Composite material texture analysis method and analyzer

Abstract

To provide an appropriate texture feature analysis method and an analyzer for composite material texture.SOLUTION: A composite material texture analysis method using an information processing device includes: an image input step (S1) of inputting an observation image of a composite material; a binary image creation step (S2) of dividing the observation image into regions and creating a binary image; a texture feature analysis step (S3) of analyzing a texture feature from the binary image; and a texture feature output step (S4) of outputting the analyzed texture feature. The texture feature analysis step (S3) further includes: a boundary image creation step (S31) of 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) of 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 a ratio of an imaged area to a designated area. The composite material contains Gd2O2S.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] The performance of a light-emitting element made of luminescent and non-luminescent materials depends not only on the properties of each material but also on the structure of the light-emitting element. Taking resin scintillators as an example, the performance of resin scintillators, which are made by powdering ceramic scintillators, mixing them with resin, and then curing the resin to form a mold, depends not only on the material properties of the powder and resin, but also on the structure of the resin scintillator. We investigated technologies that improve performance by controlling the structure of resin scintillators. We also investigated technologies that improve performance by controlling the structure of light-emitting elements other than resin scintillators.

[0003] Patent Document 1 describes a bulk ceramic composite for light conversion that has 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 solidified crystalline phase that emits fluorescence. 2 The patent describes a ceramic composite for light conversion, characterized in that the interface length between the oxide phases is 150 mm or more and 1500 mm or less per 1000 mm. (See claims).

[0004] Patent Document 2 describes "an X-ray detecting body obtained by kneading and curing a scintillator powder and a light-transmitting resin, characterized in that the filling rate of the scintillator powder changes almost continuously in the thickness direction of the X-ray detecting body, the filling rate A on the photodetector side that converts light into electricity is greater than the filling rate B on the opposite side, and the ratio A / B of the filling rate A to the filling rate B is 1.1 or more and 5.0 or less" (see claims). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] International Publication No. 2008 / 041566 [Patent Document 2] Japanese Patent Application Laid-Open No. 2006-266936 Summary of the Invention [Problem to be solved by the invention]

[0006] We are analyzing the relationship between process, structural characteristics, and device performance, and are conducting research and development to improve device performance through structural control.

[0007] Patent Document 1 describes that controlling the interface length is important for obtaining stronger fluorescence from a ceramic composite for light conversion at the same thickness. The interface length is defined as the length of the interface observed as a boundary line when the structure of the ceramic composite for light conversion is observed in a plane. However, the method for measuring the interface length is not described.

[0008] Patent Document 2 describes that controlling the packing ratio of scintillator powder is important for reducing the amount of scintillator powder containing heavy metals used without significantly reducing the luminescence intensity of the detected body. The packing ratio of scintillator powder is measured by observing the detected body tissue with a microscope or the like, and expressing the area of scintillator powder in the observed area as a percentage. The document describes that the area percentage of scintillator powder in the observed area can be calculated by drawing vertical and horizontal grid lines on a micrograph of the detected body, counting the grids containing scintillator powder, and then calculating the ratio. However, the boundary length is not considered as a tissue characteristic.

[0009] The microstructure features depend not only on the definition of the microstructure features but also on the measurement method and the field of view size of the observed image. If the measurement method and field of view size are not specified, the microstructure features cannot be treated as macroscopic average information, and cannot be used to analyze the relationship between process, microstructure features, and device performance.

[0010] The present invention aims to provide a method for analyzing appropriate structural characteristics of composite structures. [Means for solving the problem]

[0011] An example of the "method for analyzing composite material structure" of the present invention for solving the above-mentioned problems is a method for analyzing composite material structure using an information processing device, which comprises: an image input step of inputting an observation image of the composite material; a binary image creation step of creating a binary image by dividing the observed image into regions; a tissue feature analysis step of analyzing tissue features from the binary image; a tissue feature output step of outputting the analyzed tissue features, The tissue characteristic 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 other is negative; a normalized boundary length measurement step of measuring the total number of pixels of the boundary in the boundary image, dividing the total number of pixels by the pixel size to convert the result into a boundary length, and normalizing the boundary length by the ratio of the photographed area to the specified area to measure a normalized boundary length; The present invention is characterized in that it comprises:

