Method and system for analyzing internal structure of polymeric material

Abstract

To provide a method and a system for analyzing an internal structure of a polymeric material capable of more accurately grasping the internal structure of a polymeric material using image data acquired by an electron microscope.SOLUTION: In an analysis of an internal structure of a polymeric material, an image acquisition step S110 of acquiring original image data D1 of the polymeric material using an electron microscope 2 is performed, and a computing device 3 performs a preprocessing step S120 on the original image data D1 of the polymeric material acquired by the electron microscope 2. In the preprocessing step S120, the original image data D1 is subjected to noise removal and binarization to generate emphasized image data D4 in which a string-like structure P1 is emphasized as an analysis target. In an analysis step S130, a feature amount of the analysis target is calculated using the appropriate emphasized image data D4, and predetermined characteristics of the polymeric material are grasped on the basis of the feature amount.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method and system for analyzing the internal structure of a polymeric material, and more particularly to a method and system for analyzing the internal structure of a polymeric material that can more faithfully grasp the internal structure of the polymeric material using image data acquired by an electron microscope. [Background technology]

[0002] A method has been proposed for evaluating the network structure of crosslinked rubber by extracting and sorting voids in a network structure from a binarized processed image of a 3D or 2D image acquired by an electron microscope, and then using the median diameter of the inscribed sphere or inscribed circle obtained by approximating the voids to a sphere or circle (see Patent Document 1). However, even for inscribed spheres or inscribed circles with the same median diameter, the lengths of the molecular chains that make up the network differ, resulting in different network shapes. Therefore, there is room for improvement in more faithfully understanding the internal structure of polymer materials using image data acquired by an electron microscope. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-15022 Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present invention is to provide a method and system for analyzing the internal structure of a polymeric material that can more faithfully grasp the internal structure of the polymeric material using image data acquired by an electron microscope. [Means for solving the problem]

[0005] The method for analyzing the internal structure of a polymeric material of the present invention, which achieves the above-mentioned object, is a method for analyzing the internal structure of a polymeric material by a computing device using original image data of the polymeric material obtained by an electron microscope, and includes an image acquisition step of acquiring the original image data using the electron microscope, a preprocessing step of creating enhanced image data that emphasizes the analysis target in the analysis step from the original image data, and an analysis step of calculating feature amounts of the analysis target using the enhanced image data and grasping specified characteristics of the polymeric material based on the feature amounts, characterized in that the preprocessing step performs at least noise removal and binarization on the original image data to create the enhanced image data in which the string-like structure is emphasized as the analysis target.

[0006] The internal structure analysis system for polymeric materials of the present invention has a memory unit that stores original image data of polymeric materials acquired by an electron microscope, and is equipped with a computing device that analyzes the internal structure of the polymeric material using the original image data.The computing device executes a preprocessing step in which enhanced image data is created by emphasizing the analysis target in the analysis step using the original image data, and an analysis step in which the enhanced image data is used to calculate feature quantities of the analysis target and grasp specified characteristics of the polymeric material based on the feature quantities.The preprocessing step is characterized in that the enhanced image data is created by applying at least noise removal and binarization to the original image data, thereby emphasizing the string-like structure as the analysis target. [Effects of the Invention]

[0007] According to the present invention, the string-like structure is adopted as the analysis target, and the analysis step uses the enhanced image data in which the string-like structure is enhanced. The string-like structure can be roughly regarded as a molecular chain of the polymer material, and is therefore closely related to understanding the characteristics. Therefore, using the enhanced image data in which the string-like structure is enhanced is advantageous for more faithfully understanding the internal structure of the polymer material. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is an explanatory diagram illustrating an embodiment of a system for analyzing the internal structure of a polymer material; [Figure 2] FIG. 1 is a flowchart showing an example of the procedure of a method for analyzing the internal structure of a polymer material. [Figure 3] FIG. 1 is an explanatory diagram illustrating image data of a polymer material obtained by an electron microscope. [Figure 4] FIG. 4 is an explanatory diagram showing an enlarged view of a part of the image data in FIG. 3. [Figure 5] FIG. 10 is an explanatory diagram illustrating an example of noise-removed image data. [Figure 6] FIG. 10 is an explanatory diagram illustrating another example of noise-removed image data. [Figure 7] FIG. 10 is an explanatory diagram illustrating an example of processed image data. [Figure 8] FIG. 10 is an explanatory diagram illustrating an example of replaced image data. [Figure 9] FIG. 10 is an explanatory diagram illustrating an example of differential data. [Figure 10] FIG. 10 is an explanatory diagram illustrating an example of emphasized image data. [Figure 11] FIG. 10 is a flowchart showing a modified example of the procedure of the internal structure analysis method. [Figure 12] 10A and 10B are explanatory diagrams illustrating an example of a state in which each end point is detected in the highlighted image data. [Figure 13] FIG. 10 is an explanatory diagram illustrating a state in which fragmented string-like structures and non-string-like structures are bonded together. [Figure 14] 13 is an explanatory diagram illustrating an example of highlighted image data created based on the highlighted image data of FIG. 12. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, the method and system for analyzing the internal structure of a polymer material according to the present invention will be described based on the embodiments shown in the drawings.

