersion | distribution degree of the said thickness is below a predetermined value.

  Moreover, the 9th bacteria discrimination | determination apparatus of this invention is set as the structure which the said bacteria determination part determines with the said test substance other than bacteria, when the dispersion | distribution degree of the said thickness is larger than predetermined value.

  Further, in the tenth germ discrimination device of the present invention, when the germ discriminating unit has a thickness dispersion degree equal to or smaller than a predetermined value, and the number of divisions indicating the number of repetitions of the division is larger than the predetermined number of times. The specimen is determined to be bacteria.

  In the eleventh bacteria discrimination apparatus of the present invention, the bacteria determination unit has a thickness dispersion degree of a predetermined value or less, and a division number indicating the number of times the division is repeated is a predetermined number or less. In this case, the specimen is determined to be other than bacteria.

In addition, the first bacteria discrimination method of the present invention includes an optical step of irradiating light to fluoresce a specimen,
An imaging step for capturing an image including fluorescence from the specimen, a region extraction step for extracting a fluorescent region from the image, and division for repeating the division of the region until the area in the region is less than a predetermined area A thickness calculating step of calculating the thickness of the sample based on a process, the length of each dividing line segment for dividing the region, and whether the sample is a bacterium based on the thickness A bacterium determination step for determining whether or not the division step includes a centroid position calculation step for calculating a centroid position of the region, a contour calculation step for calculating a contour of the region, and a contour forming the contour Among the points, a target contour point calculating step for calculating a target contour point whose distance from the position of the center of gravity is equal to or less than a predetermined distance; a straight line calculating step for calculating a straight line passing through the position of the center of gravity and the target contour point; On the straight line, A line segment calculating step of calculating one of the line segments connecting the contour points as the dividing line segment, a region dividing step of dividing the region by the dividing line segment, and an area in the divided region is the predetermined area The method includes an area determination step for determining whether the area is less than the area.

  In the second bacteria discrimination method of the present invention, when the dividing line segment calculated repeatedly intersects with other dividing line segments in the dividing step, the repetition of the dividing is terminated.

  Moreover, the 3rd bacteria discrimination | determination method of this invention is set as the method of complete | finishing the said division | segmentation repetition, when the division | segmentation number which shows the frequency | count of repeating the said division | segmentation is larger than predetermined number in the said division | segmentation process.

  Moreover, the 4th bacteria discrimination | determination method of this invention is a method of calculating the shortest outline point nearest to the position of the said gravity center as said object outline point among the said outline points in the said object outline point calculation process.

  Further, in the fifth bacteria discrimination method of the present invention, in the target contour point calculating step, a plurality of contour points whose distance from the position of the center of gravity is equal to or less than a predetermined distance among the contour points are calculated, and the division is performed. The contour point having the shortest line segment length is calculated as the target contour point.

  Further, in the sixth method of determining a bacterium according to the present invention, in the line segment calculation step, when the dividing line segment calculated repeatedly intersects with another dividing line segment, the center of gravity of the area where the dividing line segment is calculated is calculated. A line segment that passes through the position and is perpendicular to the dividing line segment is used as the dividing line segment.

  The seventh bacteria discrimination method of the present invention is based on the thickness by excluding the maximum and minimum thicknesses of the specimen calculated by the thickness calculator in the bacteria determination step. In this method, it is determined whether or not the specimen is a bacterium.

  Moreover, the 8th bacteria discrimination | determination method of this invention is a method of determining that the said test substance is bacteria when the dispersion | distribution degree of the said thickness is below a predetermined value in the said bacteria determination process.

  Further, the ninth bacteria discrimination method of the present invention is a method in which, in the bacteria determination step, when the degree of dispersion of the thickness is larger than a predetermined value, the specimen is determined to be other than bacteria.

  Further, in the tenth bacterium determination method of the present invention, in the bacterium determination step, the dispersion degree of the thickness is not more than a predetermined value, and the number of divisions indicating the number of times the division is repeated is larger than the predetermined number. In this method, the specimen is determined to be bacteria.

  In the eleventh bacterial identification method of the present invention, in the bacterial identification step, the degree of dispersion of the thickness is a predetermined value or less, and the number of divisions indicating the number of repetitions of the division is a predetermined number of times or less. In this case, the specimen is determined to be other than bacteria.

  According to the present invention, it is possible to measure the thickness and determine the bacteria from the thickness even when the shape of the bacteria is such that it is crushed in the vertical direction of the character “ku”.

  Below, the bacteria discrimination | determination apparatus and bacteria discrimination | determination method in embodiment of this invention are demonstrated in detail with drawing.