[0012] Furthermore, an example of the "composite material structure analysis device" of the present invention is an apparatus for analyzing a composite material structure, an image input unit for inputting an observed image of the composite material; a binary image creating unit that creates a binary image by dividing the observed image into regions; a tissue feature analysis unit that analyzes tissue features from the binary image; a tissue feature output unit that outputs the analyzed tissue features, The tissue characteristic analysis unit a boundary image creating unit that creates a boundary image in which the boundary in the binary image is positive and the other is negative; a normalized boundary length measurement unit that measures the total number of pixels of the boundary in the boundary image, divides the number by the pixel size to convert it into a boundary length, and normalizes the boundary length by the ratio of the captured area to the specified area to measure a normalized boundary length; The present invention is characterized in that it comprises: [Effects of the Invention]

[0013] According to the present invention, a method for analyzing appropriate structural characteristics of a composite structure can be provided by measuring the normalized boundary length using a boundary image created from a binary image. The present invention improves the accuracy and efficiency of structural feature analysis, accelerating research and development into improving device characteristics through structural control.

[0014] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a flowchart for measuring a normalized boundary length as a texture feature in the first embodiment. [Figure 2A] 1 shows SEM images illustrating the criteria for correcting binary images. [Figure 2B] 1 shows a binary image before correction, illustrating the criteria for correcting the binary image. [Figure 2C] 1 shows a corrected binary image illustrating the criteria for correcting the binary image. [Figure 3A] FIG. 1 is an explanatory diagram showing the relationship between a binary image and a boundary image, and shows a binary image. [Figure 3B] FIG. 1 is an explanatory diagram showing the relationship between a binary image and a boundary image, and shows a boundary image. [Figure 4] The display screens for the SEM image, binary image, and boundary image used for the texture characterization are shown. [Figure 5] An example of the analysis results of tissue characteristics is shown below. [Figure 6] 10 is a flowchart showing the visual field size analysis in Example 2. [Figure 7A] This is an explanatory diagram of the t-test, showing the change in the t-distribution when the number of observations is changed. [Figure 7B] This is an explanatory diagram of the t-test, showing the areas on both sides of P(T<=t) for the difference in mean values T. [Figure 8] FIG. 10 is a diagram showing an example of calculation of the relationship between the field of view size and P(T<=t) on both sides. [Figure 9] FIG. 10 is a diagram showing an apparatus for analyzing the structure of a composite material according to a third embodiment. [Figure 10] FIG. 10 is a functional block diagram of a composite structure analysis device according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention should not be interpreted as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed within the scope of the idea or gist of the present invention. In addition, in the configuration of the invention described below, the same parts or parts having similar functions are denoted by the same reference numerals in different drawings, and redundant explanations may be omitted. The terms "first," "second," "third," etc. used in this specification are used to identify components and do not necessarily limit the number, order, or content thereof. Furthermore, numbers used to identify components are used in different contexts, and numbers used in one context do not necessarily indicate the same configuration in another context. Furthermore, a component identified by a certain number may not function as a component identified by another number. In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings etc. may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings etc. [Example]

[0017] In Example 1, a resin scintillator is taken as the analysis target, 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. Hereinafter, the details of each step will be described.

[0019] <S1. Image input step> The observation image of the resin scintillator is obtained by a scanning electron microscope ( S canning E lectron MIt is photographed with a (microscope). To take a SEM image, the sample cross-section is exposed. If there are irregularities on the cross-section to be photographed, image contrast depending on the irregularities is observed in the SEM image, so the surface to be photographed is processed to be flat by polishing. A focused ion beam processing apparatus or a cross-section polishing 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 by an ion sputtering apparatus for antistatic purposes, and then the observation image was taken. The taken image is input into the composite material structure analysis system. Note that the input image is not limited to the SEM image. Images taken with other imaging apparatuses such as an optical microscope or a probe microscope may also be used.

