Method and apparatus for analyzing composite material structure, and program thereof

Abstract

To provide an appropriate texture feature analysis method for composite material texture.SOLUTION: Provided is a composite material texture analysis method using an information processing device, the method including: an image input step (S1) for inputting the observation image of a composite material, a binary image creation step (S2) for dividing the observation image into regions and creating a binary image; a texture feature analysis step (S3) for analyzing a texture feature from the binary image; and a texture feature output step (S4) for outputting the analyzed texture feature. The texture feature analysis step (S3) further includes a boundary image creation step (S31) for creating a boundary image in which boundaries in the binary image are defined as positive and others are defined as negative, and a normalized boundary length measurement step (S32) for measuring the total number of pixels in boundaries of the boundary image, dividing by a pixel size to convert into a boundary length, and measuring a normalized boundary length derived by normalizing the boundary length by the ratio of an imaged area to a designated area.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method and an analysis apparatus for analyzing the tissue characteristics of a composite material.

Background Art

[0002] The performance of a light-emitting element composed of a light-emitting material and a non-light-emitting material depends not only on the characteristics of each material but also on the structure of the light-emitting element. Taking a resin scintillator as an example, the performance of a resin scintillator formed by pulverizing a ceramic scintillator, mixing it with a resin, and curing the resin depends not only on the material characteristics of the powder and the resin but also on the structure of the resin scintillator. Technologies for improving the performance by controlling the structure of the resin scintillator were investigated. In addition, technologies for improving the performance by structure control were investigated for light-emitting elements other than resin scintillators.

[0003] Patent Document 1 describes "a bulk light-converting ceramic composite composed of a solidified body having a structure in which at least two or more oxide phases are continuously and three-dimensionally intertwined with each other, and at least one of the oxide phases is a crystalline phase that emits fluorescence, and the interface length between the oxide phases per 1 mm on a plane in the light-converting ceramic composite is 150 mm or more and 1500 mm or less." (See the claims). 2 A light-converting ceramic composite characterized in that the interface length between the oxide phases is 150 mm or more and 1500 mm or less per 1 mm on a plane." (See the claims) is described.

[0004] Patent Document 2 describes "an X-ray detector obtained by kneading and curing a scintillator powder and a light-transmitting resin, wherein the filling degree of the scintillator powder changes almost continuously in the thickness direction of the X-ray detector, the filling degree A on the light detector side for converting light into electricity is larger than the filling degree B on the opposite side, and the ratio A / B of the filling degree A to the filling degree B is 1.1 or more and 5.0 or less." (See the claims) is described.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] We are conducting research and development to analyze the relationship among process-organizational characteristics-device performance and improve device performance through tissue control.

[0007] Patent Document 1 describes that in a ceramic composite for optical conversion it is important to control the interface length in order to obtain stronger fluorescence at the same thickness. The interface length is defined as the length of the interface observed as a boundary line when observing the ceramic composite structure for optical conversion in a plane. However the method for measuring the interface length is not described.

[0008] Patent Document 2 describes that in order to reduce the amount of scintillator powder containing heavy metals without significantly reducing the luminescence intensity of the detector it is important to control the filling rate of the scintillator powder. The filling degree of the scintillator powder is expressed as a percentage of the area of the scintillator powder in the observed area when observing the detector tissue with a microscope or the like. It is described that the area percentage of the scintillator powder in the observed area is calculated by drawing vertical and horizontal grid lines on a micrograph of the detector and counting the grids containing the scintillator powder. However the boundary length is not considered as an organizational characteristic.

[0009] Organizational characteristics depend not only on the definition of organizational characteristics but also on the measurement method and the field-of-view size of the observation image. Without defining the measurement method and the field-of-view size organizational characteristics cannot be treated as macroscopic average information and cannot be used for analyzing the relationship among process-organizational characteristics-device performance.

[0010] An object of the present invention is to provide a method for analyzing appropriate organizational characteristics of a composite material structure. [Means for Solving the Problems]

[0011] If an example of the "analysis method of composite material structure" of the present invention for solving the above problems is given, it is an analysis method of composite material structure using an information processing apparatus, an image input step of inputting an observation image of a composite material, a binary image creation step of dividing the observation image into regions to create a binary image, a tissue feature analysis step of analyzing tissue features from the binary image, and a tissue feature output step of outputting the analyzed tissue features, wherein the tissue feature analysis step includes a boundary image creation step of creating a boundary image in which the boundary in the binary image is positive and the rest is negative, a normalized boundary length measurement step of measuring the total number of pixels of the boundary in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length, and is characterized by including the above.