  FIG. 1 is a block diagram illustrating an example of the configuration of a bacteria detection apparatus 100 according to an embodiment of the present invention. The bacteria detection apparatus 100 includes an optical unit 101, an imaging unit 102, an image processing unit 103, and a display unit 104. The bacteria detection apparatus 100 can realize a bacteria discrimination method.

  The bacteria detection device 100 is an example of a “bacteria discrimination device”. Further, the optical unit 101 has a function as an “optical unit”. The imaging unit 102 has a function as an “imaging unit”. Further, the image processing means 103 has functions as an “area extraction unit”, “division unit”, “thickness calculation unit”, and “bacteria determination unit”.

  In FIG. 1, a plate 1 is an inspection object. The optical means 101 irradiates the plate 1 with excitation light, and guides the fluorescence emitted when the bacteria on the plate 1 are excited to the imaging means 102. The image capturing unit 102 captures an area under fluorescence observation, generates an image, and outputs the image to the image processing unit 103. The image processing means 103 detects bacteria from the brightness and shape of the photographed image and outputs the detection result to the display means 104. The display unit 104 displays the detection result.

  Next, the plate 1 will be described in detail.

  The plate 1 uses a slide glass and is coated with a specimen adjusted so that bacteria are excited by excitation light and emit fluorescence. If necessary, a heat treatment or the like is performed. Examples of the specimen include human sputum and stool. Note that any plate 1 such as a petri dish may be adopted as the plate 1.

  Next, an example of the optical unit 101 will be described in detail with reference to FIG. FIG. 2 is an explanatory diagram showing an example of processing of the optical means 101 in fluorescence observation.

  The optical means 101 performs fluorescence observation using a fluorescence microscope. First, a mercury lamp or the like is used as the light source 201 to irradiate the plate 1 with light 202 containing ultraviolet rays. Light 202 from the light source 201 passes through the excitation filter 203. The excitation filter 203 transmits light having a wavelength necessary for the bacteria to fluoresce (hereinafter referred to as excitation light 204) and shields unnecessary light. Then, the excitation light 204 is guided to the objective lens 206 by the dichroic mirror 205 and irradiated onto the plate 1 via the objective lens 206. The dichroic mirror 205 is installed at an angle of 45 degrees with respect to the light 202 from the light source 201 and reflects only a specific wavelength. The bacteria in the specimen are excited by the irradiated excitation light 204 and emit fluorescence 207. The fluorescence 207 passes through the absorption filter 208 via the objective lens 206 and the dichroic mirror 205 and is guided to the imaging means 102. The absorption filter 208 transmits the fluorescence 207 and blocks light 209 other than the fluorescence 207 such as reflected excitation light 204 and external light. Note that any optical means 101 such as a micrometer capable of fluorescence observation may be employed as the optical means 101.

  Next, an example of the imaging unit 102 will be described in detail.

The imaging unit 102 uses a monochrome CCD camera, and is joined to a fluorescence microscope as the optical unit 101 by a C mount. Fluorescence 207 from the specimen guided by the optical means 101
Is captured by a CCD camera, and the captured image is output to the image processing means 103. Note that any imaging unit 102 such as a color CCD camera may be employed as the imaging unit 102.

  Next, an example of the configuration of the image processing unit 103 will be described in detail with reference to FIGS.

FIG. 3 is an explanatory diagram showing that the position of a pixel of an image is handled as the coordinates of a point on a two-dimensional coordinate. FIG. 3A is an explanatory diagram showing that the position of each pixel in the image is handled as the coordinates of a point on a two-dimensional coordinate system with the horizontal direction as the X axis, the vertical direction as the Y axis, and the lower left pixel as the origin. . FIG. 3B is an explanatory diagram showing that the center position 301 of the target pixel is an integer coordinate position (Xn, Yn) on the XY axes of the two-dimensional coordinates. For the image handled by the image processing means 103, the position of the pixel is handled as the coordinates of the point on the two-dimensional coordinate as shown in FIG.

  FIG. 4 is a flowchart showing an example of each step in the bacteria discrimination method performed by the image processing means 103.

  First, in step S101, the image processing unit 103 performs binarization so that bacteria can be extracted from the luminance information of the captured image. As a binarization method, for example, the following processing is performed on all pixels of an image. In the photographed image, the luminance value of the pixel in the fluorescent portion is higher than the luminance value of the pixel in the background portion. Therefore, binarization is performed by setting 1 if the luminance value of the pixel of interest is higher than the threshold value and setting it to 0 otherwise. As a method of determining the threshold value, for example, for an area where the observer has identified the bacteria and the background, the average value of the luminance values of the bacteria and the average value of the luminance values of the background are measured in advance, and the intermediate value is set as the threshold value. A pixel value of 1 is a pixel that is likely to be part of the bacterial region, and a pixel value of 0 is the background.