[0020] <S2. Binary Image Creation Step> The input observation image is regionally divided into the scintillator powder and the resin. After normalizing the intensity of the SEM image, it is binarized by the threshold method for regional division. To normalize the intensity of the SEM image, an intensity histogram of the SEM image is created, and the most frequent value of the image intensity of the scintillator powder and the most frequent value of the image intensity of the resin are specified. The image intensity is converted so that each most frequent value becomes the specified value. For example, after normalizing so that the most frequent value of the image intensity of the scintillator powder is 200 and the most frequent value of the image intensity of the resin is 60, it is binarized with a threshold of 130. An example of a 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, when there are irregularities on the sample surface, secondary electrons increase from the side walls. It was judged that there were surface irregularities in the region with high SEM image intensity, and it was corrected with the paint function while referring to the SEM image. Fig. 2C shows the image after correcting the particle removal and polishing scratches.

[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 included 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 process 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 boundary pixels in the boundary image, converting it to a boundary length by dividing by the pixel size, and measuring the 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 pixels of the outermost periphery 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 positive and the other pixels are negative. Note that the same processing is executed if an image in which the pixels of boundary 4 are negative and the other pixels are positive is used and the pixels with a negative label are treated as the boundary.

[0024] <S32. Normalized Boundary Length Measurement Step> First, the number of boundary pixels included in the boundary image is counted. The boundary length is measured by dividing the number of boundary pixels by the pixel size. The boundary length is normalized by the specified normalized area. When normalized with 100 μm 2 the boundary length is multiplied by 100 μm 2 / the field of view area for normalization. The normalized boundary length is an organizational feature quantity indicating the density of the boundary.

[0025] <Display Screens for 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 Run is clicked, a binary image is created. The binary image is displayed, and threshing and polishing scratches are corrected (S2 completed). After correction, parameters necessary for tissue feature analysis are set. The parameters that need to be set are the pixel size and the 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 Run 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 (S3 completed). 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 line. It is in a format that allows a binary image set to be input, the tissue features to be analyzed by batch processing, and output as a tissue feature set. The numerical values are the same as those of the boundary image in Fig. 4.

[0027] According to this embodiment, a method for measuring the normalized boundary length, which is an appropriate tissue feature of the composite material tissue, can be provided, improving the accuracy and efficiency of tissue feature analysis, and accelerating 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, two sample surfaces are prepared that have different macroscopic average information of the structural features. The difference in the structural features between the first and second sample surfaces is set as the analytical accuracy required for the structural features. For example, the top surface of the sample perpendicular to the sedimentation direction is set as the first sample surface, and the bottom surface of the sample perpendicular to the sedimentation direction is set as the second sample surface. A first image set is obtained by taking multiple images of the first sample surface and binarizing the observed images, and a second image set is obtained by taking multiple images of the second sample surface and binarizing the observed images (S51). The field of view size of the image set for field of view size analysis is set as wide as possible within the experimentally feasible range. The field of view size to be cut out from the image set is input (S52). A third image set is created by extracting the first image set, and a fourth image set is created by extracting the second image set (S53). A first set of texture features analyzed using the third image set and a second set of texture features analyzed using the fourth image set are created (S54). As texture features, in addition to the normalized boundary length and packing fraction, mean grain size, mode grain size, etc. may also be selected. Next, the probability of occurrence of a difference between the mean value of the first tissue feature set and the mean value of the second tissue feature set is calculated (S55).

[0030] Here, we will explain in detail how to calculate the probability of a difference in mean values occurring. The t-test is a method for determining whether the difference in mean values between two groups is within the range of random error. The t-test is a method used when the sample size is small. Figure 7A shows the t-distribution p(t) for different numbers of observations. The distribution is 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. In other words, even if the difference in mean values is large, it is determined to be within the range of random error. The difference in mean values T is calculated using the following formula.

[0031]

number

[0032] This formula is used when the data are unpaired and the variances of the two groups are different. P(T<=t) (two-sided) is calculated using T and p(t) (Figure 7B). P(T<=t) (two-sided) corresponds to the probability of the difference T in the means occurring. If P(T<=t) (two-sided) is at a significance level of 5% or less, the difference in the means is considered to be significant, not random error.

[0033] Next, we will explain the process (S56) of estimating the field of view size at which the probability of an average difference occurring is 5%. The field of view size and both sides of P(T<=t) calculated in S55 are saved and plotted on the graph shown in Figure 8. If both sides of P(T<=t) are greater than 5%, the field of view size is enlarged; if smaller, the field of view size is reduced and both sides of P(T<=t) are calculated (S57, S53, S54, S55). By repeating the above steps, a graph (Figure 8) is created showing the relationship between the field of view size and both sides of P(T<=t). From the graph, the field of view size at which both sides of P(T<=t) are 5% is estimated.

