WO2013102949A1 - Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire - Google Patents

Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire Download PDF

Info

Publication number
WO2013102949A1
WO2013102949A1 PCT/JP2012/000016 JP2012000016W WO2013102949A1 WO 2013102949 A1 WO2013102949 A1 WO 2013102949A1 JP 2012000016 W JP2012000016 W JP 2012000016W WO 2013102949 A1 WO2013102949 A1 WO 2013102949A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
histogram
threshold
line
peak value
Prior art date
Application number
PCT/JP2012/000016
Other languages
English (en)
Japanese (ja)
Inventor
公太郎 岡田
和昭 中根
成昭 松浦
鈴木　貴
Original Assignee
株式会社知能情報システム
国立大学法人大阪大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社知能情報システム, 国立大学法人大阪大学 filed Critical 株式会社知能情報システム
Priority to JP2013552329A priority Critical patent/JP5762571B2/ja
Priority to PCT/JP2012/000016 priority patent/WO2013102949A1/fr
Publication of WO2013102949A1 publication Critical patent/WO2013102949A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • the present invention relates to an image processing method for obtaining a binarized image by performing binarization processing on a biological tissue image.
  • the present invention also relates to an image processing apparatus that executes the image processing method, an image processing program for operating the image processing method on a computer, and a recording medium on which the image processing program is recorded.
  • Patent Documents 1 to 3 can be cited as conventional examples of such threshold setting methods.
  • a threshold value for binarization processing is calculated from the maximum value and the minimum value of each density signal in the red, green, and blue wavelength regions.
  • the threshold value is obtained from the peak of the density histogram of the cell image.
  • a binarized image is obtained while adjusting a threshold value to a biological tissue image.
  • the biological tissue image is obtained by imaging the stained biological tissue with an imaging device through a microscope. For this reason, variations are likely to occur depending on various conditions such as staining conditions for living tissue or imaging conditions for imaging living tissue. Therefore, from the method of calculating a threshold for binarization processing from the maximum value and minimum value of the density signal in the wavelength region of each color as in Patent Document 1, or from the peak of the density histogram of the cell image as in Patent Document 2.
  • the method of obtaining the threshold value it is inevitable that the threshold value will be different due to variations in the tissue image, and the objectivity is ensured by using the binarized image obtained by image processing using the threshold value.
  • accurate pathological diagnosis is difficult.
  • the present invention was made for the purpose of solving the problems of the conventional image processing method as described above, and is not affected by variations in staining conditions of living tissue or imaging conditions of living tissue, It is an object of the present invention to provide an image processing method capable of obtaining an optimum threshold value for binarization processing appropriately and quickly, and thus greatly contributing to improvement and speed of objectivity of pathological diagnosis.
  • the image processing method plots a plurality of predetermined points selected from a biological tissue image on a two-dimensional field composed of XY reference axes of two different colors based on the color density of each predetermined point, A step of obtaining a scatter diagram of a predetermined point, a regression line of the predetermined point in the scatter diagram is calculated, and a large number of virtual points oriented in a direction orthogonal to the regression line at equal intervals on the regression line From the step of setting a straight line, setting a number of regions partitioned by these virtual straight lines on the two-dimensional field, obtaining a histogram indicating the number of each predetermined point included in each region, A step of obtaining a first peak value (Hp) that is a distribution peak value, and a differentiation line of the histogram are differentiated to calculate a histogram differentiation first peak value (Dp) that is an inflection point of the distribution line.
  • Hp first peak value
  • Dp histogram differentiation first peak value
  • the first peak value (Hp) in the present invention means a peak value appearing at a position closest to the origin in the histogram.
  • the histogram differential first peak value (Dp) is a peak value that appears at the closest position from the origin in the primary differential curve. In short, the distribution line from the origin to the first peak value (Hp) is changed. It means a music point.
  • a plurality of predetermined points selected from a biological tissue image are plotted on a two-dimensional field composed of XY reference axes of two different colors based on the color density of each predetermined point” If only the aspect of plotting the blue and red luminances of a plurality of pixel points arbitrarily selected from a biological tissue image on a two-dimensional field with the blue luminance on the X axis and the red luminance on the Y axis It is a concept including a mode in which all pixel points included in the biological tissue image are selected and the luminance of all the pixel points is plotted on a two-dimensional field.
  • the threshold straight line in the present invention is obtained by substituting the threshold obtained from the first peak value (Hp) and the histogram differential first peak value (Dp) as the value of the X coordinate and substituting the X coordinate into the equation of the regression line.
  • This is a straight line that passes through a point on the two-dimensional plot (point on the regression line) with the Y coordinate value obtained as the Y coordinate value, and is orthogonal to the regression line.
  • the binarization process using the threshold line means a process of binarizing depending on which of the two-dimensional plot regions each predetermined point is divided by the threshold line.
  • the variable is preferably set to 0.6.
  • the image processing apparatus plots a plurality of predetermined points selected from a biological tissue image on a two-dimensional field composed of XY reference axes of two different colors based on the luminance of the color of each predetermined point.
  • a scatter diagram creating means for creating a scatter diagram of a predetermined point; a regression line calculating means for calculating a regression line of a predetermined point in the scatter diagram; and orthogonally spaced relative to the regression line at equidistant positions on the regression line
  • a histogram creating means for creating a histogram, a differentiation means for differentiating the histogram distribution line obtained by the histogram creating means to obtain a primary differential curve, and the histogram distribution
  • Threshold value calculation means threshold value straight line calculation means for obtaining a threshold straight line orthogonal to the regression line with the threshold value obtained by the threshold value calculation means as an intersection, and binarization for a biological tissue image using the threshold value straight line Binarization processing means for performing processing to obtain a binarized image.
  • ⁇ Formula> (Hp ⁇ Dp) ⁇ variable + Dp threshold variable: a value (0.5 to 0.9) determined in advance according to the biological tissue image to be a pathological diagnosis target
  • the biological tissue image is a pathological diagnosis image for determining whether or not an image of a cancer tissue is included, it is desirable to set the variable to 0.6.
  • the present invention is an image processing program for operating the image processing apparatus, and is an image processing program for causing a computer to function as each means.