  Step S102 is a region extraction step, in which the image processing unit 103 extracts a region using the image binarized to 0 and 1 calculated in step S101. In the extraction of the area, the following processing is performed for all pixels of the binarized image. If the value of the pixel of interest is 1, if there is a value of 1 in the vicinity of the surrounding 8, that pixel is identified as the same region as the region of the pixel of interest. As a general method for extracting a region, there is labeling for identifying each region by giving a different label value for each region.

  In the following steps, one of the calculated areas is targeted.

  Step S103 is a center-of-gravity position calculation step, and the image processing unit 103 calculates the center of gravity for the region calculated in step S102.

  An example of a method for calculating the center of gravity in the region will be described with reference to FIG. FIG. 5 is an explanatory diagram showing an example of the extracted area 501 and the center of gravity 502 of the area 501. The average value of the coordinate positions of each pixel in the region 501 is calculated as the center of gravity 502. Specifically, first, the X coordinate and Y coordinate of each pixel are added. Then, the average value is calculated by dividing the added value by the number of pixels. The coordinate position represented by the average value of the X coordinate and the average value of the Y coordinate is calculated as the center of gravity 502.

  Step S104 is a contour calculation step, and the image processing unit 103 calculates a contour for the region 501 calculated in step S102.

An example of a contour calculation method will be described with reference to FIG. FIG. 6 is an explanatory diagram showing an example of a contour 601 in the extracted region 501 and a contour point 602 on the contour 601. For example, the pixel of interest in the region 501 is calculated as a pixel on the contour 601 when there is a background pixel in the vicinity of 8 of the pixel of interest. A pixel on the contour 601 is defined as a contour point 602.

  Step S105 is the shortest contour point calculation step, and the image processing means 103 calculates the distance between the center of gravity 502 and the contour point 602 calculated in the contour calculation step of step S104, and calculates the contour point with the shortest distance. . In calculating the distance, the distance between two points in two dimensions is obtained.

  An example of a method for obtaining the distance between two points will be described. In the two-dimensional XY coordinate system, the relational expression for obtaining the distance L between the two points when the coordinates (X1, Y1) of the center of gravity 502 and the coordinates (X2, Y2) of the contour point 602 are as follows. The square of the difference is calculated for each of X and Y and added to obtain the square root to calculate the distance L between the two points.

  An example of the contour point having the shortest distance will be described with reference to FIG. FIG. 7 is an explanatory diagram illustrating an example of a contour point 701 having the shortest distance from the center of gravity 502. The distance from the center of gravity 502 is calculated for all the contour points 602, and the contour point 701 having the shortest distance as shown in FIG. 7 is calculated.

  Step S106 is a straight line calculating step, in which the image processing means 103 calculates a straight line passing through the two points of the contour point 701 having the shortest distance from the center of gravity 502.

An example of a method for calculating a straight line passing through two points will be described with reference to FIG. FIG. 8 is a diagram illustrating an example of a straight line 801 passing through the contour point 701 having the shortest distance from the center of gravity 502. In the XY coordinate system, the relational expression of the straight line 801 passing through the two points when the coordinates (X1, Y1) of the center of gravity 502 and the coordinates (X2, Y2) of the contour point 701 having the shortest distance is as follows. .
Y−Y1 = ((Y2−Y1) / (X2−X1)) * (X−X1) (Expression 2)
Further, in Expression 2, the relational expression of the straight line 801 when X1 and X2 are equal is as follows.
X = X1 (Formula 3)
A straight line 801 is calculated using Expression 2 or 3.

  Step S107 is an intersection number calculation step, and the image processing means 107 calculates the number of intersection points.

  An example of the method for calculating the intersection will be described with reference to FIG. FIG. 9 is an explanatory diagram showing an example of the intersection of the straight line 801 and the contour 601 in FIG. In the case of FIG. 9, since the contour point 701 with the shortest distance is also an intersection, the intersection is the intersection 901a, the contour point 701 with the shortest distance, the intersection 901b, and the intersection 901c, and the number of intersections is four.