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

[0035] In this study, we estimated the field of view size at a significance level of 5%, but if the difference in the structural features between the first and second sample surfaces used in the field of view size analysis process is large, we can set the probability to less than 5%, or if the difference is small, we can set the probability to greater than 5%. Furthermore, if the number of samples is large, we can also use a z-test. We estimate the required field of view size by employing a method that can test the significance of the difference in the average values of the structural features between the first and second sample surfaces.

[0036] According to this embodiment, the analysis accuracy of the structural features is specified by the difference between the structural features on the first sample surface and the second sample surface, and the field of view size required to analyze the difference in the structural features at a significant level is estimated. By performing the analysis with the required field of view size, the analyzed structural features can be treated as macroscopic average information. Furthermore, if the field of view size is larger than the required size, the TAT of the analysis decreases. By analyzing the structural features with the required field of view size, the efficiency of the structural feature 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 composite structure analysis device 600 is configured as an information processing device, such as a personal computer or a general-purpose computer, and includes a processor 604 such as a CPU, a storage unit 606, a user interface 608 (referred to as a user I / F in the figure), a network interface 610 (referred to as a network I / F in the figure), and an internal network that connects these components.

[0039] The processor 604 can execute a program stored in the storage unit 606. A CPU or a GPU is an example of a processor, but other semiconductor devices may be used as long as they are capable of executing predetermined processes. The storage unit 606 stores the program to be executed by the processor 604 and various information used by the program. In this embodiment, the storage unit 606 stores a composite material structure analysis program 612 and stores various information in a 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), or the like, and may be either a volatile memory or a non-volatile memory.

[0040] The analysis program 612 is a program for executing an analysis of the composite material structure, and is composed of a structure characteristic analysis program 614 and a field size analysis program 616. The structure characteristic analysis program 614 is a program for analyzing structure characteristics such as normalized boundary length using the method of Example 1. The field size analysis program 616 is a program for analyzing the optimal field size using the method of Example 2.

[0041] The user interface 608 may be, for example, a touch panel, a display, a keyboard, a mouse, or any other device that can accept operations from an operator (user) and display information. The user interface 608 may be configured from a plurality of these devices.

[0042] The network interface 610 is an interface for communicating with an external device (such as the user terminal 602) via a network.

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

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

[0045] As shown in FIG. 10, the tissue characteristic 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 characteristic output unit 710. The image input unit 702 inputs an observation image of a composite material made of a luminescent material and a non-luminescent material. The binary image creation unit 704 creates a binary image by dividing the observed image into regions. The boundary image creating 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, divides by the pixel size to convert it into a boundary length, and measures the normalized boundary length by normalizing the boundary length by the ratio of the captured area to the specified area. The tissue feature output unit 710 outputs the normalized boundary length, which is the analyzed tissue feature.

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

[0047] The composite microstructure analysis device can be realized by a processor such as a CPU loading a predetermined program into memory in a computer (calculator), and then the processor executing the predetermined program loaded into memory. This predetermined program can be loaded into memory from a storage unit. For example, the program can be input via a user interface from a storage medium on which the program is stored, or from a network via a network interface and loaded directly into memory, or it can be stored in an external storage device and then loaded into memory. The processor and storage unit may be configured on a cloud.

[0048] The program of the present invention is thus incorporated into a computer and causes the computer to operate as an apparatus for analyzing the microstructure of a composite material. By incorporating the program of the present invention into a computer, an apparatus for analyzing the microstructure of a composite material shown in the block diagrams of Figures 9 and 10 is configured.

[0049] According to this embodiment, by measuring the normalized boundary length using a boundary image created from a binary image, it is possible to provide an analysis device for appropriate structural characteristics of composite structures. It is also possible to provide an analysis device with a field of view size necessary to obtain macroscopic average information. This improves the accuracy and efficiency of structural characteristic analysis, accelerating research and development into improving device characteristics through structural control.

[0050] In the above-described embodiments, a resin scintillator was used as the object of analysis, but the present invention can also be applied to other composite materials.