[0010] An embodiment of an internal structure analysis system 1 (hereinafter referred to as analysis system 1) illustrated in Fig. 1 is used to carry out an internal structure analysis method for a polymer material illustrated in Fig. 2. In this analysis method, image data D1 (hereinafter referred to as original image data D1) of a polymer material illustrated in Fig. 3, acquired by an electron microscope 2, is subjected to noise removal, binarization, and thinning to generate processed image data D3 illustrated in Fig. 7, and enhanced image data D4 illustrated in Fig. 10 is created based on the generated processed image data D3. The internal structure of the polymer material is then analyzed using this enhanced image data D4.

[0011] An overview of the internal structure analysis method illustrated in FIG. 2 will be described. This internal structure analysis method includes an image acquisition step S110, a preprocessing step S120, and an analysis step S130, which are performed in sequence. In the image acquisition step S110, original image data D1 is acquired using an electron microscope 2. In the preprocessing step S120, the acquired original image data D1 is subjected to noise removal, binarization, and thinning (S121, S122) to generate processed image data D3. Non-string-like structures P2 are removed from the generated processed image data D3, and appropriate enhanced image data D4 is obtained in which the string-like structures P1 are emphasized as the analysis target (S123, S124). In the analysis step S130, feature quantities of the analysis target are calculated using the appropriate enhanced image data D4, and predetermined properties of the polymer material are determined based on these feature quantities. The string-like structures P1 that are emphasized in the emphasized image data D4 are specifically molecular chains that make up the mesh structure of the polymer material, and in the emphasized image data D4, non-string-like structures P2, which are structures other than the string-like structures, are removed as targets for removal, and the string-like structures P1 are emphasized.

[0012] Examples of polymeric materials include crosslinked rubber (vulcanized rubber), resin, and unvulcanized rubber. These polymeric materials may contain fillers. In this embodiment, crosslinked rubber is used as the polymeric material. This crosslinked rubber is made by crosslinking natural rubber (NR) or synthetic rubbers such as styrene-butadiene rubber (SBR), butadiene rubber (BR), and isoprene rubber (IR) with a crosslinking agent (vulcanizing agent) such as sulfur or an organic peroxide. This crosslinked rubber may contain fillers such as silica or carbon black, as well as additives such as vulcanization accelerators and antioxidants.

[0013] To obtain the original image data D1, an ultrathin section taken from a cross-linked rubber test piece is used as the sample S. The thickness of the sample S is, for example, 50 nm to 300 nm. The cross-linked rubber test piece is prepared by swelling the cross-linked rubber with a polymerizable monomer and then polymerizing the polymerizable monomer in the presence of a polymerization initiator, resulting in a saturated, swollen network structure that is fixed. The polymerizable monomer used to prepare the test piece may be any polymerizable monomer that can swell the cross-linked rubber to a saturated state and polymerize in the presence of the cross-linked rubber. Examples of the polymerizable monomer include methyl methacrylate, styrene monomer, and silicone. The polymerization initiator may be any polymerizable monomer that generates radicals by heat, light, or oxidation-reduction. Examples of the polymerization initiator include benzoyl peroxide, peroxide, and triethylborane. The sample S may also be stained with a stain. Examples of the stain include osmium tetroxide and iodine. When collecting sample S from the test piece, it is advisable to set the magnification of the electron microscope 2 lower than when acquiring the original image data D1, identify a position of the test piece that exhibits a representative cross-linked state, and collect sample S from that position.

[0014] The analysis system 1 includes a calculation device 3 to which original image data D1 of a sample S acquired by an electron microscope 2 is input. The calculation device 3 is configured as a computer, to which various data are input and stored, and which performs data processing using this data. Various known computers can be used for the calculation device 3. The calculation device 3 includes a calculation processing unit (CPU) 4, a main storage unit (memory) 5, and an auxiliary storage unit (e.g., HDD) 6. The auxiliary storage unit 6 stores the original image data D1.

[0015] Various known transmission electron microscopes can be used for the electron microscope 2. This electron microscope 2 irradiates the sample S with an electron beam and acquires original image data D1 from which the spatial distribution of electrons inside the sample S can be observed based on the intensity of the transmitted or diffracted electron beam. The original image data D1 acquired by the electron microscope 2 is input to the calculation device 3. In this embodiment, two-dimensional original image data D1 is acquired by the electron microscope 2 and used for analysis, but three-dimensional original image data D1 can also be acquired and used for analysis. The electron microscope 2 may acquire a bright-field image formed by a transmitted wave as the original image data D1, or a dark-field image formed by a diffracted wave.

[0016] In the analysis system 1, when original image data D1 acquired by an electron microscope 2 is input to a calculation device 3 and a predetermined program is started and executed by an input unit (keyboard, mouse, etc.) 7, the calculation device 3 executes each data processing instructed by the program. Then, the calculation device 3 outputs the analysis results obtained by executing each data processing to an output unit (display, etc.) 8.

