JP7709893B2 - Method, system, and program for calculating silica dispersion evaluation value - Google Patents

Description

This disclosure relates to a method, system, and program for calculating a dispersion evaluation value of silica.

To evaluate the dispersion of silica in rubber, human visual evaluation is performed based on scanning electron microscope (SEM) images, taking advantage of the fact that silica appears strong (white) in SEM images. However, manual evaluation is subject to experience and individual variability, so a method that allows quantitative evaluation is desirable.

Patent document 1, paragraph 0056, describes determining the average aggregate area of vulcanized rubber. Patent document 1 also describes binarizing the SEM image with a threshold determined by the Otsu method, and calculating the aggregate area of the silica portion based on the binarized image.

Patent No. 5894583

However, SEM images of rubber surfaces show different intensity unevenness depending on the shooting conditions, even when the object being photographed is the same rubber, and it is believed that unless appropriate binarization is performed, the accuracy of the evaluation value will be compromised. The Otsu method described in Patent Document 1 can determine a relatively appropriate threshold value for binarization if the image has two peaks in its intensity histogram, but SEM images of rubber surfaces do not have a clear intensity peak, and the Otsu method cannot determine an appropriate threshold value. No method has been proposed for determining an appropriate threshold value for binarizing SEM images of rubber, not limited to the Otsu method.

This disclosure provides a method, system, and program for calculating a dispersion evaluation value of silica that avoids deterioration in the accuracy of the evaluation value due to binarization.

The method for calculating the silica dispersion evaluation value disclosed herein is a method executed by one or more processors, which extracts multiple pixel clusters that are relatively stronger than the surrounding area from an SEM image, which is a grayscale image of a rubber surface, identifies the center of gravity of each of the extracted pixel clusters, and calculates the silica dispersion evaluation value based on the identified multiple centers of gravity.

FIG. 1 is a block diagram showing a system according to an embodiment of the present invention. 4 is a flowchart showing a process executed by the system. FIG. 2 is a SEM image of a rubber surface. FIG. 4 is a diagram showing an intensity histogram of the image body portion shown in FIG. 3 . FIG. 4 is a diagram showing a first image generated from the image body portion shown in FIG. 3 . FIG. 4 is a diagram showing a second image obtained by performing expansion processing using a 5-pixel disk element on the main image portion of the SEM image shown in FIG. 3 . FIG. A simplified diagram showing the principle of extracting the center of a cluster of pixels that has a relatively higher intensity than the surrounding pixels. 7 is a diagram showing an image obtained by subtracting the intensity of the second image shown in FIG. 6 from the intensity of the first image shown in FIG. 5 . The identified centroids are indicated by crosses and are overlaid on the original SEM image. A diagram showing in white the line that connects the centers of gravity (black crosses) at the shortest distance.

An embodiment of the present disclosure will be described below with reference to the drawings.

[system]
The system 1 (apparatus) of this embodiment calculates the dispersion evaluation value of silica from an SEM image of a rubber surface. The SEM image is a grayscale image captured by an electron microscope. In a grayscale image, each pixel has an intensity, and the intensity indicates a shade of black. For example, the intensity gradation is 256 for 8 bits and 65536 for 16 bits. Low intensity is black, and high intensity is white. Single particles or aggregates of silica particles are relatively shown as white pixels in the SEM image.

As shown in FIG. 1, the system 1 has an SEM image acquisition unit 10, a pixel block extraction unit 11, a center of gravity identification unit 12, and an evaluation value calculation unit 13. The pixel block extraction unit 11 has a first image generation unit 14, a second image generation unit 15, and a center extraction unit 16. These units 10 to 16 are realized by software and hardware working together as the processor 1a executes the processing routine shown in FIG. 2, which is stored in advance in a computer equipped with a processor 1a, a memory 1b, various interfaces, etc. In this embodiment, the processor 1a in one device realizes each unit, but this is not limited to this. For example, the system may be configured to distribute the processing of each unit using a network, and multiple processors may execute the processing of each unit. In other words, one or multiple processors execute the processing. The memory 1b stores the SEM image, the first image with the intensity of the SEM image increased, the second image with the high-intensity pixels of the SEM image expanded, center information of the high-intensity pixels, center of gravity information, nearest neighbor distance, silica dispersion evaluation value, etc.