  In step S108, the image processing unit 103 confirms the number of intersections. If the intersection is one point, that is, if only one point of the contour point 701 having the shortest distance is the intersection, the process proceeds to the continuous division determination step in step S112. If there are two or more intersections, the process proceeds to the line segment calculation step in step S109.

  Step S109 is a line segment calculation step, and the image processing unit 103 calculates a line segment that divides the region 501.

An example of a line segment calculation method will be described with reference to FIGS. FIG. 10 is an explanatory diagram showing an example of a line segment 1001 on a straight line 801 dividing the region 501 in FIG. When there are two intersections, the two points are extracted as a line segment 1001. When there are more intersections than two points and there are a plurality of line segments 1001 to be divided, the line segment 1001 that divides the region 501 is narrowed down to one. As a narrowing condition, for example, a line segment 1001 including a contour point 701 having the shortest distance is selected. In the case of FIG. 9, the line segment 1001 that divides the region 501 can be two places from the intersection point 901a to the contour point 701 having the shortest distance and from the intersection point 901b to the intersection point 901c. In such a case, the line segment 1001 including the contour point 701 having the shortest distance as shown in FIG. 10 is calculated as a line segment 1001 that divides the region 501.

  In step S110, the image processing means 103 determines the intersection of line segments. As an example of the determination method, it is confirmed whether or not the line segment 1001 calculated in step S109 intersects with the previously calculated line segment 1001. If not, the process proceeds to the region dividing step in step S111. Transition to the continuous division determination process. If there is no previously calculated line segment 1001, that is, if the line segment 1001 has not been divided, the process proceeds to step S111.

  An example of the case where the line segments 1001 intersect will be described with reference to FIG. FIG. 11 is an explanatory diagram showing that the line segment 1101 newly calculated in the line segment calculation step in step S109 and the previously calculated line segments 1102 and 1103 intersect in the area 501 or the divided area. As shown in FIG. 11, when the newly calculated line segment 1101 intersects with one or both of the previously calculated line segment 1102 and line segment 1103, the process proceeds to step S <b> 112.

  Here, the division will be briefly described.

  The bacteria detection apparatus 100 first calculates a line segment in the area and divides the area into two. Subsequently, a line segment is calculated for each of the divided areas, and the division into two is repeated. When calculating a line segment for each area, only one is selected, but when viewed from the base area, a plurality of line segments are calculated. The bacteria detection apparatus 100 also calculates the center of gravity and the position of the shortest contour point for each divided area. The positions of the line segment, the center of gravity, and the shortest contour point obtained for each division are different.

  In step S110, it is determined that the plurality of line segments do not intersect. For example, in the case of performing the division three times, the newly calculated line segment indicates the line segment calculated for the third time, and the previously calculated line segment indicates the line segment calculated before the second time. Point to.

  Step S111 is a region dividing step, and the image processing unit 103 divides the region 501 by the line segment 1001 calculated in step S109.

  An example of the division will be described with reference to FIG. FIG. 12 is an explanatory diagram showing an example in which the region 501 is divided by a line segment 1001. As shown in FIG. 12, the area 501 is divided by a line segment 1001 into a divided area 1201a and a divided area 1201b.

  Step S112 is a continuous division determination step, and the image processing unit 103 repeats the division when the divided area 1201 can be divided. An example of the continuous division determination process in step S112 will be described with reference to FIG. FIG. 13 is a flowchart showing an example of detailed steps in the continuous division determination step of step S112.

  When the transition is made from step S111, the process transitions to step S201, and when the transition is made from step S108 or step S110, the process transitions to step S204.

  First, in step S201, when the process proceeds from step 111, the image processing unit 103 determines the priority of division of the divided area 1201, and temporarily stores the divided areas 1201 that are shifted to step S202 and waiting in order. A divided area 1201 is determined. As a determination method, for example, the first priority is given to the divided area 1201 that has been temporarily stored as the first priority, that is, the divided area 1201 that has been temporarily stored before. Then, as the second priority, priority is given to the one in which the center of gravity of the divided region 1201 is on the left or top. After the priority order is determined, the process proceeds to step S202 for the divided area 1201 having the highest priority order. If the process proceeds from step S205, the process proceeds to step S202 for the highest-priority divided area 1201 that is temporarily stored.

  In step S202, the image processing unit 103 determines the number of divisions. As an example of the determination method, the maximum predetermined number to be divided is determined in advance, and if the next division number, that is, the number obtained by adding 1 to the current division number is equal to or less than the predetermined number, the process proceeds to step S203. If more, the process proceeds to step S204. The predetermined number can be determined by dividing the region 501 into N equal parts if, for example, the aspect ratio of the unbent bacteria when the shorter one of the objects is set to 1 is 1: N. The aspect ratio of 1201 is 1: 1. Since there is a possibility that each divided area 1201 can be further divided, it can be equally divided into 2N at the maximum. Since the number of divisions at this time is 2N-1, the predetermined number is an integer that is rounded up to the nearest 2N-1.