[0012] Also, if an example of the "analysis apparatus for composite material structure" of the present invention is given, it is an analysis apparatus for composite material structure, an image input unit for inputting an observation image of a composite material, a binary image creation unit for dividing the observation image into regions to create a binary image, a tissue feature analysis unit for analyzing tissue features from the binary image, and a tissue feature output unit for outputting the analyzed tissue features, wherein the tissue feature analysis unit includes a boundary image creation unit for creating a boundary image in which the boundary in the binary image is positive and the rest is negative, a normalized boundary length measurement unit for measuring the total number of pixels of the boundary in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length, and is characterized by including the above.

Advantages of the Invention

[0013] According to the present invention, by measuring the normalized boundary length using a boundary image created from a binary image, it is possible to provide an analysis method for appropriate tissue characteristics of a composite material structure. According to the present invention, the accuracy and efficiency of tissue characteristic analysis can be improved, and research and development for improving element characteristics by tissue control can be accelerated.

[0014] Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of Drawings

[0015]

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Embodiments for Carrying Out the Invention

[0016] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention is not to be construed as being limited to the description of the embodiments shown below. Those skilled in the art can easily understand that the specific configuration can be changed without departing from the idea or gist of the present invention. In addition, in the configuration of the invention described below, the same reference numerals are commonly used among different drawings for the same part or parts having the same function, and redundant descriptions may be omitted. Expressions such as "first", "second", "third", etc. in this specification and the like are attached to identify components, and do not necessarily limit the number, order, or content thereof. In addition, the numbers for identifying components are used for each context, and the numbers used in one context do not necessarily indicate the same configuration in other contexts. Also, a component identified by a certain number is not precluded from having the functions of a component identified by another number. The positions, sizes, shapes, ranges, etc. of the respective configurations shown in the drawings and the like may not represent the actual positions, sizes, shapes, ranges, etc. in order to facilitate understanding of the invention. For this reason, the present invention is not necessarily limited to the positions, sizes, shapes, ranges, etc. disclosed in the drawings and the like.

Examples

[0017] In Example 1, a resin scintillator is taken as the object of analysis, and a method for analyzing the composite material tissue characteristics is described. When radiation such as X-rays or γ-rays hits a scintillator, it absorbs the energy and emits visible light. A resin scintillator is a composite material composed of scintillator powder and epoxy resin. The scintillator powder is prepared by pulverizing a ceramic scintillator. Examples of the ceramic scintillator material include Gd2O2S and CdWO4. A paste-like mixture composed of scintillator powder and epoxy resin is poured into a mold and the resin is cured to produce it. During curing, a difference occurs in the tissue characteristics in the direction of sedimentation due to the self-weight of the scintillator powder.

[0018] To analyze the morphological characteristics of the tissue, it is necessary to binarize the observation image. The tissue characteristics are analyzed in the binary image, and the analyzed tissue characteristics are output. Fig. 1 shows a flowchart for analyzing the tissue characteristics of the observation image. It includes an image input step (S1) for inputting an observation image of a composite material composed of a luminescent material and a non-luminescent material, a binary image creation step (S2) for dividing the observation image into regions to create a binary image, a tissue characteristic analysis step (S3) for analyzing the tissue characteristics in the binary image, and a tissue characteristic output step (S4) for outputting the analyzed tissue characteristics. The tissue characteristic analysis step (S3) includes a boundary image creation step (S31) for creating a boundary image and a normalized boundary length measurement step (S32) for measuring the normalized boundary length as a tissue characteristic. The details of each step will be described below.

[0019] <S1. Image input step> The observation image of the resin scintillator is obtained using a scanning electron microscope ( S canning E lectron MTake images with a (microscope). To obtain an SEM image, expose the sample cross-section. Since image contrast depending on the unevenness will be observed in the SEM image if there are unevenness on the cross-section to be imaged, process the surface to be imaged flat by polishing. A focused ion beam processing apparatus or a cross-section polishing processing apparatus may be used for the flat processing. Since the scintillator and the epoxy resin are insulators, after polishing, Pt-Pd was coated with a thickness of several nm using an ion sputtering apparatus for antistatic purposes, and then the observation image was taken. Input the taken image into the composite material structure analysis system. Note that the input image is not limited to the SEM image. Images taken with other imaging devices such as an optical microscope or a probe microscope may also be used.