The SEM image acquisition unit 10 acquires a grayscale image of an SEM image of a rubber surface and stores it in the memory 1b. FIG. 3 shows an SEM image of a rubber surface, which is a grayscale image in which the intensity is expressed in 16-bit gradations. The imaging conditions are described at the bottom of FIG. 3, but the calculation of the dispersion evaluation value of silica is performed on the main body of the image excluding the bottom. Hereinafter, the SEM images other than FIG. 3 show images corresponding to the main body of the image excluding the bottom of FIG. 3. FIG. 4 shows an intensity histogram of the main body of the image shown in FIG. 3. The horizontal axis of FIG. 4 is the intensity, and the vertical axis is the number of pixels (number of pixels). The left end of the horizontal axis of FIG. 4 indicates 0 (black), and the right end of the horizontal axis indicates 65535 (white). As shown in FIG. 4, the intensity distribution of the SEM image of the rubber surface is uniform, and there is no clear peak, making it difficult to determine an appropriate threshold value for binarization that is applicable to the entire image.
Therefore, this specification proposes a method for calculating the dispersion evaluation value of silica without binarizing the SEM image.

The pixel block extraction unit 11 extracts multiple pixel blocks that are relatively more intense than the surrounding pixels from the SEM image. The pixels that are relatively more intense than the surrounding pixels are white pixels with high intensity, and are pixels that are likely to be silica. In this embodiment, the pixel block extraction unit 11 is composed of a first image generation unit 14, a second image generation unit 15, and a center extraction unit 16.

The first image generating unit 14 generates a first image by increasing the intensity of the SEM image. In this embodiment, the intensity of each pixel of the SEM image is increased by 1.05 times, but the numerical value can be changed as appropriate. It is preferable that the lower limit of the amount of increase in intensity is 1.01 times, and the upper limit of the amount of increase in intensity is 1.15 times. This is because if the intensity can be increased slightly, the center of the white pixels can be extracted by intensity calculation with the second image. Figure 5 shows the first image generated from the main body part of the image shown in Figure 3. The image in Figure 5 is an image in which the intensity of the main body part of the image in Figure 3 has been increased by 1.05 times.

The second image generating unit 15 generates a second image by performing an expansion process on the SEM image to expand a region of pixels having a relatively higher intensity than the surrounding pixels. The expansion process can be a morphological expansion process. In this embodiment, a disk element having a size of 5 pixels is used as a structuring element (also called a kernel), but the size of the disk element can be changed as appropriate. The expansion process of the morphological operation is well known, so a detailed description will be omitted, but will be briefly described using FIG. 7. FIG. 7 is an explanatory diagram regarding the expansion process. As shown in FIG. 7, it is assumed that the original image has a rectangular block of white pixels based on black pixels. As shown in the figure, the following operation is performed while moving the disk element one pixel at a time with respect to the original image. Each time the disk element is moved, an operation of outputting the pixel located at the center of the disk element to the corresponding pixel of the new image as the value with the highest intensity in the disk element is repeated. If the number of pixels in the original image is N, an operation of outputting the pixel located at the center of the disk element to the corresponding pixel of the new image as the value with the highest intensity in the disk element is performed N times. Since only the original image is referenced within the disk element, and the new image is not referenced, the pixels of the new image once output do not affect the expansion process.
This causes pixels with relatively high intensity in the original image to expand into a disk shape. In the example of Figure 7, rectangular clusters of white pixels are changed to circular clusters of white pixels. In this embodiment, disk elements are used as structuring elements (kernels), but this is not limiting and rectangular elements may be used, but disk elements are considered to be most preferable because silica has a circular shape. Figure 6 shows a second image in which the image body of the SEM image shown in Figure 3 has been expanded using 5-pixel disk elements.