  In step S203, the image processing unit 103 determines the transition destination by determining the area of the divided region 1201. As an example of the determination method, if the area of the divided region 1201 is equal to or larger than the predetermined value A, the process proceeds to step S103, and if smaller than the predetermined value A, the process proceeds to step S204. As a method for determining the predetermined value A, for example, if the maximum predetermined number to be divided is determined in advance, a value obtained by dividing the area of the region 501 by the predetermined number is set as the predetermined value A.

  In step S204, the image processing unit 103 ends the division of the target divided area 1201, and proceeds to step S205.

  In step S205, the image processing unit 103 confirms whether all the divided areas 1201 have been divided. As an example of the confirmation method, it is checked whether there is a divided area 1201 temporarily stored in step S201. If there is no divided area 1201 temporarily stored, the process proceeds to step S113, and the temporarily stored divided area 1201 is obtained. If there is, the process proceeds to step S201.

  In such a continuous division determination step of step S112, the image processing unit 103 repeats the division when the divided area 1201 can be divided.

  Step S113 is a thickness calculating step, in which the image processing means 103 extracts the length of the line segment 1001 calculated when the region 501 is repeatedly divided as the thickness at each location. Assuming the region before the division, the respective portions indicate a plurality of line segments calculated by the division, and the portions where the plurality of line segments are calculated.

  Step S114 is a bacteria determination process, and the image processing unit 103 analyzes the distribution of the thickness calculated in the thickness calculation process of step S113 to determine bacteria. Here, in order to quantify the thickness dispersion degree, for example, the thickness dispersion normalized by setting the calculated thickness average to 100 is used.

An example of a bacteria determination method will be described with reference to FIG. FIG. 14 is a bar graph in which the vertical axis indicates the distribution of the normalized thickness, and the horizontal axis indicates the area number for identifying a plurality of areas calculated in the area extraction process in step S102. It is a figure which shows an example of the value which calculated dispersion | distribution, respectively. The dispersion of the thickness normalized for each region has a small value due to the characteristic that bacteria have a constant thickness except for the tip. Therefore, the variance of the normalized thickness is compared with a predetermined value B prepared in advance, and if it is equal to or less than the predetermined value B, it is determined as bacteria. If it is greater than the predetermined value B, it is determined that it is not a bacterium.

  As a method of determining the predetermined value B, for example, the variance of the normalized thickness is measured by performing steps S101 to S113 in advance for those other than bacteria and bacteria, and the average of the variance of thickness normalized for those other than bacteria and bacteria. A value is calculated, and an intermediate value of each average value is set as a predetermined value B.

  For the variance, for example, sample variance is used. When there are n pieces of data X1, X2,..., Xn, if Xa is the average of the data, and some data in the data is Xi (i is from 1 to n), the average of the squares of (Xa−Xi) Is the sample variance, and this relational expression is as follows. By making the data thickness using Equation 4, the variance of the thickness can be calculated.

  Next, an example of the display unit 104 will be described in detail.

  For example, a CRT monitor is used as the display unit 104. The display unit 104 displays the detection result output from the image processing unit 103. As the display unit 104, an arbitrary display unit 104 such as a touch panel may be adopted.

  Next, an example of operation | movement of the bacteria detection apparatus 100 is demonstrated.

  The optical means 101 observes the specimen coated on the plate 1 with fluorescence, and the imaging means 102 takes an image of the fluorescence. The image processing means 103 outputs the photographed image, and determines bacteria from the degree of thickness dispersion. Then, the display unit 104 displays the result.

  According to such a bacteria detecting apparatus 100, the shape is crushed in the vertical direction of the character “K” by repeatedly dividing the region by a straight line passing through the center of gravity 502 of the region and the contour point 701 having the shortest distance. Even in the case of the shape, the thickness can be measured, and the bacteria can be identified by analyzing the dispersion degree of the thickness.

  Note that, in the shortest contour point calculation step in step S105, the image processing unit 103 calculates a plurality of contour points 602 whose distances are within a predetermined range, instead of calculating the contour point 701 having the shortest distance to the center of gravity 502. Alternatively, the contour point 602 where the length of the line segment 1001 calculated in the line segment calculation step of step S109 is the shortest may be calculated.