[0020] <S2. Binary Image Creation Step> Divide the input observation image into regions of the scintillator powder and the resin. After normalizing the intensity of the SEM image, binarize it by the threshold method for region division. To normalize the intensity of the SEM image, create an intensity histogram of the SEM image and specify the mode value of the image intensity of the scintillator powder and the mode value of the image intensity of the resin. Convert the image intensity so that each mode value becomes the specified value. For example, after normalizing so that the mode value of the image intensity of the scintillator powder is 200 and the mode value of the image intensity of the resin is 60, binarize it with a threshold of 130. An example of an SEM image of the resin scintillator is shown in Fig. 2A, and the image binarized by the threshold method is shown in Fig. 2B. In the binary image, the intensity of the pixels of the scintillator powder 1 is set to 1 (white), and the intensity of the pixels of the resin 2 is set to 0 (black). When the observation surface is produced by mirror polishing, regions 31 where particles are removed and regions 32 with polishing scratches are observed. When this SEM image is binarized by the threshold method, information different from the structure of the resin scintillator powder is mixed into the binary image. The information on particle removal and polishing scratches was excluded from the binary image by the following procedure. In the SEM image, if there are unevenness on the sample surface, the secondary electrons increase from the side walls. It was judged that there were surface unevenness in the regions with high SEM image intensity, and it was corrected using the paint function while referring to the SEM image. Fig. 2C shows the image with particle removal and polishing scratches corrected.

[0021] <S3. Structure Feature Analysis Step> As an organizational feature analyzed using a binary image, there is a filling rate. The number of pixels of the scintillator powder contained in the binary image is counted and normalized by the total number of pixels to measure the filling rate. On the other hand, the normalized boundary length is an organizational feature that cannot be measured from the number of pixels of the binary image. Therefore, a method for measuring the normalized boundary length as an organizational feature was devised.

[0022] The step of measuring the normalized boundary length includes a boundary image creation step (S31) of creating a boundary image in which the boundaries of each region in the binary image are positive and the rest are negative, and measuring the total number of pixels of the boundaries in the boundary image, converting it to a boundary length by dividing by the pixel size, and measuring a normalized boundary length obtained by normalizing the boundary length by the ratio of the imaging area to the specified area (S32).

[0023] <S31. Boundary Image Creation Step> A boundary image is created by extracting the outermost pixels of the scintillator powder in the binary image. An example of the binary image is shown in Fig. 3A, and an example of the boundary image created from the binary image is shown in Fig. 3B. The boundary image is an image in which the pixels of boundary 4 are made positive and the other pixels are made negative. Note that if an image in which the pixels of boundary 4 are made negative and the other pixels are made positive is used, the same processing will be executed if the pixels with a negative label are treated as the boundary.

[0024] <S32. Normalized Boundary Length Measurement Step> First, count the number of pixels of the boundaries included in the boundary image. Measure the boundary length by dividing the number of pixels of the boundary by the pixel size. Normalize the boundary length by the specified normalized area. 100μm 2 When normalizing by, multiply the boundary length by 100μm 2 / field of view area for normalization. The normalized boundary length is a feature quantity of the organization indicating the density of the boundary.

[0025] <Display Screens of S2 and S3> The display screen used for measuring the filling rate and the normalized boundary length is shown in Fig. 4. First, the SEM image is displayed, and the parameters necessary for binarization are set. The parameters necessary for binarization are the parameters for intensity normalization and the binarization threshold. When parameters are set and "Execute" is clicked, a binary image is created. The binary image is displayed, and threshing and polishing scratches are corrected (completion of S2). After correction, parameters required for tissue feature analysis are set. The parameters that need to be set are pixel size and normalized area. In the example of the figure, the pixel size is 0.44 μm and the normalized area is 100 μm 2 is. When "Execute" is clicked, tissue analysis is executed, and the boundary image, and the measurement results of the filling rate and the normalized boundary length are displayed (completion of S3). In the example of the figure, the filling rate is 0.55 and the normalized boundary length is 6.21.