[0017] The contents of each step (S110, S120, S130) illustrated in FIG. 2 will be described in detail below.

[0018] In the image acquisition step S110, original image data D1 of the sample P collected from a test piece of crosslinked rubber is acquired by the electron microscope 2. In this step, the acceleration voltage, magnification, etc. of the electron microscope 2 are adjusted to acquire a dark-field image, as the original image data D1, in which diffracted waves of molecular chains of the crosslinked rubber are imaged.

[0019] The original image data D1 shown in Figures 3 and 4 is obtained as a grayscale dark-field image of the object, which is a molecular chain of crosslinked rubber, using an electron microscope 2, and is stored in the auxiliary memory unit 6 of the computing device 3. Figure 4 shows an enlarged view of a portion of the original image data D1 in Figure 3. In this original image data D1, the white portion roughly represents the object image P, which represents the molecular chain of crosslinked rubber, and the black portion represents the background, i.e., the embedding resin (mesh portion) that fixes the network structure formed by the molecular chain of crosslinked rubber. Furthermore, in this original image data D1, part of the white portion represents noise N. Specifically, noise N is present within the white frame in Figure 4.

[0020] In step S121 of the pre-processing step S120, the arithmetic unit 3 performs data processing to remove noise from the original image data D1 to generate noise-removed image data D2. This noise removal method can be any of various known noise removal image processes. Examples of this noise removal image process include L1 regularization, L2 regularization, TV regularization, and Gaussian filtering. An appropriate method can be selected from these noise removal image processes depending on the occurrence of noise N in the original image data D1.

[0021] The noise-removed image data D2 shown in Fig. 5 is generated by performing noise-removal image processing using L1 regularization on the original image data D1 shown in Fig. 4. In this noise-removed image data D2, the pixel values ​​of pixels corresponding to the background are reduced by removing the noise N, and the boundary between the object image P and the background becomes clearer.

[0022] The noise-removed image data D2 shown in Fig. 6 is generated by performing noise-removal image processing using TV regularization on the original image data D1 shown in Fig. 4. In this noise-removed image data D2, minute noise N has been smoothed, making the boundary between the object image P and the background clearer.

[0023] In step S122, the arithmetic unit 3 performs data processing to binarize and thin the noise-removed image data D2 to generate processed image data D3 containing a large number of line images Pa. This binarization method can be any of various known binarization methods, such as Otsu's binarization (binarization using a threshold), an active contour model (binarization using region extraction), or a region separation model created by machine learning. Furthermore, this thinning method can be any of various known thinning methods, such as the Hilditch algorithm, Tamura's method, the Nagendraprasad-Wang-Gupta algorithm, or the Zhang-Suen algorithm.

[0024] The processed image data D3 shown in Fig. 7 is generated by binarizing and thinning the noise-removed image data D2. In Fig. 7, the white portion indicates the line image Pa. The line image Pa is represented by the center line of the object image P in the noise-removed image data D2, and the skeleton of the object image P is identified. In other words, the structural features of each object image P are more pronounced in the processed image data D3.

[0025] The numerous line images Pa present in this processed image data D3 can generally be regarded as molecular chains of crosslinked rubber. However, among the numerous line images Pa, some are unsuitable for analysis in the analysis step S130 described below, i.e., are inappropriate for use as the analysis target. Among the numerous line images Pa, the line images Pa suitable for analysis are string-like structures P1, while the inappropriate line images Pa unsuitable for analysis are structures other than the string-like structures P1 (hereinafter referred to as non-string-like structures P2). Because the processed image data D3 contains non-string-like structures P2, using the processed image data D3 as is makes it impossible to faithfully grasp the internal structure of the polymer material. Therefore, in this preprocessing step S120, the following steps (S123, S124) are performed to remove the non-string-like structures P2 from the processed image data D3, thereby creating enhanced image data D4 in which the string-like structures P1 suitable for analysis are emphasized as the analysis target.

[0026] The string-like structure P1 is clearly a molecular chain that constitutes the network structure of cross-linked rubber, and because this molecular chain is suitable for analyzing the properties of cross-linked rubber, it can be used as an analysis target. The non-string-like structure P2 is not clearly a molecular chain that constitutes the network structure of cross-linked rubber. This non-string-like structure P2 includes, for example, noise N that cannot be removed by noise removal, molecular chains extending in the depth direction of the paper, and images resulting from uneven dyeing when the sample S is stained. Therefore, the non-string-like structure P2 is inappropriate for use as an analysis target. "Inappropriate" means that it is largely unrelated to the properties of cross-linked rubber and is not suitable (unnecessary) for analyzing those properties. It is desirable to treat molecular chains extending in the depth direction of the paper as non-string-like structures P2 because the network structure formed by these molecular chains is unclear due to the thinness of the sample S used, even if the original image data D1 is three-dimensional.