  As a method for determining the predetermined range, for example, the distance between each contour point 602 and the center of gravity 502 is calculated, and a distance equal to or less than the intermediate value between the shortest distance and the average of the distances is calculated as the predetermined range.

Next, an example of a method for calculating the contour point 602 where the length of the line segment 1001 is the shortest will be described with reference to FIG. FIG. 15A is an explanatory diagram showing an example of a straight line 801 passing through each contour point 602 and the center of gravity 502 whose distance is within a predetermined range in the straight line calculation step of step S106. FIG. 15B is an explanatory diagram showing an example of a line segment 1001 that divides the region 501 with respect to the straight line 801 calculated as shown in FIG. 15A by the line segment calculation step in step S109. As shown in FIG. 15B, a line segment 1001 is calculated for each contour point 602 whose distance is within a predetermined range, and among the contour points 602 where the length of the line segment 1001 is the shortest, the distance is the shortest. Calculation is performed instead of the contour point 701.

  As described above, in the line segment calculation step in step S109, by adjusting the length of the calculated line segment 1001 to be the shortest, the thickness can be reduced by variation due to division, and accurate determination can be made. Therefore, it is more preferable to adjust the length of the line segment 1001 to be the shortest.

  Further, in the line segment calculation step in step S109, instead of ending when the newly calculated line segment 1101 intersects with one or both of the previously calculated line segment 1102 and line segment 1103, image processing is performed instead of ending. The means 103 may calculate a vertical line segment and divide it by using it instead of the newly calculated line segment 1101. As an example of a method for calculating a vertical line segment, first, a line segment that passes through the center of gravity of the divided region 1201 and is perpendicular to the line segment 1101 is calculated.

An example of a vertical line segment will be described with reference to FIG. FIG. 16A is an explanatory diagram showing an example of a divided area 1201 that is a part of the area 501 where the line segment 1101 of FIG. 11 intersects. FIG. 16B is an explanatory diagram showing an example of a line segment 1602 that passes through the center of gravity 1601 of the divided region 1201 and is perpendicular to the line segment 1101. Instead of ending the division when the line segment 1101 intersects with one or both of the previously calculated line segment 1102 and the line segment 1103 as shown in FIG. 16A, instead of the division area 1201 as shown in FIG. A line segment 1602 passing through the center of gravity 1601 and perpendicular to the line segment 1101 is calculated, and the process proceeds to step S110. In the case of FIG. 16, by calculating a vertical line segment 1602 instead of the line segment 1101 at a position where the division has been completed in the divided area 1201, the length of the vertical line segment 1602 can be obtained without ending the division. It can be calculated as the thickness.

  Thus, by using the vertical line segment 1602 in place of the newly calculated line segment 1101, it is possible to make an accurate determination by increasing the number of areas 501 to be measured. It is more preferable to consider the minute 1602.

  In addition, in the bacteria determination process in step S114, the distribution is analyzed for all the thicknesses calculated in step S113. By removing the maximum value and the minimum value of the thickness, the influence of errors in the thickness measurement. Therefore, it is more preferable to exclude the maximum value and the minimum value of the calculated thickness.

  Moreover, although the bacteria are determined from the degree of dispersion of the thickness, the calculated division number may be taken into consideration. Since the number of divisions mainly depends on the aspect ratio of the bacteria when not bent, for example, when the number of divisions is 1, the aspect ratio is from 1: 1 to 1: 2. In this case, for example, when it is known that the aspect ratio of the shape to be measured in advance is 1: 4, if the predetermined value C is 1, it can be determined that it is not a bacteria when the division number is equal to or less than the predetermined value C. On the contrary, if it is larger than the predetermined value C, it is determined that it is a bacterium.

  As described above, the aspect ratio of the shape is taken into consideration by considering the number of divisions when the aspect ratio of the shape to be measured is known in advance in the bacteria determined in step S114. Since the determination can be made accurately, it is more preferable to consider the calculated number of divisions.

The present invention has a function of measuring the thickness even in the case where the shape is crushed in the vertical direction of the character “ku”, an image recognition technique for measuring the thickness of a region, a bacteria discrimination device, a bacteria discrimination This is useful as a method.