[0051] 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 to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0052] 1...Scintillator powder 2...Epoxy resin 31...Shedding area 32...Area with polishing scratches 4...Boundary between scintillator powder and resin 600…Analysis equipment 602...User terminal 604...Processor 606...Storage section 608...User Interface 610...Network interface 612...Analysis program 614...Tissue Characterization 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 section 710...Tissue characteristic output unit 720 field of view size analysis unit

Claims

1. A method for analyzing a composite structure using an information processing device, comprising: an image input step of inputting an observation image of the composite material; a binary image creation step of creating a binary image by dividing the observed image into regions; a tissue feature analysis step of analyzing tissue features from the binary image; a tissue feature output step of outputting the analyzed tissue features, The tissue characteristic 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 other is negative; a normalized boundary length measurement step of measuring the total number of pixels of the boundary in the boundary image, dividing the total number of pixels by the pixel size to convert the result into a boundary length, and normalizing the boundary length by the ratio of the photographed area to the specified area to measure a normalized boundary length; Including, The composite material is Gd ２ O ２ A method for analyzing a composite structure, comprising:

2. The method for analyzing a composite material structure according to claim 1, The method for analyzing the structure of a composite material, wherein the composite material absorbs radiation energy and emits light.

3. The method for analyzing a composite material structure according to claim 1, further comprising: A method for analyzing a composite material structure, comprising a field of view size analysis step of estimating the field of view size of the observed image required to analyze the difference in structural characteristics between the first sample surface and the second sample surface at a significant level.

4. The method for analyzing a composite material structure according to claim 3, The visual field size analysis step includes: a step of inputting a first image set obtained by photographing a plurality of images of a first sample surface and binarizing the observed images, and a second image set obtained by photographing a plurality of images of a second sample surface and binarizing the observed images; inputting a field of view size to be cropped from the image set; creating a third set of images cropped from the first set of images and a fourth set of images cropped from the second set of images; generating 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; calculating the probability of a difference occurring between the mean value of the first set of tissue features and the mean value of the second set of tissue features; A step of estimating the field size required to achieve a significant level of probability of a difference occurring; outputting a relationship between the probability of difference occurrence and the field of view size; A method for analyzing a composite material structure, comprising:

5. The method for analyzing a composite material structure according to claim 4, A method for analyzing a composite material structure, characterized in that a t-test or a z-test is used in the step of estimating the field of view size required to bring the probability of occurrence of the difference to a significant level.

6. An apparatus for analyzing composite structure, an image input unit for inputting an observed image of the composite material; a binary image creating unit that creates a binary image by dividing the observed image into regions; a tissue feature analysis unit that analyzes tissue features from the binary image; a tissue feature output unit that outputs the analyzed tissue features, The tissue characteristic analysis unit a boundary image creating unit that creates a boundary image in which the boundary in the binary image is positive and the other is negative; a normalized boundary length measurement unit that measures the total number of pixels of the boundary in the boundary image, divides the number by the pixel size to convert it into a boundary length, and normalizes the boundary length by the ratio of the captured area to the specified area to measure a normalized boundary length; Including, The composite material is Gd ２ O ２ 1. A composite structure analysis device comprising:

7. The apparatus for analyzing a composite material structure according to claim 6, The composite structure analyzing device is characterized in that the composite absorbs radiation energy and emits light.

8. The apparatus for analyzing a composite material structure according to claim 6, further comprising: A composite material structure analysis device characterized by comprising a field of view size analysis unit that estimates the field of view size of the observed image required to analyze the difference in structure characteristics between the first sample surface and the second sample surface at a significance level.

9. The apparatus for analyzing a composite material structure according to claim 8, The visual field size analysis unit a first image set obtained by binarizing a plurality of observation images of a first sample surface and a second image set obtained by binarizing a plurality of observation images of a second sample surface; Enter the field of view size to be cropped from the image set. creating a third set of images cropped from the first set of images and a fourth set of images cropped from the second set of images; generating 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; calculating the probability of a difference occurring between the mean value of the first set of tissue features and the mean value of the second set of tissue features; Estimate the field size required for the probability of a difference occurring to reach a significant level, Output the relationship between the probability of difference and the field of view size. A composite structure analysis device characterized by:

10. The apparatus for analyzing a composite material structure according to claim 9, An apparatus for analyzing composite structure, characterized in that a t-test or a z-test is used to estimate the field of view size required for the probability of occurrence of the difference to reach a significant level.

Images