JP2012198139A - Image processing program, image processing device, measurement analysis device and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To easily and accurately set a region of interest (ROI) so as to include a desired ratio of regions.SOLUTION: The image processing device is mounted on a device for measuring and analyzing a behavior of a living organism to support an operation to select a region of the living organism to be measured. The program of the image processing device includes the steps of: moving a region of the same shape as a predetermined shape on a biological image according to the selecting operation in oder to set a measurement region of the predetermined shape in a part of the obtained biological image; calculating the ratio of partitions of pixels included in the region on the basis of a biological structure of the living organism associated with the pixels of the biological image or a value for partitioning a biological state; and presenting information including the calculated partition ratio as an index in the region. The image processing device executes the program.

Description

  The present invention relates to a technique for processing a biological image.

  At present, it is possible to acquire biological samples and biological images by various methods with various sizes (scales). For example, in a radiograph of a human body, it is possible to identify objects of millimeters or smaller in the range of tens of centimeters. Moreover, it is possible to identify the object of the micrometer size from the millimeter range from the microscopic image of the cell or the living tissue. These images provide users with indispensable information for medical diagnosis and medical science / biology research. Hereinafter, these images are collectively referred to as biological images.

  Conventionally, a method for identifying a specific part in a living body has been devised and put into practical use by processing a living body according to a photographing method when acquiring a living body image. For example, in a radiograph, it is possible to identify blood vessels and digestive tracts using a contrast medium. In MRI, various imaging methods using differences in relaxation time for each tissue are used. In an optical microscope, specific tissues, cells, and organelles can be emphasized by staining tissue sections with various staining methods. For example, in hematoxylin and eosin staining, cell nuclei and bone tissue are stained in blue-purple of hematoxylin, and cytoplasm, erythrocytes, and endocrine granules are stained in red in eosin, and this property can be used to identify tissues and determine their status. It is.

  In addition, the development of various synthetic fluorescent dye molecules and fluorescent proteins such as GFP has made it possible to modify specific regions and molecules in the living body. Advances in the development of laser light sources and LED light sources make it easy to excite fluorescent molecules with light of many different wavelengths, and the same sample can be modified simultaneously with multiple fluorescent molecules to create multiple regions and molecules. Taking a distribution image is often performed.

  With recent development of image acquisition electronic devices such as charge-coupled devices (CCD) and higher performance of computers, these biological images are acquired electronically and stored as electronic data. Since the image data has been digitized, it has become easy to quantitatively analyze the image data with a computer.

  When quantitative analysis is performed, it is often performed to select a part to be analyzed from an image and perform numerical processing within the range. A part of the image is called a region of interest or ROI (Region of Interest). The shape of the ROI is often a square or a circle, but may be an arbitrary shape such as a rectangle, an ellipse, or a polygon. In addition, there may be a large number of ROIs in the entire image. In that case, the size and shape of the ROI may be the same or different between the ROIs.

By the way, a user who wants to analyze an image must discriminate the feature of the image and set the ROI at a location suitable for the purpose of the analysis.
Usually, the ROI is often set so that it is completely included in the same region. For example, in a cell image, the ROI is set so as to be included in the nucleus, or the ROI is set so as to be included in the cytoplasm.
However, depending on the analysis method and purpose, an ROI that straddles a plurality of regions may be set. For example, in a biological image of a tissue in which two types of cells A and B are mixed, the correlation between the amount of protein expressed in the cell A and the mixing ratio of the cells A and B is analyzed.

  By the way, Raster-scan Image Correlation Spectroscopy (RICS) is known as an analysis method that needs to set an ROI across a plurality of regions. RICS is a technique for measuring the time-space correlation of fluorescence intensity at two points by utilizing the difference in time at which each pixel in an image is acquired in a laser scanning microscope. By using RICS, the diffusion coefficient of the fluorescence-modified molecule can be measured. In RICS, since the accuracy of measurement varies greatly depending on the size of ROI, even if the structure to be measured is small, it is not possible to select a small range of ROIs according to the structure. Therefore, a method has been adopted in which an analysis result is compared between an ROI including a target structure and an ROI not including the target structure using an ROI larger than the structure to be measured.