The block diagram which shows an example of a structure of the bacteria detection apparatus in embodiment of this invention. The figure for demonstrating the fluorescence observation in the optical means in embodiment of this invention It is an example of the explanatory view of an image and a coordinate system in an embodiment of the present invention, (a) is a figure for explaining an example which expands the position of each pixel of an image to a two-dimensional coordinate system, (b) is attention It is a figure for demonstrating an example of the center position of a pixel, and a two-dimensional coordinate. The flowchart which shows an example of the bacteria detection method in embodiment of this invention The figure for demonstrating an example of the area | region and gravity center in embodiment of this invention The figure for demonstrating an example of the outline and outline point in embodiment of this invention The figure for demonstrating an example of the contour point from which the distance from the gravity center in embodiment of this invention becomes the shortest The figure for demonstrating an example of the straight line which passes along the outline point and gravity center in embodiment of this invention The figure for demonstrating an example of the intersection of the straight line and the outline in embodiment of this invention The figure for demonstrating an example of the line segment which divides | segments the area | region in embodiment of this invention The figure for demonstrating an example that the line segment in embodiment of this invention crosses The figure for demonstrating an example which divides | segments an area | region by the line segment in embodiment of this invention The flowchart which shows an example of the continuous division | segmentation determination process in embodiment of this invention. The figure for demonstrating an example of the bacteria determination method in embodiment of this invention The figure for demonstrating an example of the method in which the distance of the line segment in embodiment of this invention becomes the shortest The figure for demonstrating an example of the perpendicular | vertical line segment with respect to the line segment in embodiment of this invention

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Plate 100 Bacteria detection apparatus 101 Optical means 102 Imaging means 103 Image processing means 104 Display means 201 Light source 202 Light 203 from light source 201 Excitation filter 204 Excitation light 205 Dichroic mirror 206 Objective lens 207 Fluorescence 208 Absorption filter 209 Light 301 other than fluorescence 301 Center 501 Area 502 Centroid 601 Outline 602 Outline point 701 Contour point 801 having the shortest distance Straight line 901 Intersection 1001 Line segment 1101 Line segment 1102 Line segment 1103 Line segment 1201 Segment area 1601 Center of gravity 1602 of segment area 1201 Vertical segment

Claims (22)