[0026] <S4. Tissue Feature Output Step> Fig. 5 shows an output example of tissue feature analysis. The analysis results of the tissue features in the input binary image are summarized and output in a single row. It is in a format that allows a set of binary images to be input, the tissue features to be analyzed by batch processing, and output as a set of tissue features. The numerical values are the same as those of the boundary image in Fig. 4.

[0027] According to this embodiment, it is possible to provide a method for measuring the normalized boundary length, which is an appropriate tissue feature of the composite material tissue, improve the accuracy and efficiency of tissue feature analysis, and accelerate the research and development of improving element characteristics by tissue control.

Example

[0028] Example 2 is an example in which, in addition to the tissue feature analysis of Example 1, the field of view size corresponding to the purpose of tissue feature analysis is analyzed. When the analyzed tissue features are used for the analysis of the relationship between process - tissue feature - element performance, the tissue features need to be macroscopic average information. The field of view size required to obtain macroscopic average information varies depending on the analysis accuracy required for the tissue features and the non - uniformity of the material tissue.

[0029] Fig. 6 shows a flowchart of the field of view size analysis step for analyzing the field of view size corresponding to the purpose of tissue feature analysis. First, prepare two sample surfaces with differences in the macroscopic average information of tissue characteristics. The difference in tissue characteristics between the first sample surface and the second sample surface is defined as the analysis accuracy required for tissue characteristics. For example, set the upper surface of the sample perpendicular to the sedimentation direction as the first sample surface, and the lower surface of the sample perpendicular to the sedimentation direction as the second sample surface. Take multiple images of the first sample surface, and input the first image set obtained by binarizing the observation images, and take multiple images of the second sample surface, and input the second image set obtained by binarizing the observation images (S51). Set the field size of the image set for field size analysis as wide as possible within the experimental range. Input the field size to be cut out from the image set (S52). Create a third image set cut out from the first image set and a fourth image set cut out from the second image set (S53). Create a first set of tissue characteristics analyzed by the third image set and a second set of tissue characteristics analyzed by the fourth image set (S54). As tissue characteristics, not only the normalized boundary length and filling rate, but also the average particle size, the most frequent particle size, etc. may be selected. Next, calculate the probability that a difference occurs between the average value of the first set of tissue characteristics and the average value of the second set of tissue characteristics (S55).

[0030] Here, the details of the calculation method of the probability that a difference in the average value occurs will be explained. There is a t-test as a method for examining whether the difference in the average values of two groups is within the range of random error. The t-test is a method used when the number of samples is small. Figure 7A shows the t-distribution p(t) with different numbers of observations. It is a distribution with a shape very similar to the standard normal distribution, but as the number of observations decreases, the distribution becomes broader and the width of the 95% confidence interval increases. That is, even if the difference in the average value is large, it is judged to be within the range of random error. The difference T in the average value is calculated by the following formula.

[0031]

Equation

[0032] This formula is used when there is no correspondence to the data and the variances of the two groups are different. Use T and p(t) to calculate both sides of P(T<=t) (Figure 7B). Both sides of P(T<=t) correspond to the probability that the difference T in the mean value occurs. When both sides of P(T<=t) are less than or equal to the significance level of 5%, it is determined that the difference in the mean is not a random error but a significant difference.

[0033] Next, the process of estimating the field of view size at which the probability of the difference in the mean becomes 5% (S56) will be described. Save the field of view size and both sides of P(T<=t) calculated in S55 and plot them on the graph shown in Figure 8. If both sides of P(T<=t) are greater than 5%, expand the field of view size, and if they are smaller, reduce the field of view size to calculate both sides of P(T<=t) (S57, S53, S54, S55). By repeating the above process, a graph (Figure 8) showing the relationship between the field of view size and both sides of P(T<=t) is created. Estimate the field of view size at which both sides of P(T<=t) become 5% from the graph.

[0034] After the estimation, output the relationship between the field of view size and both sides of P(T<=t) (S58).

[0035] In this case, the field of view size at which the significance level becomes 5% was estimated. However, if the difference in the tissue characteristics between the first sample surface and the second sample surface used in the field of view size analysis process is large, it may be set to a probability smaller than 5%, and if the difference is small, it may be set to a probability larger than 5%. Also, when the number of samples is large, a z-test may be used. Adopt a method that can test the significance of the difference in the mean values of the tissue characteristics between the first sample surface and the second sample surface, and estimate the required field of view size.