  • the present invention is a computer-readable recording medium on which the image processing program is recorded.
  • the first peak value (Hp) and the histogram differential first peak value (Dp), which are distribution peak values, are obtained from the regression line of the predetermined points in the scatter diagram and the histogram indicating the distribution status of the predetermined points. calculate.
  • a threshold for binarization processing is calculated from the first peak value (Hp) and the histogram differential first peak value (Dp), a threshold straight line is calculated using the threshold, and the threshold straight line is used. Then, binarization processing is performed on the biological tissue image to obtain a binarized image.
  • a threshold line for binarization processing is obtained using a histogram in which the predetermined points are counted in a direction orthogonal to the regression line, the position related to the distance from the origin of the conventional XY reference axis is obtained.
  • influences such as variations in staining conditions of living tissue or variations in imaging conditions of living tissue can be eliminated. Therefore, according to the present invention, it is possible to obtain an optimal threshold line for binarization processing without being affected by variations in staining conditions or imaging conditions, thereby improving and speeding up the objectiveness of pathological diagnosis. You can contribute a lot.
  • the same biological tissue is stained with the same staining time, and even when imaging is performed under the same imaging conditions, the final biological body obtained if the color condition of the staining agent is different. Tissue images will be different. That is, when a reddish stain is used, the biological tissue image is also reddish, and when a bluish stain is used, the biological tissue image is also bluish.
  • 20 (a) and 20 (b) all pixel points included in these two types of biological tissue images are determined from the red and blue XY reference axes based on the luminance (red and blue color intensities) of each pixel point.
  • a scatter diagram of pixel points obtained by plotting on a two-dimensional field is schematically shown.
  • FIG. 20A shows a scatter diagram when using a reddish stain
  • FIG. 20B shows a scatter diagram when using a bluish stain. That is, when a reddish stain is used, as shown in FIG. 20A, each pixel point has a high red color intensity and a low blue color intensity. On the other hand, when a bluish stain is used, each pixel point has a high blue color intensity and a low red color intensity, as shown in FIG.
  • FIG. 21 (a) is a histogram showing the frequency of the X-axis values created from the scatter diagrams of FIGS. 20 (a) and 20 (b).
  • FIG. 21B is a histogram created from the scatter diagrams of FIGS. 20A and 20B and counting each pixel point in a direction orthogonal to the regression line. Note that the histogram in which each pixel point is counted in a direction orthogonal to the regression line referred to here is, specifically, a large number of pixels that are oriented at equal intervals on the regression line in a direction orthogonal to the regression line. Is created by counting the number of pixels belonging to the area defined by the virtual straight line.
  • hatched areas indicate areas that should be employed in the binarization process. That is, if a binarization process is performed using a threshold straight line in which the hatched area is adopted, finally, the same binarized image can be obtained regardless of the color condition of the staining agent.
  • the “threshold value” in FIGS. 21A and 21B indicates the optimum threshold value for the binarization process.
  • the shapes of the two distribution curves are completely different. That is, in the histogram of FIG. 21A, the shapes of the two distribution lines are completely different due to the influence of the staining condition called the color condition of the staining agent. For this reason, it is impossible to obtain a uniquely optimum threshold value for binarization processing from these two distribution lines. That is, in the histogram of FIG. 21A, no correlation can be found between various values such as the first peak values of these two distribution lines and the optimum threshold value. As described above, it is impossible to calculate the optimum threshold using the histogram indicating the frequency of the X-axis value.
  • the position of two distribution lines position in the X-axis direction
  • the peak height height in the Y-axis direction
  • the distribution line width width in the X-axis direction
  • the shapes of the two distribution lines are substantially the same.
  • the optimum threshold position the position in the X-axis direction
  • the entire width in the X-axis direction of the distribution line is the same in the two distribution lines. For this reason, it is possible to uniquely obtain the same threshold value for the optimum binarization process from these two distribution lines. That is, in the histogram of FIG. 21B, there is a correlation between these two distribution lines and the optimum threshold value, and therefore it is possible to uniquely obtain the optimum threshold value based on the distribution line. It is.
  • the inventors of the present invention compared the optimum threshold value with the distribution line, and as a result of intensive studies on the correlation, the first peak value (Hp) of the distribution line and the histogram that is the inflection point of the distribution line. It was found that an optimum threshold value can be calculated from the differential first peak value (Dp).
  • the optimum threshold value always takes a value smaller than the first peak value (Hp), and the optimum threshold value always takes a value larger than the histogram differential first peak value (Dp).
  • Hp first peak value
  • Dp histogram differential first peak value
  • the “variable” used in the threshold value calculation method as described above differs depending on the biological tissue image to be subjected to pathological diagnosis.
  • the variable can be set to 0.6. preferable. This is because when the binarization process is performed using the threshold value obtained by adopting the variable (0.6) to create a binarized image, the peripheral edge of the cell nucleus is clearly displayed. Based on the rule of thumb.
  • the image processing method can be provided in the form of program data (image processing program) that runs on a computer, and can also be provided in the form of a recording medium on which the image processing program is recorded.
  • FIG. 1 is a schematic diagram illustrating a configuration of an image processing apparatus according to the present invention.
  • 3 is a flowchart illustrating an image processing method according to the present invention. It is a figure which shows an example of a biological tissue image. This is a binarized image of the biological tissue image, which is obtained by performing binarization processing with an optimal threshold line. It is the binarized image of the biological tissue image, and is obtained by performing binarization processing with the first peak value (Hp). It is the binarized image of the biological tissue image, and is obtained by performing binarization processing with the histogram differential first peak value (Dp).
  • Hp first peak value
  • Dp histogram differential first peak value
  • FIG. 6 is a scatter diagram of predetermined points obtained by plotting a plurality of predetermined points selected from a biological tissue image on a two-dimensional field composed of different two-color reference axes based on the luminance of the color of each predetermined point.