An optical unit that irradiates light to fluoresce the specimen;
An imaging unit that captures an image including fluorescence from the specimen;
A region extraction unit for extracting a fluorescent region from the image;
A division unit that repeats division of the region until the area in the region is less than a predetermined area;
A thickness calculating unit that calculates the thickness of the specimen based on the length of each dividing line segment for dividing the region;
A bacteria determination unit that determines whether the specimen is bacteria based on the thickness, and
The dividing unit is
A centroid position calculator for calculating the position of the centroid of the region;
An outline calculating unit for calculating an outline of the region;
Among the contour points that form the contour, a target contour point calculation unit that calculates a target contour point whose distance from the position of the center of gravity is equal to or less than a predetermined distance;
A straight line calculation unit for calculating a straight line passing through the position of the center of gravity and the target contour point;
On the straight line, a line segment calculation unit that calculates one of the line segments connecting the two contour points as the divided line segment;
An area dividing unit that divides the area by the dividing line segments;
A bacteria discrimination apparatus having an area determination unit that determines whether an area in a divided region is less than the predetermined area. The bacteria discrimination device according to claim 1,
The division unit is a bacteria discrimination device that ends the division repetition when a division line segment calculated repeatedly intersects with another division line segment. The bacteria discrimination device according to claim 1 or 2,
The said discrimination | determination part is a bacteria discrimination | determination apparatus which complete | finishes the repetition of the said division | segmentation, when the division number which shows the frequency | count of repeating the said division | segmentation is larger than predetermined times. The bacteria discrimination device according to any one of claims 1 to 3,
The target contour point calculation unit is a bacteria discrimination apparatus that calculates, as the target contour point, the shortest contour point closest to the position of the center of gravity among the contour points. The bacteria discrimination device according to any one of claims 1 to 3,
The target contour point calculation unit calculates a plurality of contour points whose distance from the position of the center of gravity is equal to or less than a predetermined distance among the contour points, and determines the contour point having the shortest length of the dividing line segment. A bacteria discrimination device that calculates as a target contour point. The bacteria discrimination device according to any one of claims 1 to 5,
When the dividing line segment calculated repeatedly intersects with another dividing line segment, the line segment calculating unit passes a position perpendicular to the dividing line segment through the position of the center of gravity of the area where the dividing line segment is calculated. A bacteria discrimination device that substitutes for the dividing line segment. The bacteria discrimination device according to any one of claims 1 to 6,
The bacteria determination unit determines whether the sample is a bacterium based on the thickness, excluding the maximum value and the minimum value of the sample thickness calculated by the thickness calculation unit. Bacteria discrimination device. The bacteria discrimination device according to any one of claims 1 to 7,
The bacteria determination device, wherein the bacteria determination unit determines that the specimen is bacteria when the degree of dispersion of the thickness is a predetermined value or less. The bacteria discrimination device according to any one of claims 1 to 7,
The bacteria determination apparatus, wherein the bacteria determination unit determines that the specimen is other than bacteria when the thickness dispersion degree is greater than a predetermined value. The bacteria discrimination device according to any one of claims 1 to 7,
The bacteria determining unit determines that the specimen is bacteria when the degree of dispersion of the thickness is equal to or less than a predetermined value and the number of divisions indicating the number of repetitions of the division is greater than a predetermined number . The bacteria discrimination device according to any one of claims 1 to 7,
The bacteria determination unit determines that the specimen is other than bacteria when the degree of dispersion of the thickness is not more than a predetermined value and the number of divisions indicating the number of repetitions of the division is not more than a predetermined number of times. Discriminator. An optical step of irradiating light to fluoresce the specimen;
An imaging step of imaging an image containing fluorescence from the specimen;
A region extraction step of extracting a fluorescent region from the image;
A division step of repeating division of the region until the area in the region is less than a predetermined area;
A thickness calculating step of calculating the thickness of the specimen based on the length of each dividing line segment for dividing the region;
And a bacteria determination step for determining whether the specimen is bacteria based on the thickness,
The dividing step includes
A center-of-gravity position calculating step of calculating the position of the center of gravity of the area;
A contour calculating step for calculating a contour of the region;
Among the contour points forming the contour, a target contour point calculating step for calculating a target contour point whose distance from the position of the center of gravity is a predetermined distance or less;
A straight line calculating step of calculating a straight line passing through the position of the center of gravity and the target contour point;
On the straight line, a line segment calculating step for calculating one of the line segments connecting the two contour points as the divided line segment;
A region dividing step of dividing the region by the dividing line segments;
A bacteria discrimination method comprising an area determination step of determining whether an area in a divided region is less than the predetermined area. The method for distinguishing bacteria according to claim 12,
In the dividing step, when the dividing line segment calculated repeatedly intersects with another dividing line segment, the bacteria discrimination method for ending the division repetition. The method for distinguishing bacteria according to claim 12 or 13,
In the division step, when the number of divisions indicating the number of repetitions of the division is larger than a predetermined number, the bacteria discrimination method for ending the division repetition. The method for distinguishing bacteria according to any one of claims 12 to 14,
In the target contour point calculating step, a bacteria discrimination method of calculating, as the target contour point, the shortest contour point closest to the position of the center of gravity among the contour points. The method for distinguishing bacteria according to any one of claims 12 to 14,
In the target contour point calculating step, among the contour points, a plurality of contour points whose distance from the position of the center of gravity is equal to or less than a predetermined distance are calculated, and the contour points having the shortest segment length are Bacteria discrimination method to calculate as target contour points. The bacteria discrimination method according to any one of claims 12 to 16, comprising:
In the line segment calculation step, when a division line segment calculated repeatedly intersects with another division line segment, a line segment that passes through the position of the center of gravity of the area where the division line segment is calculated and is perpendicular to the division line segment The method for distinguishing bacteria as a substitute for the dividing line. The bacteria discrimination method according to any one of claims 12 to 17,
In the bacteria determination step, the maximum and minimum values of the thickness of the sample calculated by the thickness calculation unit are excluded, and it is determined whether or not the sample is bacteria based on the thickness. Bacteria discrimination method. The method for distinguishing bacteria according to any one of claims 12 to 18,
A bacteria discrimination method in which, in the bacteria determination step, the specimen is determined to be bacteria when the degree of thickness dispersion is a predetermined value or less. The method for distinguishing bacteria according to any one of claims 12 to 18,
A bacteria discrimination method in which, in the bacteria determination step, the specimen is determined to be other than bacteria when the degree of dispersion of the thickness is greater than a predetermined value. The method for distinguishing bacteria according to any one of claims 12 to 18,
In the bacteria determination step, if the degree of dispersion of the thickness is equal to or smaller than a predetermined value and the number of divisions indicating the number of times of repeating the division is larger than the predetermined number, the bacteria determination method of determining that the specimen is bacteria . The method for distinguishing bacteria according to any one of claims 12 to 18,
In the bacteria determination step, if the degree of dispersion of the thickness is equal to or less than a predetermined value and the number of divisions indicating the number of repetitions of the division is equal to or less than the predetermined number, the bacteria that determine that the specimen is other than bacteria How to determine.

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