[0027] Those skilled in the art can determine whether each line image Pa in the processed image data D3 is appropriate for use as an analysis target based on the large amount of data accumulated during the production and development of crosslinked rubber and polymer materials, as well as data described in various publicly known literature. Therefore, in this preprocessing step S120, the calculation device 3 can distinguish each line image Pa into a string-like structure P1 and a non-string-like structure P2 using such a standard of discrimination. For example, the processed image data D3 can be output to the output unit 9, and a threshold can be set as a criterion for visually distinguishing each line image Pa into a string-like structure P1 and a non-string-like structure P2. This threshold can be, for example, the extension length or occupied area of ​​the line image Pa that is deemed to be a non-string-like structure P2. The set threshold can then be input to the calculation device 3 via the input unit 7. Based on the input threshold, the calculation device 3 can distinguish each line image Pa into a string-like structure P1 and a non-string-like structure P2, and select and remove the non-string-like structure P2 from the processed image data D3.

[0028] In step S123, a threshold value is input to the calculation device 3 by the input unit 7. This threshold value varies depending on various conditions, such as the acquisition conditions of the original image data D1, such as the imaging magnification and imaging conditions of the electron microscope 2, and the generation conditions of the processed image data D3, and so may be changed as appropriate depending on these conditions. If the processed image data D3 is generated under the same conditions, that is, based on the original image data D1 acquired under the same acquisition conditions, the same threshold value can be used. Therefore, once a threshold value is set, it can be used under the same conditions, so step S123 can be omitted.

[0029] In step S124, the calculation device 3 performs data processing based on the input threshold value to remove non-string-like structures P2 from the processed image data D3 as removal targets, and create enhanced image data D4. Specifically, the calculation device 3 classifies each line image Pa of the processed image data D3 into string-like structures P1 and non-string-like structures P2 based on the input threshold value, and removes the distinguished non-string-like structures P2 from the processed image data D2. As a result, enhanced image data D4 is created in which only the string-like structures P1 are emphasized as the analysis target.

[0030] In steps S123 and S124, it is preferable to use a threshold region R as a threshold. This threshold region R is set as a region in which the non-string-like structure P2 fits but the string-like structure P1 protrudes (does not fit). Fitting within the threshold region R refers to a state in which the threshold region R surrounds the entire periphery of the target structure and the target structure fits in a directionally flexible manner. Fitting in a directionally flexible manner means that when the center position of the target structure is fixed and the target structure is rotated around that center as an axis, the target structure does not protrude at all from the region. The non-string-like structure P2 also includes, for example, a spiral structure whose extension length is long enough to be considered as the string-like structure P1. Such a structure would not be identified as a non-string-like structure P2 even if the extension length or occupied area of ​​the line image Pa, which can be considered as a non-string-like structure P2, is used as a threshold, but it can be identified as a non-string-like structure P2 by using the threshold region R. Therefore, using the threshold region R as a threshold is advantageous for more accurately identifying the non-string-like structure P2. This threshold region R is, for example, a square region made up of a collection of 9 to 36 pixels.

[0031] In step S124 using the threshold region R, replacement image data D5 shown in Fig. 8 is obtained based on the processed image data D3 shown in Fig. 7. Next, difference data D6 shown in Fig. 9 is obtained based on the difference between the processed image data D3 and the replacement image data D5. Next, enhanced image data D4 shown in Fig. 10 is created based on the difference between the processed image data D3 and the difference data D6.

[0032] 8, each pixel of the processed image data D3 is treated as a target pixel, and the pixel value of the target pixel is replaced with a representative value (average, mode, median, maximum, etc.) of the pixel values ​​of the many pixels surrounding the target pixel. The area surrounding the target pixel is set based on a threshold region R. For example, if the threshold region R is a square region containing nine pixels, the pixel located at the center of the square region is the target pixel, and the eight pixels adjacent to the central pixel are the pixels surrounding the target pixel.

[0033] In this replaced image data D5, there are many pixels whose pixel values ​​are greater than 0 among the target pixels corresponding to the string-like structure P1 and among the pixels surrounding the target pixels located in the vicinity of the string-like structure P1, and therefore the representative values ​​of these target pixels are greater than 0. As a result, the replaced image data D5 contains enlarged string-like structures P3, in which the string-like structure P1 has enlarged due to the replacement of pixel values.

[0034] In this replaced image data D5, there are few pixels surrounding the target pixel corresponding to the non-string-like structure P2 that have pixel values ​​greater than 0, and the representative value of that target pixel approaches 0. Furthermore, since the pixels surrounding the corresponding pixel that exists near the non-string-like structure P2 include pixels that correspond to the non-string-like structure P2, the representative value of that target pixel becomes greater than 0. As a result, in the replaced image data D5, the non-string-like structure P2 is removed and an area surrounded by a white square in Figure 8 appears around the removed non-string-like structure P2. The size of this area differs for each non-string-like structure P2, and is equal to or smaller than the size of the threshold area R.

[0035] In this replaced image data D5, the pixels surrounding the target pixel corresponding to the non-string-like structure P2 that is close to the string-like structure P1 include many pixels that correspond to that string-like structure P1, i.e., pixels with pixel values ​​greater than 0. Therefore, that non-string-like structure P2 is not removed but is incorporated into the enlarged string-like structure P3.