  • a scatter diagram is created by plotting all pixel points constituting a living tissue image on a two-dimensional field. It is the histogram which counted the number of each predetermined point in the direction orthogonal to the regression line of each predetermined point. It is a primary differential curve obtained by differentiating the distribution line of the histogram of FIG.
  • (A) to (c) are obtained by plotting a plurality of predetermined points selected from a biological tissue image on a two-dimensional field consisting of different two-color reference axes based on the luminance of the color of each predetermined point. It is the figure which showed typically the scatter diagram of the predetermined point which was done.
  • (A) to (c) are scatter diagrams of biological tissue images obtained by coloring the same biological tissue using colorants having different color conditions, and (a) is a diagram in (b). In comparison, a scatter diagram of a biological tissue image colored with a reddish colorant, and (c) shows a scatter diagram of a biological tissue image colored with a bluish colorant compared to (b). ing.
  • FIG. 11 is a first-order differential curve obtained by differentiating the histogram distribution lines in FIG. 11.
  • the two-dot chain line corresponds to FIG. 10A
  • the solid line corresponds to FIG. 10B
  • the broken line corresponds to FIG. is doing.
  • (A) to (c) are histograms for explaining the binarization processing using the threshold value, and the shaded area indicates the pixel region used in the binarization processing.
  • a scatter diagram of predetermined points obtained by plotting a plurality of predetermined points selected from a biological tissue image on a two-dimensional field consisting of different two-color reference axes based on the luminance of the color of each predetermined point This is schematically shown.
  • the figure shows a scatter diagram created from biological tissue images captured using three different types of imaging devices for biological tissues stained under the same staining conditions for the same biological tissue. Yes. It is a histogram in which the number of each predetermined point is counted in a direction orthogonal to the regression line of each predetermined point, (a) corresponds to a scatter diagram specified by a regression line indicated by a two-dot chain line in FIG.
  • (b) 14 corresponds to the scatter diagram specified by the regression line indicated by the solid line in FIG. 14, and (c) corresponds to the scatter diagram specified by the regression line indicated by the broken line in FIG. 14.
  • (A) to (c) are scatter diagrams of biological tissue images captured under the same coloring conditions and different exposure conditions (imaging conditions) with respect to the same biological tissue. When the exposure is less than that in (b), (c) shows the case where there is more exposure than in (b).
  • FIG. 17 is a first-order differential curve obtained by differentiating the histogram distribution line in FIG. 17, the two-dot chain line corresponds to FIG. 16A, the solid line corresponds to FIG. 16B, and the broken line in FIG. This corresponds to c).
  • (A) to (c) are histograms for explaining the binarization processing using the threshold value, and the shaded area indicates the pixel region used in the binarization processing.
  • FIG. 5B is a scatter diagram of a biological tissue image colored using a coloring method
  • FIG. 5B is a scatter diagram of a biological tissue image colored using a bluish colorant.
  • (A) is a histogram showing the distribution of predetermined points according to the distance from the origin of the XY reference axis of each predetermined point, and (b) shows the number of each predetermined point included in each region defined by a virtual straight line. It is a histogram to show.
  • the present invention is an image for binarizing a biological tissue image that is performed as a pre-processing of image analysis when performing pathological diagnosis for determining whether or not cancer cells or the like are included in the biological tissue by image analysis. Intended for processing methods.
  • a biological tissue image is obtained by imaging a stained biological tissue with a microscope, but the biological tissue image often varies depending on various conditions such as staining conditions of the biological tissue or imaging conditions. Is as described above.
  • FIGS. 10A to 10C show scatter diagrams created from biological tissue images obtained using three kinds of stains having different hues. That is, FIGS. 10A to 10C show a plurality of predetermined points selected from the biological tissue image (here, all pixel points constituting the biological tissue image) based on the color density of each predetermined point.
  • 10A and 10C are scatter diagrams of predetermined points obtained by plotting on a two-dimensional field composed of a blue and XY reference axes.
  • pixels corresponding to the predetermined points are represented by R, G, and B.
  • Each predetermined point is plotted on a two-dimensional field based on the color components of R (red: vertical axis) and B (blue: horizontal axis).
  • FIGS. 10 (a) to 10 (c) a red or blue hue is changed up and down from one biological tissue image to create a total of three biological tissue images, and these are used as dyes having different hues.
  • the image is a biological tissue image obtained by staining.
  • FIG. 10A is a scatter diagram created based on sample image data obtained by raising the red hue from the biological tissue image as the original data according to FIG. 10B. It is assumed that the entire biological tissue is stained with a reddish stain rather than the form shown in FIG. 10 (b).
  • FIG. 10C is a scatter diagram created based on the sample image data obtained by raising the blue hue from the biological tissue image which is the original data according to FIG. 10B, and FIG.
  • each pixel point has a strong red color intensity and a low blue color intensity.
  • each pixel point has a high blue color intensity and a low red color intensity.
  • a histogram indicating the number of each predetermined point included in each area defined by the virtual straight line is created. Specifically, as shown in FIGS. 10 (a) to 10 (c), after calculating a regression line at a predetermined point in the scatter diagram, it is orthogonal to the regression line at equal intervals on the regression line. A large number of virtual straight lines oriented in the direction are set, and the partitioned area is set on the two-dimensional field by the virtual straight lines. Next, a histogram is created based on the affiliation status of each predetermined point with respect to the region.
  • FIG. 11 shows a histogram.
  • the two-dot chain line in FIG. 11 corresponds to the distribution diagram in FIG. 10A
  • the solid line corresponds to the distribution diagram in FIG. 10B
  • the broken line in FIG. Corresponds to the distribution map.
  • Hp indicates the first peak value of the histogram.
  • FIG. 12 shows such a first derivative curve
  • the two-dot chain line corresponds to the distribution diagram of FIG. 10 (a)
  • the solid line corresponds to the distribution diagram of FIG. 10 (b)
  • the broken line represents FIG. 10 (c).
  • Dp in FIG. 12 shows a histogram differential first peak value.
  • the left and right histogram shift distances (d1 ′ and d2 ′) with respect to the first-order differential curve located at the center of FIG. 12 are the left and right histogram shift distances (d1 ′ and d2) with respect to the histogram located at the center of FIG. It is noted that it is completely consistent with ′).
  • the optimum threshold value is calculated by substituting the first peak value (Hp) and the histogram differential first peak value (Dp) into the above formula (variable: 0.6). That is, three threshold values corresponding to the respective histograms in FIGS. 10A to 10C are calculated.