MA.Digman et.al., “Measuring Fast Dynamics in Solutions and Cells with a Laser Scanning Microscope.”, Biophysical Journal Vol. 89, 1317–1327, 2005. Gielen, E. et al. (2009), “Measuring diffusion of lipid-like probes in artificial and natural membranes by raster image correlation spectroscopy (RICS): use of a commercial laser-scanning microscope with analog detection”, Langmuir, 25, 5209-18.

  As described above, when the ROI is set across a plurality of regions in the image, the analysis result varies depending on the ratio of each region included in the ROI. Therefore, when comparing results from a plurality of ROIs, the user needs to set the location of the ROI in consideration of the ratio of each region included in the ROI.

  Therefore, the user has set the position of the ROI so that each region has a desired ratio by visual observation. However, when the ROI position is set visually, the judgment criteria for each user are different, and the shape of the region is not necessarily the same, so the objectivity and reproducibility of the analysis results are impaired. It will end up.

  Therefore, when setting a region of interest (ROI), there is a need for a technique that can easily and accurately set a region of interest (ROI) in which the ratio of each region has a desired value.

  The present invention for solving the above-described problems is provided in a program of an image processing apparatus that is mounted on an apparatus for measuring and analyzing a behavior of a living body and supports an operation of selecting a measurement target region of the living body. In order to set a measurement target region of a predetermined shape in part, a step of moving a region having the same shape as the predetermined shape on the biological image according to the selection operation, and corresponding to each pixel of the biological image A step of calculating a ratio of the classification of the pixels included in the region based on a value that classifies the anatomy or biological state of the living body, and information including the calculated proportion of the classification This is a program for causing the image processing apparatus to execute the step presented as an index.

  Further, the present invention provides an image processing apparatus that is mounted on an apparatus for measuring and analyzing a behavior of a living body and supports an operation of selecting a measurement target area of the living body. Means for moving an area having the same shape as the predetermined shape on the living body image in accordance with the operation for selecting the area, and a living body structure of the living body associated with each pixel of the living body image Alternatively, based on a value for classifying the biological state, means for calculating the ratio of the classification of the pixels included in the area, and means for presenting information including the calculated ratio of the classification as an index in the area An image processing apparatus provided.

  Moreover, this invention is an apparatus which measures and analyzes the behavior of the biological body provided with the image processing apparatus which is the above-mentioned invention.

  According to another aspect of the present invention, there is provided an image processing method of an image processing apparatus that is mounted on an apparatus for measuring and analyzing a behavior of a living body and that supports an operation of selecting a measurement target region of the living body. In order to set a shape measurement target region, a step of moving a region having the same shape as the predetermined shape on the biological image in accordance with the operation for selecting, and the pixel associated with each pixel of the biological image Based on a value that classifies the anatomy or biological state of a living body, a step of calculating the ratio of the classification of the pixels included in the area, and presenting information including the calculated ratio of the classification as an index in the area And an image processing method.

  According to the present invention, when setting a region of interest (ROI), it is possible to easily and accurately set a region of interest (ROI) in which the ratio of each region has a desired value.

The figure which shows the structure of the laser microscope system to which the image processing method of 1st Embodiment is applied. 3 is a flowchart showing an operation procedure of the image processing apparatus according to the first embodiment. The figure which shows the process screen displayed on a display apparatus by the image processing method of 1st Embodiment. The figure which shows the state from which the ratio of each area | region in ROI changes with the movement of ROI of 1st Embodiment. The figure which shows the Example which sets ROI of 1st Embodiment. The figure for demonstrating the method to designate the conditions about the ratio of the area | region of 2nd Embodiment. The figure which superimposed and represented the set of ROI which satisfy | fills the conditions designated on the area | region classification image of 2nd Embodiment. The figure which represented the area | region of ROI which satisfy | fills the conditions designated on the area | region classification image of 2nd Embodiment with light and dark. The figure showing that ROI settled in the area | region which satisfy | fills the conditions specified on the area | region classification image of 2nd Embodiment. The figure which shows the other method of assisting the user's ROI setting of 2nd Embodiment. The figure which showed the result which moved ROI and calculated | required the diffusion coefficient with respect to the cells A and B from which 3rd Embodiment differs. The figure which plots and shows the value of the diffusion coefficient with respect to the ratio of the cytoplasm in ROI with respect to the cells A and B of 3rd Embodiment.

[First Embodiment]
FIG. 1 is a diagram illustrating a configuration of a laser microscope system to which the image processing method of the first embodiment is applied.
The laser microscope system includes a microscope main body 1, a laser combiner 2, a scan unit 3, a detector unit 4, a control unit 5, a display device 6 and a storage device 7.