[0036] The difference data D6 shown in Fig. 9 indicates the difference between the processed image data D3 shown in Fig. 7 and the replaced image data D5 shown in Fig. 8. This difference data D6 indicates the difference in pixel values ​​between pixels that exist at the same position in the processed image data D3 and the replaced image data D5 (the value obtained by subtracting the pixel value of the replaced image data D5 from the pixel value of the processed image data D3). Note that in this difference data D6, negative differences are regarded as 0. In other words, only the non-string-like structures P2 distinguished based on the threshold region R are present in this difference data D6.

[0037] The enhanced image data D4 shown in Fig. 10 represents the difference between the processed image data D3 shown in Fig. 7 and the difference data D6 shown in Fig. 9. This enhanced image data D4 represents the difference in pixel values ​​between pixels that exist at the same position in the processed image data D3 and the difference data D6 (the value obtained by subtracting the pixel value of the difference data D6 from the pixel value of the processed image data D3). In this enhanced image data D4, the non-string-like structure P2 has been largely removed from the processed image data D3.

[0038] In this enhanced image data D4, the non-string-like structure P2 present near the string-like structure P1 has not been removed. The string-like structure P1 and non-string-like structure P2 in this close proximity can be considered as fragmented string-like structure P1 and non-string-like structure P2. Fragmentation refers to a state in which the same molecular chain is actually separated into the string-like structure P1 and non-string-like structure P2 due to the imaging conditions in the electron microscope 2 and the degree of image processing in steps S121 and S122. Therefore, it is desirable to treat the non-string-like structure P2 present near the string-like structure P1 and not removed as part of the string-like structure P1. Furthermore, in this enhanced image data D4, a portion of the string-like structure P1 has an inappropriate shape, i.e., a structure that can be considered as a non-string-like structure P2. An inappropriate shape refers to a shape that cannot be observed in an actual molecular chain and is not suitable for analysis in the analysis step S130. For example, the area enclosed by the white dashed line in Figure 10 contains end points with shapes that are inappropriate for actual molecular chain end points. Such inappropriate shapes can be converted into more appropriate shapes (e.g., string-like structures).

[0039] The enhanced image data D4 can be created as the difference between the processed image data D3 and the difference data D6, but it can also be created using the following alternative procedure. In this alternative procedure, first, within the region surrounded by pixels with pixel values ​​greater than 0 in the replacement image data D5 illustrated in Fig. 8 above, a region whose size (the number of corresponding pixels) is equal to or smaller than the size of the threshold region R (the number of pixels corresponding to the threshold region R) is identified. Next, the line image Pa present in the identified region in the processed image data D3 is regarded as a non-string-like structure P2, and the non-string-like structure P2 is removed.

[0040] The degree of emphasis of the string-like structure P1 in the created enhanced image data D4 can be set arbitrarily, but if the non-string-like structure P2 has been removed from the processed image data D3 and only the thinned string-like structure P1 remains, the string-like structure P1 can be considered to be sufficiently emphasized. In this way, the string-like structure P1 is sufficiently clear in the enhanced image data D4, and it is possible to grasp feature quantities such as the length and number of molecular chains and the number of branches from the string-like structure P1.

[0041] In the analysis step S130, the calculation device 3 executes data processing to analyze the internal structure of the crosslinked rubber using the highlighted image data D4. In this data processing, the string-like structure P1 is used as the analysis object, and the feature amounts of the analysis object (molecular chain) highlighted in the highlighted image data D4 are calculated, and predetermined properties of the crosslinked rubber are determined based on the feature amounts.

[0042] For example, at least one of the distribution density of molecular chains and the proportion of molecular chains by length is calculated as a feature quantity. The feature quantities that can be calculated include variations in the size of the mesh portion of the network structure formed by the molecular chains, the number of branch points in a single molecular chain, and the branch length from the branch point to the branch end. Using feature quantities such as the distribution density of molecular chains and variations in the size of the mesh portion of the network structure as indicators, the uniformity of the crosslinking state of the crosslinked rubber can be grasped. Using feature quantities such as the proportion of molecular chains by length, the number of branch points in a single molecular chain, and the branch length from the branch point to the branch end as indicators, the tensile properties (elongation) of the crosslinked rubber, for example, can be grasped.

[0043] The heterogeneity of the network structure can also be calculated as a feature quantity. For example, a dispersion index indicating the distribution and length dispersion of molecular chains can be calculated as a feature quantity. This dispersion index uses so-called arithmetic dispersion, standard deviation, etc. In addition, the enhanced image data D4 can be divided into multiple regions, and the dispersion index of the molecular chain distribution density and the representative value (median, average, etc.) of the molecular chain length for each region can be calculated as a feature quantity. Note that the heterogeneity of the crosslinked network structure can also be calculated as a feature quantity using known persistent homology analysis. In this way, calculating the heterogeneity of the crosslinked network structure as a feature quantity provides a useful index for improving the physical properties of vulcanized rubber, such as viscoelasticity and tensile properties.