[0036] According to this embodiment, the analysis accuracy of the tissue characteristics is specified by the difference in the tissue characteristics between the first sample surface and the second sample surface, and the field of view size required to analyze the difference in the tissue characteristics at the significance level is estimated. By analyzing with the required field of view size, the analyzed tissue characteristics can be treated as macroscopic average information. Also, if the field of view size is too large compared to the required size, the T.A.T. of the analysis will decrease. By analyzing the tissue characteristics with the required field of view size, the efficiency of the tissue characteristic analysis can be improved.

Example

[0037] Example 3 is an example of an analysis device for composite material structure. Fig. 9 shows the system configuration of the analysis device for composite material structure of Example 3. The analysis system is composed of one or more user terminals 602 and an analysis device 600 for composite material structure.

[0038] The analysis device 600 for composite material structure is composed of an information processing device, for example, a personal computer or a general-purpose computer. The analysis device 600 includes a processor 604 such as a CPU, a storage unit 606, a user interface 608 (user I / F in the figure), a network interface 610 (network I / F in the figure), and an internal network connecting these components.

[0039] The processor 604 can execute the program stored in the storage unit 606. As an example of the processor, a CPU or a GPU can be considered, but other semiconductor devices may also be used as long as they are the main body for executing predetermined processing. The storage unit 606 stores the program to be executed by the processor 604 and various information used by this program. In this example, the storage unit 606 stores the analysis program 612 for composite material structure and stores various information in the database 618. The storage unit 606 may be, for example, a semiconductor memory, a flash memory, an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc., and may be a volatile type memory or a non-volatile type memory.

[0040] The analysis program 612 is a program for analyzing the composite material structure, and is composed of a tissue feature analysis program 614 and a field of view size analysis program 616. The tissue feature analysis program 614 is a program for analyzing tissue features such as the normalized boundary length by the method of Example 1. The field of view size analysis program 616 is a program for analyzing the optimal field of view size by the method of Example 2.

[0041] The user interface 608 is, for example, a touch panel, a display, a keyboard, a mouse, etc. However, as long as it can receive operations from an operator (user) and display information, it may be other devices. The user interface 608 may be composed of these multiple devices.

[0042] The network interface 610 is an interface for communicating with external devices (for example, the user terminal 602, etc.) via a network.

[0043] The user terminal 602 is, for example, a computer used by a user who utilizes the analysis device 600. The user terminal 602 has a processor, a memory, and an interface (IF) for input and output to the user. The user terminal 602 accesses, for example, the analysis device 600 and transmits input data via a menu screen. The conditions input by the user are stored in the storage unit 606, and based on the stored data, the analysis program 612 calculates tissue features such as the filling rate and the normalized boundary length, and transmits the results to the user terminal 602. Thereby, the user can view the calculation results of tissue features such as the normalized boundary length.

[0044] FIG. 10 shows a functional block configuration diagram of the processing executed by the analysis device of FIG. 9. The functional block configuration diagram of FIG. 10 is divided into a tissue feature analysis program 614 and a field of view size analysis program 616. The tissue feature analysis program 614 corresponds to the flow of tissue feature analysis in FIG. 1, and the field of view size analysis program 616 corresponds to the flow of field of view size analysis in FIG. 6 of Example 2.

[0045] As shown in FIG. 10, the tissue feature analysis program 614 has an image input unit 702, a binary image creation unit 704, a boundary image creation unit 706, a normalized boundary length measurement unit 708, and a tissue feature output unit 710. The image input unit 702 inputs an observation image of a composite material composed of a light-emitting material and a non-light-emitting material. The binary image creation unit 704 divides the observation image into regions and creates a binary image. The boundary image creation unit 706 creates a boundary image in which the boundary in the binary image is positive and the rest is negative. The normalized boundary length measurement unit 708 measures the total number of pixels of the boundary in the boundary image, converts it to the boundary length by dividing by the pixel size, and measures the normalized boundary length obtained by normalizing the boundary length by the ratio of the imaging area to the specified area. The tissue feature output unit 710 outputs the normalized boundary length which is the analyzed tissue feature.