  • the threshold value obtained from the first peak value (Hp) and the histogram differential first peak value (Dp) is set as the value of the X coordinate, and the value of the Y coordinate obtained by substituting the X coordinate into the regression line equation
  • a threshold straight line that passes through a point on the two-dimensional plot (a point on the regression line) with Y as the value of the Y coordinate and is orthogonal to the regression line is calculated.
  • binarization processing is performed using a threshold line. Specifically, the binarization process is performed depending on which of the two-dimensional plot regions each predetermined point is divided by the threshold straight line.
  • FIGS. 13A to 13C indicate areas that are employed in the binarization process using these threshold lines.
  • FIGS. 13A to 13C it can be seen that the same area region can be extracted by binarization using a threshold line in any histogram. That is, by using the image processing method according to the present invention, it is possible to always obtain the same binarized image without being affected by the staining condition such as the color condition of the staining agent. This means that the binarization accuracy is remarkably improved, and therefore pathological diagnosis can be executed more accurately. In addition, human error intervention can be suppressed and accurate pathological diagnosis can be performed. It is also excellent in that an optimum binarized image can be obtained more quickly.
  • an image processing-dedicated IC called an image engine performs various image processing such as color space conversion, gamma correction, and noise reduction on data output from an imaging device. For this reason, it is widely known that the tendency of image quality differs when the manufacturer or type of camera is different. In other words, even when the biological tissue is stained under the same staining condition, if the imaging condition of the type of the imaging device is different, it is inevitable that the finally obtained biological tissue image will be different.
  • FIG. 14 is a diagram conceptually showing a scatter diagram of three biological tissue images obtained by imaging using three different types of imaging devices.
  • the scatter diagram specified by the regression line shown by the solid line in FIG. 14 is used as the original data, and the scatter diagram related to the original data is shifted in the direction in which red is emphasized, A specified scatter diagram is obtained, and this is a scatter diagram of a biological tissue image captured using a camera that tends to emphasize red.
  • the scatter diagram specified by the regression line indicated by the two-dot chain line is obtained, and this tends to reduce the red color.
  • It is a scatter diagram of a biological tissue image captured using a camera. In FIG. 14, these three scatter diagrams are shown on the same two-dimensional field (vertical axis: red, horizontal axis: blue) for comparison.
  • a histogram indicating the distribution status of the predetermined points is created. Specifically, after calculating a regression line of a predetermined point in the scatter diagram, a large number of virtual lines directed in a direction orthogonal to the regression line are set at equal intervals on the regression line, A region partitioned by the virtual straight line is set on the two-dimensional field. Next, a histogram indicating the distribution status of the predetermined points is created based on the affiliation status of each predetermined point with respect to the region.
  • FIG. 15 shows a histogram, and the histogram of FIG. 15A corresponds to the scatter diagram specified by the regression line shown by the two-dot chain line of FIG. 14, and the histogram of FIG.
  • a first derivative curve is created by differentiating the distribution lines of each histogram to obtain a histogram derivative first peak value (Dp).
  • the optimum threshold value is calculated by substituting the first peak value (Hp) and the histogram differential first peak value (Dp) into the above formula (variable: 0.6). That is, three threshold values corresponding to the histograms of FIGS. 15A to 15C are calculated.
  • the threshold value obtained from the first peak value (Hp) and the histogram differential first peak value (Dp) is set as the value of the X coordinate, and the value of the Y coordinate obtained by substituting the X coordinate into the regression line equation
  • a threshold straight line that passes through a point on the two-dimensional plot (a point on the regression line) with Y as the value of the Y coordinate and is orthogonal to the regression line is calculated.
  • binarization processing is performed using a threshold line. Specifically, the binarization process is performed depending on which of the two-dimensional plot regions each predetermined point is divided by the threshold straight line.
  • FIGS. 15A to 15C indicate areas that are employed in the binarization process using these threshold lines.
  • FIGS. 15A to 15C it can be seen that the same area region can be extracted by binarization processing using a threshold straight line in any histogram. That is, by using the image processing method according to the present invention, it is possible to always obtain the same binarized image without being affected by imaging conditions such as image quality directivity variations caused by using different imaging devices. . This means that the binarization accuracy is remarkably improved, and therefore pathological diagnosis can be executed more accurately. In addition, human error intervention can be suppressed and accurate pathological diagnosis can be performed. It is also excellent in that an optimum binarized image can be obtained more quickly.
  • FIGS. 16 (a) to 16 (c) show the distribution of biological tissue images imaged under three different exposure conditions using the same imaging device for one biological tissue stained under the same staining conditions. It is a figure which shows a figure notionally.
  • the biological tissue image that is the original data of the scatter diagram in FIG. 16A is an image that is captured with a smaller exposure than the biological tissue image that is the original data of the scatter diagram in FIG.
  • the biological tissue image that is the original data of the scatter diagram of FIG. 16C is captured with a larger exposure than the biological tissue image that is the original data of the scatter diagram of FIG. That is, when the exposure is reduced, each pixel point constituting the biological tissue image moves in a direction approaching the origin, and when the exposure is opened, each pixel point moves in a direction away from the origin.
  • the two-dot chain line in Fig. 16 (a), the solid line in Fig. 16 (b), and the broken line in Fig. 16 (c) indicate the regression line of each scatter diagram. As is apparent from the regression lines in FIGS. 16A to 16C, the regression lines are the same even for biological tissue images captured under different exposure conditions.
  • a histogram is created for each of the three scatter diagrams shown in FIG. Specifically, after calculating a regression line of a predetermined point in the scatter diagram, a large number of virtual lines directed in a direction orthogonal to the regression line are set at equal intervals on the regression line, An area partitioned by the virtual straight line is set on the two-dimensional field. Next, a histogram indicating the distribution status of the predetermined points is created based on the affiliation status of each predetermined point with respect to the region.
  • FIG. 17 shows the histogram created as described above.
  • the histogram indicated by the two-dot chain line in FIG. 17 corresponds to the scatter diagram of FIG. 16A and is indicated by the solid line in FIG.
  • the histogram indicated by a broken line in FIG. 17 corresponds to the scatter diagram of FIG. 16 (c).
  • the first peak value (Hp) of each histogram can be obtained.