  Laser light emitted from one laser light source provided in the laser combiner 2 is reflected by a dichroic mirror (not shown) and then enters the scan unit 3. The scanning unit 3 deflects and scans the optical axis in the X-axis and Y-axis directions and enters an objective lens (not shown) of the microscope body 1. As a result, the measurement region (focal region) can be positioned at an arbitrary position of the fluorescently labeled sample specimen on the focal plane in the field of view.

  Fluorescence emitted from the fluorescent molecules in the focal region is captured by the same objective lens and guided to the dichroic mirror through the reverse optical path. The dichroic mirror is designed to transmit fluorescence having a longer wavelength than the excitation light, and the fluorescence reaches the detector unit 4. As the detector unit 4, a photomultiplier tube is suitable.

  The fluorescence intensity signal photoelectrically converted by the detector unit 4 is input to the control unit 5. In the control unit 5, RICS analysis is performed by analysis software. The control unit 5 performs RICS analysis by associating the fluorescence intensity signal from the detector unit 4 with the scan position information from the scan unit 3. The result of the RICS analysis is appropriately displayed on the display device 6 and stored in the storage device 7.

On the right side of FIG. 1, information obtained by the laser microscope system is schematically shown.
The sample 8a is repeatedly scanned and irradiated with laser light to obtain a plurality of fluorescent images 8b. Based on each fluorescence image 8b, data 8c relating to the fluorescence intensity is obtained and input to the analysis software. The analysis software performs RICS analysis based on each data 8c. That is, spatial correlation calculation is performed to obtain a spatial correlation function diagram 8d, and a fitting analysis is performed between the result and the reference spatial correlation function diagram.

  The user can set the ROI via the control unit 5 for the biological image displayed on the display device 6. On the display device 6, the living body image and the region representing the set ROI are combined and displayed.

FIG. 2 is a flowchart illustrating an operation procedure of the image processing apparatus according to the first embodiment.
In step S01 of FIG. 2, the control unit 5 displays the fluorescent microscope image 11 of the living body on the display device 6.

FIG. 3 is a diagram illustrating a processing screen 10 displayed on the display device 6 by the image processing method according to the first embodiment.
The processing screen 10 has three display areas. The fluorescence microscope image 11 is displayed in the first block at the upper left. The region classification image 12 is displayed in the second block on the upper right. In the third block at the lower right, the area ratio diagram 15 is displayed. Details of the area classification image 12 and the area ratio FIG. 15 will be described later.

  The fluorescence microscope image 11 includes a plurality of regions. Each region can be distinguished as a difference in shape, contrast, and color according to the photographing method and the staining method. However, when determining which part of the fluorescence microscope image 11 belongs to which region, it is sufficient if information necessary for selecting the position of the ROI is obtained. Therefore, it is not necessary to distinguish all the regions, and it is not necessary to distinguish them strictly corresponding to the biological definition. For example, when attention is paid to the ratio between the nucleus and the cytoplasm in the cell image, it is not necessary to distinguish the various organelles contained in the cytoplasm. In the first embodiment, description will be made assuming that the region is composed of three types of regions: extracellular, cytoplasm, and nucleus.

  In addition, as an example of a plurality of regions, in the case of the above-described image of cultured cells, there are regions such as nuclei, cytoplasm, and organelles in cells scattered in the culture solution. In the case of an image of a part of a living individual, each organ or vascular system is distinguished as a different area. In addition, individual tissues constituting each organ can be set as another region.

  In step S02, the control unit 5 identifies a plurality of regions included in the fluorescence microscope image 11. That is, as described above, the fluorescence microscope image 11 is identified in three types of regions: extracellular, cytoplasm, and nucleus. The region may be identified manually by the user through the control unit 5 based on the image information and known knowledge, and the control unit 5 automatically or semi-automatically with the support of known image analysis software. You can go.

  In step S03, the control unit 5 displays on the processing screen 10 an area classification image 12 that represents the plurality of identified areas.

  In step S04, the control unit 5 acquires the type information of each pixel from the region classification image 12. The type information is information indicating to which area each pixel belongs.