[0044] In the pre-processing step S120, the data processing for creating the processed image data D3 and the data processing for creating the enhanced image data D4 in the above-described embodiment can be omitted, and image data obtained by performing data processing for noise removal and binarization on the original image data D1 can be used as the enhanced image data D4. This image data is data obtained during the process of generating the processed image data D3, which has undergone thinning. In this image data, the object image P and the background in the noise-removed image data D2 are clearly separated by binarization, so the string-like structures P1 can be considered to be enhanced. Similarly, in the pre-processing step S120, the data processing for creating the enhanced image data D4 in the above-described embodiment can be omitted, and the processed image data D3 can be used as the enhanced image data D4. For example, if the image data obtained by performing noise removal and binarization on the original image data D1 or the processed image data D3 contains almost no non-string-like structures P2, using these image data as the enhanced image data D4 can significantly reduce the effort required for the pre-processing step S120. In this way, in the pre-processing step S120, it is advisable to determine whether or not to carry out subsequent processing depending on the degree to which the non-string-like structure P2 exists in the image data obtained during each processing step.

[0045] As described above, according to this embodiment, the string-like structure P1 is used as the analysis target, and the analysis step S130 uses enhanced image data D4 in which the string-like structure P1 is enhanced. The string-like structure P1 can be roughly regarded as a molecular chain of a polymer material, and is therefore closely related to understanding the properties of the polymer material. Therefore, using enhanced image data D4 in which the string-like structure P1 is enhanced is advantageous for faithfully understanding the internal structure of the polymer material.

[0046] Furthermore, according to this embodiment, the string-like structure P1 is treated as a structure suitable for analysis, and the non-string-like structure P2 is treated as a structure inappropriate for use as an analysis target. Then, by distinguishing each line image Pa present in the processed image data D3 into the string-like structure P1 and the non-string-like structure P2 and removing the distinguished non-string-like structure P2, appropriate enhanced image data D4 can be obtained that emphasizes the analysis target (the string-like structure P1) suitable for analysis. Because the string-like structure P1 is a structure closely related to understanding the properties of crosslinked rubber, using enhanced image data P1 in which the string-like structure P is emphasized is advantageous for more faithfully understanding the internal structure of polymer materials.

[0047] The appropriate enhanced image data D4 clarifies the analysis target (string-like structure P1) that is closely related to understanding the characteristics of the crosslinked rubber, and therefore the analysis step S130 using the enhanced image data D4 can grasp the feature quantity of the analysis target with higher accuracy, which is advantageous for accurately grasping the characteristics of the polymer material that is the analysis target.

[0048] In this embodiment, a threshold value is used to distinguish between the string-like structures P1 and the non-string-like structures P2, which allows the calculation device 3 to distinguish between the structures based on the threshold value, making it possible to create the enhanced image data D4 more easily and in a shorter time.

[0049] In the procedure of a modified example of this embodiment illustrated in FIG. 11, steps (S125, S126) have been added to the procedure illustrated in FIG. 2 described above. Therefore, in this modified example, by executing the added steps (S125, S126), enhanced image data D4a that is more suitable for analysis than the enhanced image data D4 obtained in the above-described embodiment is created. Specifically, the arithmetic device 3 combines the fragmented string-like structure P1 and the non-string-like structure P2. Furthermore, the arithmetic device 3 corrects an inappropriate shape (non-string-like structure P2) present in a part of the string-like structure P1 into a more appropriate shape. The contents of each step (S125, S126) of the modified example are described in detail below.

[0050] In step S125, each end point (including the branch end points of the string-like structure P1) of each line image Pa is detected. Next, string-like structures P1 and non-string-like structures P2 are identified where the distance between the end point of the string-like structure P1 and the end point of the non-string-like structure P2 is less than a preset threshold distance. Next, the identified string-like structures P1 and non-string-like structures P2 are connected.

[0051] 12 shows enhanced image data D4 created using the procedure illustrated in FIG. 2 for a sample S different from that of the previous embodiment. The white crosses in FIG. 12 indicate detected ends. Various known feature detection methods can be used to detect these ends, including corner detection using a Hessian matrix, Harris corner detection, and corner detection using local image features such as HOG (Hisutograms of Oriented Gradients) and SIFT (Scaled Invariance Feature Transform).

[0052] In FIG. 13, a string-like structure P1 and a non-string-like structure P2 whose ends are separated by a distance less than a threshold distance are identified, and the identified string-like structure P1 and non-string-like structure P2 are bonded together. Specifically, the distance between the end of the string-like structure P1 and the end of the non-string-like structure P2 present in the area A enclosed by a white circle in FIG. 13 is less than the threshold distance, and the string-like structure P1 and non-string-like structure P2 are considered to be fragmented. This threshold distance can be set arbitrarily as long as it is within a range that can be considered to be fragmented. Those skilled in the art can appropriately set the approximate range that can be considered to be fragmented. This threshold distance is, for example, approximately 1.5 nm (1 pix) to approximately 15 nm (10 pix). Then, a line segment similar to that of the string-like structure P1 is extended from the end of the identified string-like structure P1 to the end of the non-string-like structure P2, thereby bonding the string-like structure P1 and non-string-like structure P2.