  • the distribution line of each histogram is differentiated to create a primary differential curve, and a histogram differential first peak value (Dp) is obtained.
  • 18 corresponds to the scatter diagram of FIG. 16 (a)
  • the primary differential curve shown by the solid line in FIG. 18 corresponds to the scatter diagram of FIG. 16 (b).
  • a primary differential curve indicated by a broken line in FIG. 18 corresponds to the scatter diagram in FIG.
  • the histogram differential first peak value (Dp) of each histogram can be obtained.
  • the optimum threshold value is calculated by substituting the first peak value (Hp) and the histogram differential first peak value (Dp) into the above formula (variable: 0.6). That is, three threshold values corresponding to each histogram in FIG. 17 are calculated.
  • the threshold value obtained from the first peak value (Hp) and the histogram differential first peak value (Dp) is set as the value of the X coordinate, and the value of the Y coordinate obtained by substituting the X coordinate into the regression line equation
  • a threshold straight line that passes through a point on the two-dimensional plot (a point on the regression line) with Y as the value of the Y coordinate and is orthogonal to the regression line is calculated.
  • binarization processing is performed using a threshold line. Specifically, the binarization process is performed depending on which of the two-dimensional plot regions each predetermined point is divided by the threshold straight line.
  • the hatched area indicates an area that is employed in the binarization process using these threshold lines.
  • FIGS. 19A to 19C in any of the histograms, it is understood that an area region having the same ratio with respect to the whole can be extracted by the binarization process using the threshold line. That is, if the image processing method according to the present invention is used, the same binarized image can always be obtained without being affected by the imaging condition such as the exposure condition. This means that the binarization accuracy is remarkably improved, and therefore pathological diagnosis can be executed more accurately. In addition, human error intervention can be suppressed and accurate pathological diagnosis can be performed. It is also excellent in that an optimum binarized image can be obtained more quickly.
  • an image processing apparatus 1 includes an image acquisition unit 2, a storage unit 3, an image processing unit 4, a display control unit 5, a display unit 6, and a primary storage unit 7.
  • the image processing apparatus 1 may be one obtained by installing a dedicated image processing program on a commercially available personal computer.
  • the image acquisition unit 2 acquires a biological tissue image obtained by imaging a biological tissue from an external device (for example, an imaging device), and causes the storage unit 3 to store the biological tissue image.
  • an external device for example, an imaging device
  • Specific examples of the image acquisition unit 2 include a capture card that directly receives a biological tissue image captured by an imaging device, a card reader that reads information from a memory card, or a reader device that reads information from a USB memory. it can.
  • the method for staining biological tissue in the present invention is not particularly limited, and examples thereof include HE (Hematoxilin-Eosin) staining.
  • HE staining cell nuclei and cytoplasm are stained, and an overall picture of cells and cell structures can be grasped.
  • FIG. 3 shows a biological tissue image obtained by HE staining.
  • the storage unit 3 is a non-volatile storage device such as a hard disk or a flash memory.
  • a scatter diagram and a regression line created by the image processing unit 4 are used.
  • Various data such as a region partitioned by virtual straight lines, a histogram, a primary differential curve, a first peak value (Hp), and a histogram differential first peak value (Dp) are stored.
  • the storage unit 3 stores an OS program in addition to various control programs for controlling the image acquisition unit 2, the display control unit 5, and the like. Further, an application program (image processing program) for executing the image processing method according to the present invention is installed and stored in advance.
  • a ROM Read Only Memory
  • the image processing unit 4 is a central processing unit called a CPU (Central Processing Unit), and a primary storage unit 7 which is a RAM (Random Access Memory) based on an application program stored in the storage unit 3. Image processing is executed as a work area. Specifically, the image processing unit 4 is based on an application program, a scatter diagram creation unit (scatter diagram creation unit), a regression line calculation unit (regression line calculation unit), a region setting unit (region setting unit), and a histogram creation Section (histogram creation means), differentiation section (differentiation means), peak value calculation section (peak value calculation means), threshold calculation section (threshold calculation means), threshold straight line calculation section (threshold straight line calculation means), and binarization processing Part (binarization processing means). Specific operations of the image processing unit 4 will be described later.
  • a scatter diagram creation unit scatter diagram creation unit
  • regression line calculation unit regression line calculation unit
  • region setting unit region setting unit
  • histogram creation Section histogram creation Section
  • differentiation section differentiate section
  • peak value calculation section peak
  • the display control unit 5 controls the display unit 6 to display the processing result by the image processing unit 4 on the display unit 6, and is a graphic chip or a graphic card equipped with a graphic chip.
  • the display unit 6 is a liquid crystal display device, for example.
  • the image processing unit 4 functions as a scatter diagram creation unit as a scatter diagram. (See FIG. 7) is created (S2).
  • a scatter diagram may be created from the biological tissue image stored in advance in the storage unit 3 (S2).
  • the image processing unit 4 plots a plurality of predetermined points selected from the biological tissue image on a two-dimensional field composed of different two-color reference axes based on the color density of each predetermined point.
  • a scatter diagram as shown in FIG. 7 is created.
  • a scatter diagram is created using a two-dimensional field in which the horizontal axis (X axis) is blue and the vertical axis (Y axis) is red.
  • tens of thousands of pixels corresponding to each predetermined point included in the biological tissue image are decomposed into R, G, and B color components, and each predetermined point is determined based on the R (red) and B (blue) color components.
  • the image processing unit 4 displays the obtained scatter diagram on the display unit 6 via the display control unit 5.
  • the image processing unit 4 attaches a file name related to the biological tissue image to the scatter diagram and stores it in the storage unit 3.
  • the image processing unit 4 functions as a regression line calculation unit, and calculates a regression line by the least square method. That is, a regression line is calculated based on all the plots on the scatter diagram or arbitrary predetermined points extracted from the scatter diagram (S3).
  • the image processing unit displays the obtained regression line on the display unit 6 via the display control unit 5. Further, the image processing unit 4 attaches a file name related to the biological tissue image to the regression line and stores it in the storage unit 3.