  The type information may be represented by an integer value, for example. In this case, the type information I can be defined as a function that returns an integer value with respect to the coordinates (x, y) of the pixel. Alternatively, when the value of the pixel (x, y) is represented as a real matrix C (x, y), the type information corresponding to the pixel (x, y) is represented as an integer matrix I (x, y). Also good. This representation facilitates mathematical handling.

For example, in the cell image, three types of areas of extracellular, cytoplasm, and nucleus are assigned type information of 0 for extracellular, 1 for cytoplasm, and 2 for nucleus. The fact that the pixel located at the coordinates (10, 20) is included in the cytoplasm region is expressed by Expression (1).
I (10, 20) = 1 Formula (1)
The data of I (x, y) can be stored in the storage device 7 and read at the time of analysis. In this case, the data of I (x, y) may be stored as an independent file, or the same data format corresponding to the original image data as long as it can store a plurality of image data. You can also save it to a file.

  In step S05, the control unit 5 supports the user's operation for setting the ROI.

  The user executes an operation for setting an ROI on the region classification image 12 of FIG. However, the user may execute an operation for setting the ROI on the fluorescence microscope image 11. When an operation for setting an ROI on one image is performed, the ROI is also displayed at a corresponding position on the other image. At that time, when the ROI operation is performed on one image, the user can select whether to display the ROI being operated on the other image.

  The user selects the ROI setting mode from a menu bar (not shown) on the processing screen. The frame 13 indicating the ROI appears by moving the mouse cursor 14 over either the region classification image 12 or the fluorescence microscope image 11 and first depressing the mouse button. When the size of the ROI is determined in advance by the setting of the analysis method, a temporary ROI is set on the spot. When the ROI size is arbitrary, the mode shifts to a mode for selecting the ROI shape (square, rectangle, circle, ellipse, etc.) and size on the spot.

  For example, when the user desires a rectangular ROI, the size is determined as follows. The starting point is the position where the mouse button is pressed for the first time. The size of the ROI frame changes as the mouse is moved while the button is held down, and the rectangle whose end point is the position where the button is released is set as the ROI. Is done.

  In step S06, the control unit 5 calculates and displays the ratio of each area in the ROI.

In the ROI, the number S (i) of pixels belonging to a region whose classification information index is i (hereinafter referred to as region i) is the number of pixels where I (x, y) = i. That is, S (i) is represented by the formula (2).

The occupation ratio R (i) in the ROI of the region i can be expressed by Expression (3) using the total number of pixels N in the ROI.

  When the user's interest is only the proportion of the region i in the ROI, R (i) can be presented to the user as a value representing the characteristic of the ROI. In addition, the user's interest is the elements i, j, k,. . . <R (i), R (j), R (k),. . . A set of> values can be presented to the user. In FIG. 15, the ratios of the extracellular, cytoplasm, and nucleus areas in the ROI are displayed. Note that the area desired to be displayed in the area ratio FIG. 15 can be designated by the user from a setting screen (not shown).

  In step S07, the control unit 5 updates the display of the ROI and the display of the ratio of each area in accordance with the movement of the ROI.

  The user can move the ROI by pressing the mouse button on the ROI frame and moving the mouse (mouse dragging) while holding down the button. When the mouse is dragged, the ROI frame 13 moves following the movement of the mouse cursor 14. As the ROI frame 13 moves, the ratio of each area in the ROI changes.

  FIG. 4 is a diagram illustrating a state in which the ratio of each region in the ROI changes as the ROI moves according to the first embodiment.

  As shown in FIG. 4, when the user moves the ROI, the ratio of each area at the position of the ROI is displayed in real time, so that the user can easily grasp the status of the moving ROI. When the user determines that the ROI is at a desired position from the position on the image and the ratio of each area, the user stops the movement of the ROI at that position and determines the ROI.

  Next, a method for displaying the ratio of the area i in real time in FIG. 15 will be described. In the following description, the ratio of the area i displayed in the area ratio FIG. 15 is represented as <R (i)>, unless there is a particular need for distinction.

  Since the calculation of <R (i)> is simple as shown in the formulas (2) and (3), it can be calculated at high speed using a computer that is generally used at present. Therefore, if the user sets an ROI having an arbitrary shape and size in the image, it can be calculated and displayed in real time according to the movement of the ROI.

  Further, when the user designates the shape and size of the ROI, it is also possible to easily calculate <R (i)> for all possible ROI positions in the image. That is, representative coordinates (x, y) defined corresponding to the shape and size of ROI, and calculated Ri (x, y), Rj (x, y), Rk (x, y),. . . Are stored in the memory in association with each other. When the user is performing an operation to move the ROI, <R (i)> corresponding to the representative coordinates (x, y) of the ROI is extracted from the memory, so that <R (I)> can be presented.