The center extraction unit 16 extracts the center of a cluster of pixels having a relatively higher intensity than the surroundings by subtracting the intensity of the second image from the first image. FIG. 9 shows an image obtained by subtracting the intensity of the second image shown in FIG. 6 from the intensity of the first image shown in FIG. 5. It can be seen that the center of the white pixels is obtained. FIG. 8 is a diagram showing the principle of extracting the center of a cluster of pixels having a relatively higher intensity than the surroundings. As shown in the upper part of FIG. 8, an example will be described in which white pixels are present on a background of black pixels as an original image. When the intensity is illustrated along a line A1-A1 passing through the white pixels in the left-right direction, the intensity changes like a mountain as shown in the upper right part of FIG. 8. The area near the peak of the mountain is the intensity of the white pixels. The first image, which has been subjected to a process of increasing the intensity of the original image, has a higher intensity while maintaining the shape of the intensity of the original image, as shown in the middle right part of FIG. The second image, which has been subjected to an expansion process, has a wider width of the relatively high intensity part while maintaining the peak intensity of the original image, as shown in the middle left part of FIG. 8. By subtracting the intensity of the second image from the intensity of the first image, the shaded area in the lower part of FIG. 8 remains as the central area of white pixels.
This extracts a part (center part) of the pixel cluster that has a relatively higher intensity than the surroundings. From the image in FIG. 9, the pixel clusters are selected based on the size of the pixel cluster. Specifically, pixel clusters in which the number of pixels constituting the pixel cluster is within a predetermined range determined by a predetermined lower limit and a predetermined upper limit are extracted. In this embodiment, the predetermined lower limit is 8 pixels and the predetermined upper limit is 300 pixels, and only pixel clusters with 8 or more and 300 or less are extracted. This makes it possible to remove noise that is too small for the pixel clusters and non-silica that is too large for the pixel clusters.

The centroid identification unit 12 identifies the centroid of the cluster of pixels extracted by the center extraction unit 16. Figure 10 shows the identified centroid with a cross mark, superimposed on the original SEM image. The black frame portion in the upper image of Figure 10 is shown enlarged in the enlarged view at the bottom of Figure 10. In the enlarged view at the bottom of Figure 10, the centroid of the white pixels in Figure 9 is represented by a black cross.

The evaluation value calculation unit 13 calculates the dispersion evaluation value of silica based on the multiple centroids identified by the centroid identification unit 12. Specifically, the evaluation value calculation unit 13 identifies a line connecting the closest centroids based on the multiple centroids. FIG. 11 is a diagram showing in white the line connecting the centroids (black crosses) at the shortest distance. The length of each line is the nearest distance, and when the number of identified lines is N, the nearest distance, which is the length of each line, is represented by _Xk . k is a natural number from 1 to N, and is represented by k=1 to N. _Xav is the average value of the nearest distances Xk. The evaluation value calculation unit 13 calculates the coefficient of variation _CV as the dispersion evaluation value. The coefficient of variation CV (evaluation value) can be calculated based on the standard deviation S of the nearest distances and the average value _Xav of the nearest distances. The coefficient of variation CV is expressed by the following formula. S indicates the standard deviation.

[Method of calculating dispersion evaluation value of silica]
A method for calculating the silica dispersion evaluation value executed by the above system 1 will be described with reference to FIG.

First, in step ST1, the SEM image acquisition unit 10 acquires an SEM image, which is a grayscale image of the rubber surface. By executing the following steps ST2 to ST4, the pixel block extraction unit 11 extracts multiple pixel blocks that are relatively stronger than the surrounding area from the SEM image. Specifically, in step ST2, the first image generation unit 14 constituting the pixel block extraction unit 11 generates and acquires a first image in which the intensity of the SEM image is increased. In step ST3, the second image generation unit 15 constituting the pixel block extraction unit 11 generates and acquires a second image in which an expansion process is performed on the SEM image to expand the area of pixels that are relatively stronger than the surrounding area. In step ST4, the center extraction unit 16 constituting the pixel block extraction unit 11 extracts the center part of the pixel block that is relatively stronger than the surrounding area by subtracting the intensity of the second image from the first image.

In the next step ST5, the center of gravity identification unit 12 identifies the center of gravity of each of the extracted pixel clusters. In the next step ST6, the evaluation value calculation unit 13 calculates a silica dispersion evaluation value based on the identified multiple centers of gravity. As a result of the above, it is possible to calculate the silica dispersion evaluation value without binarizing the SEM image.

As described above, the method for calculating a silica dispersion evaluation value of this embodiment may be a method executed by one or more processors, in which a plurality of pixel clusters having a relatively higher intensity than the surroundings are extracted from an SEM image, which is a grayscale image of a rubber surface, the center of gravity of each of the extracted pixel clusters is identified, and the silica dispersion evaluation value is calculated based on the identified plurality of center of gravity.
In this way, without binarizing the SEM image, which is a grayscale image, clusters of pixels that are likely to be silica are extracted and a dispersion evaluation value for silica is calculated based on the center of gravity of the clusters, thereby avoiding noise and loss of information due to binarization.In addition, since the center of gravity is used instead of the area, the inclusion of noise is reduced and the accuracy of the evaluation value can be improved.