  • the image processing unit 4 functions as a region setting unit, and sets the two-dimensional field to a large number of regions (S4). Specifically, the image processing unit 4 sets a large number of virtual straight lines oriented in a direction orthogonal to the regression line at equal intervals on the regression line, and defines areas partitioned by the virtual line. Set on a two-dimensional field. In FIG. 7, a form in which several virtual straight lines are drawn is shown, but actually, as described above, as shown in FIGS. 16 (a) to (c), A large number of virtual straight lines are drawn at equally spaced positions.
  • the image processing unit 4 displays the obtained virtual straight line and region on the display unit 6 via the display control unit 5. Further, the image processing unit 4 stores the file name related to the biological tissue image in the storage unit 3 in comparison with the virtual straight line and the region.
  • the image processing unit 4 functions as a histogram creation unit, and creates a histogram (see FIG. 8) indicating the number of predetermined points included in the region of each predetermined point (S5).
  • the image processing unit 4 displays the obtained histogram on the display unit 6 via the display control unit 5. Further, the image processing unit 4 attaches a file name associated with the biological tissue image to the histogram and stores it in the storage unit 3.
  • the image processing unit 4 functions as a peak value calculation unit and obtains the first peak value (Hp) from the previous histogram (S6).
  • the first peak value (Hp) here indicates the first peak value appearing at the closest position from the origin in the distribution line of the histogram. However, in the example of the histogram shown in FIG. 8, only one peak appears.
  • the image processing unit 4 displays the obtained first peak value (Hp) on the display unit 6 via the display control unit 5.
  • the image processing unit 4 attaches a name associated with the biological tissue image to the first peak value (Hp) and stores it in the storage unit 3.
  • the image processing unit 4 functions as a differentiation unit and performs a differentiation process on the histogram distribution line obtained in the previous S6 to obtain a primary differential curve (S7).
  • FIG. 9 shows a first-order differential curve obtained by such differential processing.
  • the image processing unit 4 displays the obtained primary differential curve on the display unit 6 via the display control unit 5. Further, the image processing unit 4 attaches a file name related to the biological tissue image to the first-order differential curve and stores the file name in the storage unit 3.
  • the image processing unit 4 functions as a peak value calculation unit, and obtains a histogram differential first peak value (Dp) from the previous primary differential curve (S8).
  • the histogram differential first peak value (Dp) here refers to the first peak value appearing at the closest position from the origin in the primary differential curve. That is, the histogram differential first peak value (Dp) is an inflection point of the distribution line of the previous histogram from the origin to the first peak value (Hp).
  • the image processing unit 4 displays the obtained histogram differential first peak value (Dp) on the display unit 6 via the display control unit 5. Further, the image processing unit 4 attaches a name associated with the biological tissue image to the histogram differential first peak value (Dp) and stores it in the storage unit 3.
  • the image processing unit 4 functions as a threshold value calculation unit, and calculates the first peak value (Hp) obtained in the previous S6 and the histogram differential first peak value (Dp) obtained in S8 by the mathematical expression.
  • the variables included in the mathematical formula are different depending on the biological tissue image to be pathologically diagnosed.
  • the biological tissue image includes an image of cancer tissue.
  • the variable is set to 0.6.
  • the selection of the variable is automatically or manually selected when the pathological diagnosis target is determined on the program of the image processing apparatus.
  • the image processing unit 4 displays the obtained threshold value on the display unit 6 via the display control unit 5.
  • the image processing unit 4 assigns a name associated with the biological tissue image to the threshold value and stores it in the storage unit 3.
  • the image processing unit 4 functions as a threshold value calculation unit, and calculates a threshold line from the threshold value obtained in S9 (S10). Specifically, a point on the two-dimensional plot (on the regression line) with the previous threshold as the value of the X coordinate and the value of the Y coordinate obtained by substituting the X coordinate in the regression line equation as the value of the Y coordinate. ), And a threshold straight line orthogonal to the regression line is calculated.
  • the image processing unit 4 displays the obtained threshold straight line on the display unit 6 via the display control unit 5.
  • the image processing unit 4 attaches a name associated with the biological tissue image to the threshold line and stores the name in the storage unit 3.
  • the image processing unit 4 functions as a binarization processing unit, performs binarization processing on the biological tissue image using the threshold line obtained in S10, and obtains a binarized image (S11). ).
  • the image processing unit 4 displays the obtained binarized image on the display unit 6 via the display control unit 5. Further, the image processing unit 4 attaches a file name related to the biological tissue image to the binarized image and stores it in the storage unit 3.
  • FIG. 4 is a binarized image obtained in S11. 5 is a binarized image obtained by binarization processing using the first peak value (Hp), and FIG. 6 is binarization using the histogram differential first peak value (Dp). It is the binarized image obtained by processing.
  • black portions are pixels used in the binarization process. 4 to 6, the binarized image obtained by the image processing method according to the present embodiment is positioned between the first peak value (Hp) and the histogram differential first peak value (Dp). It can be seen that it was obtained using the threshold value. That is, since the black part in FIG. 4 is less than the black part shown in FIG. 5 and more than the black part shown in FIG. 6, the binarized image obtained by the image processing method according to the present embodiment is the first. It can be seen that it was obtained using a threshold value located between the peak value (Hp) and the histogram differential first peak value (Dp).
  • the present inventors have confirmed that the use of the binarized image obtained by the image processing method according to the present embodiment as described above enables pathological diagnosis more accurately than in the past. That is, in this type of pathological diagnosis, the presence or absence of cancer cells is confirmed based on the number of spaces surrounded by the outer edge of the cancer tissue, but the binarized image obtained by the image processing method according to the present embodiment is used. It has been confirmed that the presence or absence of cancer cells can be determined with high accuracy. As described above, the image processing method according to the present embodiment can greatly contribute to the improvement of objectivity of pathological diagnosis based on image analysis.
  • the scatter diagrams, histograms, and the like shown in the above embodiments are examples, and it goes without saying that these scatter diagrams and the like differ depending on the biological tissue image to be subjected to pathological diagnosis.
  • the image processing unit 4 displays these on the display unit 6 every time a scatter diagram or a histogram is obtained.
  • the present invention is not limited to this, and the image processing unit 4 Only the finally obtained binarized image may be displayed on the display unit 6.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