  FIG. 5 is a diagram illustrating an example of setting the ROI according to the first embodiment.

  In the fluorescence microscope image 11, since the fluorescent molecule is present in the cytoplasm in the cell, it can be easily determined that the portion with high luminance is the cytoplasm and the region surrounded by the cytoplasm is the nucleus. The region classification image 12 is a diagram created by manual classification by the user based on the difference in luminance. A legend for classification is shown together with the area classification image 12.

  A plurality of ROIs were set on this region classification image. The ROI included in a single region can be easily performed while looking at the fluorescence microscope image 11 without using the region classification image 12. As such, B and G in the figure can be set as ROIs including only the nucleus, and D and F in the figure can be set as ROIs including only the cytoplasm.

  The ROI containing 50% cytoplasm (A, E in the figure) is set by moving the ROI frame near the edge of the cell by the mouse, and the value of the cytoplasm occupancy R (i = 1) displayed on the screen is This can be done by determining the ROI at the position where it has reached 0.5 (50%).

  The ROI containing 30% of the nuclei (C, H, I in the figure) is set by moving the ROI frame near the edge of the nuclei with the mouse and determining the occupancy rate R (i = 2) This can be done by determining the ROI at the position where the value becomes 0.3 (30%). In the figure, ROI of I includes all regions of nucleus, cytoplasm, and extracellular, and <R (0), R (1), R (2)> = <0.1, 0.6, 0.3> is there. If attention is paid only to R (2), it can be regarded as a region similar to C or H in the figure, and if an ROI including only the nucleus and cytoplasm is selected, care must be taken that R (0) = 0. The location of the ROI can be selected.

  Conventionally, the user has confirmed the ROI with an approximate register by visual observation. In the present embodiment, the ROI can be determined while looking at the value of R (i). High settings can be made.

[Second Embodiment]
In the second embodiment, the auxiliary function when the user performs the ROI operation is enhanced. The same parts as those in the first embodiment are denoted by the same reference numerals, and detailed description thereof is omitted.

  In the ROI operation of the user, it is possible to assist so that an ROI that satisfies the condition <R (i)> defined by the user can be easily selected. The conditions are R (i), R (j), R (k),. . . You can give them all or only some. The conditions that <R (i)> should satisfy include the condition that the value of R (i) is within the target range, the condition that the result calculated using the occupancy ratio of a plurality of regions is within the target range, and the like Is possible. For example, it is possible to give a condition such that the ratio of the occupancy ratios of the two areas is within the target range.

  FIG. 6 is a diagram for explaining a method for designating a condition for the ratio of regions according to the second embodiment.

  In FIG. 6, a column 20 for entering a name and a column 21 for entering a target range are provided for designating the ratio condition for each of the regions R1 to R3. In addition, a column 22 for entering an arithmetic expression is provided in order to make the result of calculation using each ratio a condition. In the content shown in FIG. 6, each region is named “extracellular”, “cytoplasm”, and “nucleus”, and the target range is designated. Further, the calculation of extracellular / cytoplasm is designated by the name of “ratio”, and the target range of the calculation result is designated. If the item name to be displayed is determined in advance, the column 20 for entering the name may be omitted.

FIG. 7 is a diagram in which a set of ROIs that satisfy the conditions specified on the region classification image 12 according to the second embodiment are superimposed.
When the user sets the condition that the nucleus is not included (0%) and the cytoplasm ratio is 50% (49.5% or more and less than 50.5%), a set of ROIs that meet the condition is displayed in the image. decide.

  When the ROI size is determined in advance according to the request of the analysis method, it is possible to calculate the occupancy ratios at all possible ROI positions based on the region classification image 12 in advance. Based on this information, the set of ROIs shown in FIG. 7 can be acquired.

  The set of ROIs shown in FIG. 7 is a subset that is continuously present within a certain range as shown by S1, S3, and S4, and is in the vicinity of almost the same position as shown by S2. It consists of a subset that is.

FIG. 8 is a diagram showing ROI regions that satisfy the conditions specified on the region classification image 12 according to the second embodiment in light and dark.
An area 25 in FIG. 8 is an area that envelops a set of ROIs that satisfy a specified condition. The region 25 is displayed with a gradation that is brighter than the ground portion. The user can select an appropriate ROI by setting the ROI so that the ROI frame fits in the area.