[0046] The field of view size analysis program 616 has a field of view size analysis unit 720. The field of view size analysis unit 720 estimates the field of view size necessary to obtain macroscopic average information.

[0047] The composite material tissue analysis apparatus can be realized by a processor such as a CPU loading a predetermined program onto a memory in a computer, and the processor executing the predetermined program loaded onto the memory. This predetermined program may be loaded onto the memory from a storage unit. For example, it may be directly loaded onto the memory from a storage medium storing the program via a user interface, or input from a network via a network interface and then loaded onto the memory after being temporarily stored in an external storage device. The processor and the storage unit may be configured on the cloud.

[0048] The program invention in the present invention is a program that is incorporated into a computer in this way and operates the computer as a composite material tissue analysis apparatus. By incorporating the program of the present invention into a computer, the composite material tissue analysis apparatus shown in the block diagrams of FIGS. 9 and 10 is configured.

[0049] According to the present embodiment, by measuring the normalized boundary length using a boundary image created from a binary image, an analysis apparatus for appropriate tissue characteristics of a composite material structure can be provided. In addition, an analysis apparatus for the field of view size necessary for obtaining macroscopic average information can be provided. Moreover, the accuracy and efficiency of tissue characteristic analysis can be improved, and research and development for improving element characteristics by tissue control can be accelerated.

[0050] In each of the above-described embodiments, the resin scintillator has been described as the analysis target, but the present invention can also be used for other composite materials.

[0051] Note that the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can also be added to the configuration of one embodiment. Further, for a part of the configuration of each embodiment, addition, deletion, or replacement with other configurations is possible.

Explanation of Reference Numerals

[0052] 1... Scintillator powder 2... Epoxy resin 31... Threshed region 32... Region with polishing scratches 4... Boundary between scintillator powder and resin 600... Analysis apparatus 602... User terminal 604... Processor 606... Storage unit 608... User interface 610... Network interface 612... Analysis program 614... Tissue characteristic analysis program 616... Field of view size analysis program 618... Database 702... Image input unit 704... Binary image creation unit 706... Boundary image creation unit 708... Normalized boundary length measurement unit 710... Tissue feature output unit 720 Field of view size analysis unit

Claims

1. A method for analyzing the structure of a composite material using an information processing apparatus, comprising: an image input step of inputting an observation image of the composite material; a binary image creation step of dividing the observation image into regions to create a binary image; a tissue feature analysis step of analyzing tissue features from the binary image; a tissue feature output step of outputting the analyzed tissue features, wherein the tissue feature analysis step includes a boundary image creation step of creating a boundary image in which boundaries in the binary image are positive and the rest are negative; a normalized boundary length measurement step of measuring the total number of pixels of the boundaries in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length; and the composite material is a composite material composed of a luminescent material and a non-luminescent material, and the composite material composed of the luminescent material and the non-luminescent material is a resin scintillator. A method for analyzing the structure of a composite material, characterized by this.

2. A method for analyzing the structure of a composite material using an information processing apparatus, comprising: an image input step of inputting an observation image of the composite material; a binary image creation step of dividing the observation image into regions to create a binary image; a tissue feature analysis step of analyzing tissue features from the binary image; a tissue feature output step of outputting the analyzed tissue features, wherein the tissue feature analysis step includes a boundary image creation step of creating a boundary image in which boundaries in the binary image are positive and the rest are negative; a normalized boundary length measurement step of measuring the total number of pixels of the boundaries in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length; and a field of view size analysis step of estimating the field of view size of the observation image necessary for analyzing the difference in tissue features between the first sample surface and the second sample surface at a significance level. A method for analyzing the structure of a composite material, characterized by this.

3. In the method for analyzing the structure of a composite material according to Claim 2, the field of view size analysis step includes a step of inputting a first set of images obtained by photographing a plurality of first sample surfaces and binarizing the observation images, and a second set of images obtained by photographing a plurality of second sample surfaces and binarizing the observation images; a step of inputting a field of view size to be cut out from the set of images; a step of creating a third set of images cut out from the first set of images and a fourth set of images cut out from the second set of images; a step of creating a first set of tissue features analyzed from the third set of images and a second set of tissue features analyzed from the fourth set of images; A step of calculating the probability of occurrence of a difference between the average value of the first set of tissue features and the average value of the second set of tissue features; A step of estimating the field of view size required for the probability of occurrence of the difference to reach the significance level; A step of outputting the relationship between the probability of occurrence of the difference and the field of view size; A method for analyzing a composite material structure, characterized by including the above steps.