Although not particularly limited, as in this embodiment, a block of pixels to be extracted may be one in which the number of pixels constituting the block is within a specified range determined by a specified lower limit value and a specified upper limit value.
In this way, by setting a predetermined lower limit, it is possible to remove noise with too few pixels, and by setting a predetermined upper limit, it is possible to avoid extraction of substances other than silica. Since silica is locally dispersed uniformly, it is considered that it is not necessary to extract all of the silica, and it is possible to calculate an appropriate silica dispersion evaluation value.

Although not particularly limited, as in this embodiment, a first image in which the intensity of an SEM image is increased may be obtained, a second image in which an expansion process is performed on the SEM image to expand the area of pixels that are relatively more intense than their surroundings may be obtained, and the intensity of the second image may be subtracted from the first image to extract the central portion of the cluster of pixels that are relatively more intense than their surroundings.
The following are preferred specific examples.

Although not particularly limited, as in this embodiment, multiple nearest neighbor distances, which are the lengths of the lines connecting the nearest centers of gravity, may be identified based on multiple centroids, and the silica dispersion evaluation value may be calculated based on the standard deviation of the nearest neighbor distances and the average value of the nearest neighbor distances.
The following are preferred specific examples.

The system according to this embodiment includes one or more processors that execute the above method.

The program according to this embodiment is a program for causing one or more computers to execute the above-described method.
By executing these programs, it is possible to obtain the effects of the above-mentioned methods.

Although the embodiments of the present disclosure have been described above with reference to the drawings, the specific configuration should not be considered to be limited to these embodiments. The scope of the present disclosure is indicated not only by the description of the above embodiments but also by the claims, and further includes all modifications within the meaning and scope equivalent to the claims.

(1) In the above embodiment, the pixel block extraction unit 11 is composed of a first image generation unit 14, a second image generation unit 15, and a center extraction unit 16, but is not limited to this configuration as long as it is possible to extract pixels that are relatively stronger than their surroundings.

(2) In the above embodiment, the coefficient of variation CV is calculated as the dispersion evaluation value of silica, but other evaluation values may be used as long as the extracted center of gravity is used.

The structures employed in each of the above embodiments can be employed in any other embodiment. The specific configurations of each part are not limited to the above-mentioned embodiments, and various modifications are possible without departing from the spirit of this disclosure.

For example, the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings, can be realized in any order, so long as the output of a previous process is not used in a subsequent process. Even if the flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, this does not mean that it is necessary to execute the processes in this order.

Each unit shown in FIG. 1 is realized by executing a specific program on one or more processors, but each unit may also be configured with a dedicated memory or dedicated circuit. In the above embodiment of system 1, each unit is implemented in processor 1a of a single computer, but each unit may be distributed and implemented on multiple computers or in the cloud. In other words, the above method may be executed on one or more processors.

System 1 includes a processor 1a. For example, processor 1a can be a central processing unit (CPU), a microprocessor, or other processing unit capable of executing computer-executable instructions. System 1 also includes memory 1b for storing data for system 1. In one example, memory 1b includes computer storage media, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, DVD or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other media that can be used to store desired data and that can be accessed by system 1.

1... System, 10... SEM image acquisition unit, 11... Pixel block extraction unit, 12... Center of gravity identification unit, 13... Evaluation value calculation unit.

Claims (5)

A method executed by one or more processors, comprising:
A first image is obtained by increasing the intensity of a SEM image, which is a grayscale image of a rubber surface;
Obtaining a second image by performing a dilation process of a morphological operation on the SEM image;
Extracting a plurality of clusters of white pixels which are single particles or aggregates of silica particles obtained by subtracting the intensity of the second image from the intensity of the first image;
Identifying the center of gravity of each of the extracted clusters of white pixels;
A method for calculating a dispersion evaluation value of silica, the method comprising: calculating a dispersion evaluation value of silica based on a plurality of identified centers of gravity. 2. The method according to claim 1, wherein the number of pixels constituting the cluster of white pixels to be extracted is within a predetermined range determined by a predetermined lower limit value and a predetermined upper limit value. 3. The method according to claim 1 or 2, further comprising: determining a plurality of nearest neighbor distances, which are the lengths of the lines connecting the nearest neighbor centers of gravity, based on the plurality of centroids; and calculating a dispersion evaluation value of the silica based on a standard deviation of the nearest neighbor distances and an average value of the nearest neighbor distances. A system comprising one or more processors for carrying out the method according to any one of claims 1 to 3 . A program for causing one or more processors to execute the method according to any one of claims 1 to 3 .