FIG. 9 is a diagram illustrating that the ROI is within an area that satisfies the conditions specified on the area classification image 12 according to the second embodiment.
As shown in FIG. 9, it is possible to explicitly indicate to the user that the ROI is within the region 25 that satisfies the condition by changing the thickness and color of the frame, thereby assisting the user's operation. When the ROI does not fit in the region 25 that satisfies the condition, the ROI frame is displayed thinly as indicated by the frames F1 and F3. When the ROI is within the region 25 that satisfies the condition, the ROI frame is displayed thick as indicated by the frame F2.

  In this way, the user can know the position of the ROI that is allowed in advance, so that the user can concentrate on the determination of a more advanced image state without performing unnecessary trial and error.

  FIG. 10 is a diagram illustrating another method for assisting the user in setting the ROI according to the second embodiment. In this other method, the ROI in the region 25 closest to the position indicated by the mouse cursor is displayed. The ROI is selected such that the distance between the center of gravity of the displayed ROI and the position indicated by the mouse cursor is the shortest.

  In the example shown in FIG. 10, when the mouse cursor is at the position 1a, 1b is selected as the ROI, ROI2b is selected for the mouse cursor 2a, and ROI3b is selected for the mouse cursor 3a.

  In the method shown in FIG. 10, the user can always select the ROI in the area 25. However, when the shape of the region 25 is complicated, the user may feel that the selected ROI is inappropriate. In that case, this function can be disabled.

  In the second embodiment, the region 25 that envelops the set of ROIs that satisfy the specified condition is clearly indicated, but the existence of the region 25 may not be clearly indicated. When a user satisfies the condition given by <R (i)> while changing the shape, size, and position of the ROI on the image, the condition is determined by the screen display or a sound signal. The user may be informed that the ROI is at the position to be filled. As a transmission method, for example, there is a method of displaying a frame indicating the ROI shape / position or a character displaying <R (i)> thickly or changing the color to draw attention.

[Third Embodiment]
In the third embodiment, an example in which RICS analysis is performed using the functions described in the first and second embodiments will be described. The same parts as those in the first and second embodiments are denoted by the same reference numerals, and detailed description thereof is omitted.

  As described in the first and second embodiments, <R (i)> is an index that can easily indicate what the ROI means for an image. Therefore, when an analysis value F for a certain ROI is obtained, it may be desirable to process the analysis value in combination with <R (i)>. For example, the analysis value of ROI having the same <R (i)> is statistically processed, or the regression analysis of <R (i)> and the analysis value F is performed. Therefore, when displaying and storing the analysis value F, <R (i)> is simultaneously displayed and stored.

  The results of analyzing the cell membrane using the occupancy of the cytoplasm in the ROI when deriving the diffusion coefficient of the fluorescently labeled protein by RICS will be described below.

  Since the diffusion coefficient in the cell membrane and the diffusion coefficient in the cytoplasm are different, the measured value differs depending on the ratio of the ratio between the cytoplasm and the cell membrane in the ROI. Since the thickness of the cell membrane is about 10 nm, which is much thinner than the resolution of the optical microscope, it is difficult to identify the cell membrane region on the screen. Therefore, analysis was performed to estimate the diffusion coefficient in the cell membrane by analyzing how the measured value changes depending on the cytoplasm ratio in the ROI.

FIG. 11 is a diagram illustrating a result of obtaining the diffusion coefficient by moving the ROI for the different cells A and B of the third embodiment.
The diffusion coefficient is represented by D1. G1 is an index representing the number of molecules. In FIG. 11, the measurement results of different cells A and B are displayed in correspondence with the cytoplasm ratios of 10%, 50%, 80%, and 100%. Since there is a common index called cytoplasm occupancy, diffusion coefficients in ROIs that show the same cytoplasm occupancy can be compared between different cells.

FIG. 12 is a diagram plotting diffusion coefficient values with respect to the ratio of cytoplasm in ROI with respect to cells A and B.
As shown in FIG. 12, the diffusion coefficients can be compared between different cells A and B using the cytoplasm ratio as an index. From this result, the relationship between the cytoplasm ratio and the diffusion coefficient can be obtained by regression analysis, and the diffusion coefficient when the cytoplasm ratio is 0 can be estimated. That is, the diffusion coefficient in the cell membrane can be obtained as the diffusion coefficient when the cytoplasm ratio is zero.