Procédé de traitement d'image dans lequel on obtient une valeur seuil optimale pour traitement de binarisation, sans influence de facteurs tels que les conditions de coloration de tissu biologique. L'invention comprend une étape consistant à tracer, sur la base de la densité de couleur, une pluralité de points prédéterminés, sélectionnés à partir d'une image de tissu biologique, sur un champ bidimensionnel comprenant des axes de référence XY ayant deux couleurs différentes, et à obtenir un diagramme de dispersion ; une étape de calcul de la ligne de régression des points prédéterminés sur le diagramme de dispersion, le traçage de lignes droites imaginaires orientées dans une direction orthogonale, à des positions équidistantes sur la ligne de régression, et le traçage de régions délimitées par les lignes droites imaginaires sur le champ bidimensionnel ; une étape d'obtention d'un histogramme montrant le nombre de points prédéterminés inclus dans chaque région ; une étape pour l'obtention d'une première valeur de crête, qui est une valeur de crête de distribution, à partir de l'histogramme ; une étape pour différencier la ligne de distribution de l' histogramme et calculer la première valeur de crête de l'histogramme de différenciation, qui est le point d'inflexion de la ligne de distribution ; et une étape pour calculer la valeur de seuil de la première valeur de crête et la première valeur de crête de l'histogramme de différenciation.
PCT/JP2012/000016 2012-01-04 2012-01-04 Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire WO2013102949A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2013552329A JP5762571B2 (ja) 2012-01-04 2012-01-04 画像処理方法、画像処理装置、画像処理プログラム、および記録媒体
PCT/JP2012/000016 WO2013102949A1 (fr) 2012-01-04 2012-01-04 Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2012/000016 WO2013102949A1 (fr) 2012-01-04 2012-01-04 Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire

Publications (1)

Publication Number Publication Date
WO2013102949A1 true WO2013102949A1 (fr) 2013-07-11

Family

ID=48745028

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2012/000016 WO2013102949A1 (fr) 2012-01-04 2012-01-04 Procédé de traitement d'image, dispositif de traitement d'image, programme de traitement d'image, et support de mémoire

Country Status (2)

Country Link
JP (1) JP5762571B2 (fr)
WO (1) WO2013102949A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05501151A (ja) * 1989-02-24 1993-03-04 セル・アナラシス・システムズ・インコーポレーテッド 細胞の染色および分析を行なうためのデュアルカメラ顕微鏡およびその方法
JPH09119892A (ja) * 1995-10-26 1997-05-06 Olympus Optical Co Ltd 走査型細胞測定装置
JP2000500665A (ja) * 1997-04-18 2000-01-25 アプライド スペクトラル イメージング リミテッド 染色体の分類方法
JP2005535888A (ja) * 2002-08-09 2005-11-24 ユニバーシティー オブ マサチューセッツ 癌の診断法および予後判定法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1504247A1 (fr) * 2002-05-14 2005-02-09 Amersham Biosciences Niagara Inc. Systeme et procede de criblage automatique et rapide de cellules
WO2010087112A1 (fr) * 2009-01-27 2010-08-05 国立大学法人大阪大学 Appareil d'analyse d'image, procédé d'analyse d'image, programme d'analyse d'image et support d'enregistrement
JP2011081648A (ja) * 2009-10-08 2011-04-21 Olympus Corp 病理診断支援装置および病理診断支援システム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05501151A (ja) * 1989-02-24 1993-03-04 セル・アナラシス・システムズ・インコーポレーテッド 細胞の染色および分析を行なうためのデュアルカメラ顕微鏡およびその方法
JPH09119892A (ja) * 1995-10-26 1997-05-06 Olympus Optical Co Ltd 走査型細胞測定装置
JP2000500665A (ja) * 1997-04-18 2000-01-25 アプライド スペクトラル イメージング リミテッド 染色体の分類方法
JP2005535888A (ja) * 2002-08-09 2005-11-24 ユニバーシティー オブ マサチューセッツ 癌の診断法および予後判定法

Also Published As

Publication number Publication date
JPWO2013102949A1 (ja) 2015-05-11
JP5762571B2 (ja) 2015-08-12

Similar Documents

Publication Publication Date Title
US8849024B2 (en) Image processing device and medium storing image processing program
US9916666B2 (en) Image processing apparatus for identifying whether or not microstructure in set examination region is abnormal, image processing method, and computer-readable recording device
US8649580B2 (en) Image processing method, image processing apparatus, and computer-readable recording medium storing image processing program
JP5995215B2 (ja) 癌細胞領域抽出装置、方法、及びプログラム
CN102393959A (zh) 图像处理设备、图像处理方法和图像处理程序
US8346022B2 (en) System and method for generating an intrinsic image using tone mapping and log chromaticity
US10620417B2 (en) Method for generating a reflection-reduced contrast image and corresponding device
EP2040218B1 (fr) Dispositif de traitement d'images et programme de traitement d'images
US20130170756A1 (en) Edge detection apparatus, program and method for edge detection
JP6382973B2 (ja) レジストレーション方法、コンピュータプログラム及び記憶媒体
JP7171549B2 (ja) 画像解析評価方法、コンピュータプログラム、画像解析評価装置
US20150260973A1 (en) Focus determination apparatus, focus determination method, and imaging apparatus
US11210791B2 (en) Computer-implemented method for locating possible artifacts in a virtually stained histology image
WO2019181072A1 (fr) Procédé de traitement d'image, programme informatique et support d'enregistrement
KR20120042988A (ko) 화상 처리 방법, 화상 처리 장치 및 프로그램
Pollatou An automated method for removal of striping artifacts in fluorescent whole-slide microscopy
JP5762571B2 (ja) 画像処理方法、画像処理装置、画像処理プログラム、および記録媒体
US8428352B1 (en) Post processing for improved generation of intrinsic images
JP5860970B2 (ja) 固有画像の生成を改良するための後処理
JP2020144012A (ja) 染色画像推定器学習装置、画像処理装置、染色画像推定器学習方法、画像処理方法、染色画像推定器学習プログラム、及び、画像処理プログラム
CN106462964B (zh) 用于对彩色图像进行分割的方法以及数字显微镜
JP2009053116A (ja) 画像処理装置および画像処理プログラム
US20160320603A1 (en) Method for generating a contrast image of an object structure and apparatuses relating thereto
WO2020107196A1 (fr) Procédé et appareil d'évaluation de qualité de photographie destinés à un appareil photographique et dispositif terminal
WO2019239532A1 (fr) Dispositif de traitement d'image, procédé de traitement d'image et programme

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12864028

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2013552329

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 12864028

Country of ref document: EP

Kind code of ref document: A1