4. In the method for analyzing a composite material structure according to Claim 3, In the step of estimating the field of view size required for the probability of occurrence of the difference to reach the significance level, a t-test or a z-test is used. A method for analyzing a composite material structure characterized by this.

5. An apparatus for analyzing a composite material structure, An image input unit for inputting an observation image of a composite material; A binary image creation unit for dividing the observation image into regions and creating a binary image; A tissue feature analysis unit for analyzing tissue features from the binary image; Including a tissue feature output unit for outputting the analyzed tissue features, The tissue feature analysis unit, A boundary image creation unit for creating a boundary image in which the boundaries in the binary image are positive and the rest are negative; A normalized boundary length measurement unit for measuring the total number of pixels of the boundary in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length; Including The composite material is a composite material composed of a light-emitting material and a non-light-emitting material, The composite material composed of the light-emitting material and the non-light-emitting material is a resin scintillator. An apparatus for analyzing a composite material structure characterized by this.

6. An apparatus for analyzing a composite material structure, An image input unit for inputting an observation image of a composite material; A binary image creation unit for dividing the observation image into regions and creating a binary image; A tissue feature analysis unit for analyzing tissue features from the binary image; Including a tissue feature output unit for outputting the analyzed tissue features, The tissue feature analysis unit, A boundary image creation unit for creating a boundary image in which the boundaries in the binary image are positive and the rest are negative; A normalized boundary length measurement unit for measuring the total number of pixels of the boundary in the boundary image, converting it to a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length; Including An apparatus for analyzing a composite material structure, characterized by comprising a field of view size analysis unit for estimating the field of view size of the observation image required for analyzing the difference in tissue features between the first sample surface and the second sample surface at the significance level.

7. In the apparatus for analyzing a composite material structure according to Claim 6, The field of view size analysis unit, Input a first set of images obtained by binarizing observation images of a plurality of first sample surfaces and a second set of images obtained by binarizing observation images of a plurality of second sample surfaces. Input the field size to be cut out from the image set. Create a third set of images cut out from the first set of images and a fourth set of images cut out from the second set of images. Create a first set of tissue features analyzed from the third set of images and a second set of tissue features analyzed from the fourth set of images. Calculate the probability that a difference occurs between the average value of the first set of tissue features and the average value of the second set of tissue features. Estimate the field size required for the probability of the difference occurring to reach the significance level. Output the relationship between the probability of the difference occurring and the field size. An analyzer for composite material structures, characterized by the above. [

8. ] In the analyzer for composite material structures according to claim 7, When estimating the field size required for the probability of the difference occurring to be significant, a t-test or a z-test is used. An analyzer for composite material structures, characterized by the above. [

9. ] A program that causes a computer to function as an analyzer for composite material structures, An image input unit for inputting observation images of the composite material, A binary image creation unit for dividing the observation image into regions to create a binary image, A boundary image creation unit for creating a boundary image in which the boundaries in the binary image are positive and the rest are negative, A normalized boundary length measurement unit that measures the total number of pixels of the boundaries in the boundary image, converts it to a boundary length by dividing by the pixel size, and normalizes the boundary length by the ratio of the imaging area to the specified area. A tissue feature output unit that outputs the measured normalized boundary length, and A program that causes it to function as a field size analysis unit that estimates the field size of the observation image required to analyze the difference in tissue features between the first sample surface and the second sample surface at the significance level. [

10. ] In the program according to claim 9, As the field size analysis unit, Input a first set of images obtained by binarizing observation images of a plurality of first sample surfaces and a second set of images obtained by binarizing observation images of a plurality of second sample surfaces. Input the field size to be cut out from the image set. Create a third set of images cut out from the first set of images and a fourth set of images cut out from the second set of images. Create a first set of tissue features analyzed from the third set of images and a second set of tissue features analyzed from the fourth set of images. Calculate the probability that a difference occurs between the average value of the first set of tissue features and the average value of the second set of tissue features, estimate the field of view size required for the probability that a difference occurs to reach the significance level, and output the relationship between the probability that a difference occurs and the field of view size, a program characterized by the above.

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