  By using the method for assisting ROI selection disclosed in the present embodiment, a large number of ROIs having the same cytoplasm ratio can be selected. Therefore, an average value is calculated for the diffusion coefficient obtained for each ROI. Processing is possible. Therefore, when recording the result of obtaining the diffusion coefficient for each ROI on the disk, the ratio of the cytoplasm in the ROI is recorded at the same time, and when performing statistical analysis in later operations, the recording is performed. Can be used for analysis of cytoplasm percentage information.

  Note that the functions described in the above-described embodiments are not limited to being configured using hardware, and can be realized by causing a computer to read a program describing each function using software. Each function may be configured by appropriately selecting either software or hardware.

Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage.
Various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, you may combine suitably the component covering different embodiment.

  DESCRIPTION OF SYMBOLS 1 ... Microscope main body, 2 ... Laser combiner, 2b ... ROI, 3 ... Scan unit, 3b ... ROI, 4 ... Detector unit, 5 ... Control unit, 6 ... Display apparatus, 7 ... Memory | storage device, 8a ... Sample, 8b ... Fluorescence Image, 8c ... data, 10 ... processing screen, 11 ... fluorescent microscope image, 12 ... area classification image, 13 ... frame, 14 ... mouse cursor, 25 ... area.

Claims (10)

In a program for an image processing apparatus that is mounted on an apparatus for measuring and analyzing the behavior of a living body and supports an operation of selecting a measurement target region of the living body,
Moving a region of the same shape as the predetermined shape on the biological image in accordance with the operation of selecting to set a measurement target region of a predetermined shape in a part of the acquired biological image;
Calculating a ratio of the classification of pixels included in the region based on a value that classifies the anatomy or biological state of the living body associated with each pixel of the biological image;
A program for causing the image processing apparatus to execute the step of presenting information including the calculated ratio of the classification as an index in the region. Obtaining a condition that satisfies information including the proportion of the category;
The program according to claim 1, further causing the image processing apparatus to execute a step of outputting that the information including the proportion of the section is in a position satisfying the condition. Obtaining a set of positions of the regions for which the information including the ratio of the sections satisfies the condition for the biological image;
The program according to claim 2, further causing the image processing apparatus to execute a step of synthesizing and displaying an area enveloping a set of the obtained position of the area on the biological image.   The program according to claim 3, wherein the information including the division ratio includes a value calculated by combining the division ratios.   The program according to claim 1, further causing the image processing apparatus to execute a step of associating and storing information including a ratio of the division in the measurement target region and the analysis result in the measurement target region. In an image processing apparatus that is mounted on an apparatus for measuring and analyzing a behavior of a living body and supports an operation of selecting a measurement target region of the living body,
Means for moving a region having the same shape as the predetermined shape on the biological image in accordance with the operation of selecting to set a measurement target region of a predetermined shape in a part of the acquired biological image;
Means for calculating a ratio of the classification of pixels included in the region based on a value that classifies the anatomy or biological state of the living body associated with each pixel of the biological image;
An image processing apparatus comprising: means for presenting information including the calculated proportion of the section as an index in the region. Means for obtaining a condition that information including a ratio of the classification is satisfied;
The image processing apparatus according to claim 6, further comprising: means for outputting that the area includes information including the ratio of the division at a position satisfying the condition. Means for acquiring, for the biological image, a set of positions of the region where the information including the ratio of the sections satisfies the condition;
The image processing apparatus according to claim 7, further comprising a unit that synthesizes and displays a region enveloping the acquired set of the region positions on the biological image. In a device that measures and analyzes the behavior of a living body,
An apparatus comprising the image processing apparatus according to claim 6. In an image processing method of an image processing apparatus that is mounted on an apparatus for measuring and analyzing a behavior of a living body and supports an operation of selecting a measurement target region of the living body,
Moving a region of the same shape as the predetermined shape on the biological image in accordance with the operation of selecting to set a measurement target region of a predetermined shape in a part of the acquired biological image;
Calculating a ratio of the classification of pixels included in the region based on a value that classifies the anatomy or biological state of the living body associated with each pixel of the biological image;
An information processing method comprising: a step of presenting information including the calculated proportion of the section as an index in the region.

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