[0053] In step S127, the arithmetic device 3 performs data processing to expand and shrink the improper shape (non-string-like structure P2) of a portion of the string-like structure P1, followed by thinning, to generate enhanced image data D4a in which the improper shape has been corrected to the string-like structure P1. This expansion and shrinkage can be performed using known morphology processing. Area A, circled in white in FIG. 13, contains an improper shape resulting from the combination of the fragmented string-like structure P1 and the non-string-like structure P2. Area B, circled in white in FIG. 13, contains an improper shape that cannot be observed in an actual molecular chain. In step S127, these improper shapes are expanded and shrunk and thinned, thereby correcting them to the string-like structure P1. The expansion and shrinkage and thinning in step S127 can be performed by selecting areas A and B and applying them to the selected areas A and B, thereby requiring only partial data processing. Alternatively, it is possible to more simply apply the processing uniformly to the entire image to correct the improper shape.

[0054] In the enhanced image data D4a shown in Fig. 14, the fragmented string-like structure P1 and the non-string-like structure P2 in the enhanced image data D4 shown in Fig. 12 are combined and can be treated as a single string-like structure P1. In addition, in the enhanced image data D4a, inappropriate shapes have been corrected.

[0055] As described above, according to the modified example, the non-string-like structures P2 that were separated from the string-like structures P1 by fragmentation are bonded to the string-like structures P1, and the improper shape of the string-like structures P1 is corrected. In other words, the enhanced image data D4a more faithfully represents the actual molecular chains and is more suitable for analysis. This is advantageous for understanding the properties of polymeric materials with even greater accuracy.

[0056] In the embodiments and modifications described above, enhanced image data D4 is created by removing non-string-like structures P2 in step S125, but removal of non-string-like structures P2 can also be performed multiple times. For example, in step S125, smaller non-string-like structures P2 can be removed using an area smaller than threshold area R, and after step S127, larger non-string-like structures P2 can be removed using threshold area R.

[0057] The present invention is not limited to a specific embodiment, and various modifications and variations are possible within the scope of the gist of the present invention.

[0058] The present disclosure encompasses the following inventions. Invention 1: 1. A method for analyzing the internal structure of a polymer material, using original image data of the polymer material acquired by an electron microscope, by a computing device to analyze the internal structure of the polymer material, an image acquisition step of acquiring the original image data using the electron microscope; a pre-processing step of creating enhanced image data by emphasizing the analysis target in the analysis step with respect to the original image data; an analysis step of calculating a feature amount of the analysis object using the enhanced image data and grasping a predetermined property of the polymer material based on the feature amount; In the pre-processing step, the original image data is subjected to at least noise removal and binarization to create enhanced image data in which string-like structures are emphasized as the analysis target. Invention 2: The method for analyzing the internal structure of a polymer material according to Invention 1, in which the pre-processing step performs data processing to generate processed image data containing a large number of line images by performing thinning on the original image data that has been subjected to the noise removal and binarization, and data processing to distinguish each of the line images into the string-like structures and non-string-like structures and remove the non-string-like structures from the processed image data, thereby creating the enhanced image data. Invention 3: In the preprocessing step, a threshold value for distinguishing each of the line images into the string-like structures and the non-string-like structures is input to the arithmetic device, and the string-like structures and the non-string-like structures are distinguished by the arithmetic device based on the input threshold value. This is a method for analyzing the internal structure of a polymer material as described in Invention 2. Invention 4: The method for analyzing the internal structure of a polymer material according to invention 3, wherein the threshold value is a threshold region within which the non-string-like structure falls and the string-like structure protrudes. Invention 5: A method for analyzing the internal structure of a polymer material according to any one of Inventions 2 to 4, wherein in the preprocessing step, each pixel of the processed image data is set as a target pixel, the pixel value of the target pixel is replaced with a representative value of a large number of pixels surrounding the target pixel to obtain replaced image data, and the string-like structures and the non-string-like structures are distinguished based on the obtained replaced image data. Invention 6: A method for analyzing the internal structure of a polymeric material according to any one of Inventions 2 to 5, wherein in the pre-processing step, the fragmented string-like structures and the non-string-like structures are identified from each of the line images, and a single line image combining the fragmented string-like structures and the non-string-like structures is regarded as the object of analysis. Invention 7: A method for analyzing the internal structure of a polymer material according to any one of Inventions 2 to 6, wherein in the pretreatment step, if a portion of the string-like structure can be regarded as the non-string-like structure, the portion is expanded, shrunk, and thinned to modify the portion into the string-like structure. Invention 8: A method for analyzing the internal structure of a polymer material according to any one of Inventions 1 to 7, in which a crosslinked rubber is swelled to fix a network structure formed by molecular chains, and this crosslinked rubber is used as the polymer material, and in the enhanced image data, the molecular chains are emphasized as the analysis target. Invention 9: A method for analyzing the internal structure of a polymeric material according to any one of Inventions 1 to 8, wherein in the analysis step, at least one of the distribution density of the target structure and the presence ratio of the target structure by size is calculated as the feature. Invention 10: A method for analyzing the internal structure of a polymeric material according to any one of Inventions 1 to 9, wherein the analysis step calculates a dispersion index indicating the dispersion state of at least one of the distribution of the target structures and the size of the target structures as the feature. Invention 11: 11. The method for analyzing the internal structure of a polymer material according to any one of inventions 1 to 10, wherein three-dimensional data is used as the original image data. Invention 12: A system for analyzing the internal structure of a polymer material, comprising a storage unit for storing original image data of a polymer material acquired by an electron microscope, and a computing device for analyzing the internal structure of the polymer material using the original image data, The calculation device executes a preprocessing step of creating enhanced image data by enhancing an analysis target in the analysis step with respect to the original image data, and an analysis step of calculating a feature amount of the analysis target using the enhanced image data and grasping predetermined characteristics of the polymer material based on the feature amount, In the pre-processing step, the original image data is subjected to at least noise removal and binarization to create enhanced image data in which string-like structures are emphasized as the analysis target. [Explanation of symbols]

[0059] 1. Internal structure analysis system 2. Electron microscope 3 Computing device 4. Processing unit 5 Main memory 6 Auxiliary storage 7 Input section 8 Output section D1 Original image data D2 Noise-removed image data D3 processed image data D4, D4a enhanced image data D5 Replacement Image Data D6 Differential Data

Claims

1. 1. A method for analyzing the internal structure of a polymer material, using original image data of the polymer material acquired by an electron microscope, by a computing device to analyze the internal structure of the polymer material, an image acquisition step of acquiring the original image data using the electron microscope; a pre-processing step of creating enhanced image data by emphasizing the analysis target in the analysis step with respect to the original image data; an analysis step of calculating a feature amount of the analysis object using the enhanced image data and grasping a predetermined property of the polymer material based on the feature amount; In the pre-processing step, data processing is performed on the original image data to at least remove noise and binarize it, thereby creating enhanced image data in which string-like structures are emphasized as the analysis target.

2. 2. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the pre-processing step performs data processing to generate processed image data containing a large number of line images by performing thinning on the original image data that has been subjected to the noise removal and binarization, and data processing to distinguish each of the line images into the string-like structures and non-string-like structures and remove the non-string-like structures from the processed image data, thereby creating the enhanced image data.

3. 3. The method for analyzing the internal structure of a polymer material according to claim 2, wherein in the preprocessing step, a threshold value for distinguishing each of the line images into the string-like structure and the non-string-like structure is input to the arithmetic device, and the string-like structure and the non-string-like structure are distinguished by the arithmetic device based on the input threshold value.

4. The method for analyzing the internal structure of a polymer material according to claim 3 , wherein a threshold region within which the non-string-like structure falls and the string-like structure protrudes is used as the threshold.

5. 3. The method for analyzing the internal structure of a polymer material according to claim 2, wherein in the preprocessing step, each pixel of the processed image data is set as a target pixel, the pixel value of the target pixel is replaced with a representative value of a large number of pixels surrounding the target pixel to obtain replaced image data, and the string-like structure and the non-string-like structure are distinguished based on the obtained replaced image data.

6. The method for analyzing the internal structure of a polymer material according to claim 2, wherein in the preprocessing step, the fragmented string-like structures and the non-string-like structures are identified from each of the line images, and a single line image combining the fragmented string-like structures and the non-string-like structures is regarded as the object of analysis.

7. The method for analyzing the internal structure of a polymer material according to claim 2, wherein in the pre-processing step, if a portion of the string-like structure can be regarded as the non-string-like structure, the portion is expanded / contracted and thinned to modify the portion into the string-like structure.

8. 8. The method for analyzing the internal structure of a polymer material according to claim 1, wherein a crosslinked rubber is swollen to fix a network structure formed by molecular chains, and this crosslinked rubber is used as the polymer material, and the molecular chains are emphasized as the analysis target in the emphasized image data.

9. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the analysis step calculates at least one of a distribution density of the analysis objects and a size-specific abundance ratio of the analysis objects as the feature amount.

10. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein the analysis step calculates a dispersion index indicating a dispersion state of at least one of the distribution of the analysis objects and the size of the analysis objects as the feature amount.

11. 3. The method for analyzing the internal structure of a polymer material according to claim 1, wherein three-dimensional data is used as the original image data.

12. A system for analyzing the internal structure of a polymer material, comprising a storage unit for storing original image data of a polymer material acquired by an electron microscope, and a computing device for analyzing the internal structure of the polymer material using the original image data, The calculation device executes a preprocessing step of creating enhanced image data by enhancing an analysis target in the analysis step with respect to the original image data, and an analysis step of calculating a feature amount of the analysis target using the enhanced image data and grasping predetermined characteristics of the polymer material based on the feature amount, In the pre-processing step, the original image data is subjected to at least noise removal and binarization to create enhanced image data in which string-like structures are emphasized as the analysis target.

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