A method for analyzing pore structure of large soil columns using X-ray imaging

Abstract

The present invention proposes a method for analyzing the pore structure of large soil column X-ray imaging, which belongs to the fields of soil science, hydroecology, and environmental science. It includes pre-processing of large soil column samples before binary segmentation, binary image denoising, plant root pore extraction based on pore connectivity and morphology, and root and non-root pore parameter quantification. Among them, binary segmentation adopts the Phansalkar method based on the local threshold method and is optimized for large soil columns. Image denoising uses the MorphoLibJ plug-in, and image removal is performed to extract disturbed pores. The extraction of plant root pores adopts the improved Biopore method. Finally, the ImageJ plug-in is used to quantify the parameters of root and non-root pores. This method can effectively extract and analyze the pore structure of large soil column X-ray imaging and can be used for the research and analysis of soil structure.

Description

Technical Field

[0001] The present invention belongs to the fields of soil science, hydrology and ecology, and environmental science, and more particularly relates to an analysis method for the pore structure of a large soil column by X-ray imaging. Background Art

[0002] Traditional methods for quantifying soil pore structure include indirect inference of soil pore distribution through soil water infiltration, staining, or soil characteristic curves. However, these indirect inferences of soil structure are based on a series of assumptions and are subject to significant uncertainty. Compared to these traditional techniques, X-ray CT imaging provides three-dimensional (3D) views, allowing direct and quantitative information about the sample's physical environment to be extracted non-destructively. Consequently, X-ray CT imaging is increasingly being used to study dynamic processes at the pore scale in soils. Using X-ray CT images with appropriate scanning parameters, it is possible to visualize and quantify the organization of soil aggregates, the development of preferential flow pathways, soil-root interactions, and root systems. X-ray CT imaging provides an effective means of gaining a deeper understanding of soil habitat structure. The core component of X-ray CT imaging is post-processing, and current image processing methods fall into two main categories: commercial software and free open-source software.

[0003] The shortcomings of existing methods are mainly reflected in the following two aspects. First, for commercial software, the high cost of commercial software and the complex interface operation of existing commercial software limit its widespread use by users. At the same time, due to the small number of commercial software users, the iterative update rate is relatively slow, which makes it difficult for commercial software to meet certain special needs. Second, for free and open source software, although there are currently a large number of application cases in different disciplines, there is still a lack of a complete method for large soil columns that can distinguish the number and morphology of pores in the direction of soil pore structure quantification, which limits the widespread use of X-ray imaging technology in this field. Summary of the Invention

[0004] The present invention proposes a method for analyzing the pore structure of large soil columns using the open source software ImageJ. This method can effectively solve the unique axis deviation phenomenon in the large soil column image analysis process, solve the disturbed pore removal, and propose a method specifically suitable for the binary segmentation of large soil column images, which solves the existing technical difficulties such as the difficulty in extracting soil plant pores.

[0005] In order to achieve the above object, the present invention is implemented by adopting the following technical solutions: the method comprises:

[0006] The large soil column image sample binary segmentation method based on local threshold method is used for preprocessing before binary segmentation.

[0007] Denoising of binary images after segmentation;

[0008] Plant root pore extraction method based on pore connectivity and morphology;

[0009] Quantification of root and non-root pore parameters.

[0010] In one embodiment, the large soil column sample binary segmentation method based on the local threshold method includes:

[0011] The Phansalkar method in the local threshold method is used, and the optimal Phansalkar improved algorithm for general large soil columns is proposed. The calculation formula is as follows:

[0012] t=mean*(1+p*exp(-q*mean)+k*((stdev / r)-1))

[0013] Where mean and stdev are the local mean and standard deviation respectively; the values ​​of p and q are fixed, where p = 2 and q = 10; k and r are parameters 1 and 2 respectively, where k = 0.25 and r = 7.

[0014] In one embodiment, the segmented binary image denoising process uses the "InteractiveMorphologicalRestructuring3D" method in the MorphoLibJ plug-in to extract the disturbed pores, and performs image removal on the extracted disturbed pores and the original image;

[0015] The noise caused by the resolution was removed using the built-in denoising method of ImageJ. For large low-resolution soil columns, "iterations = 1 count = 8 black" was used for closing operations to remove the noise.

[0016] In one embodiment, the plant root pore extraction method adopts an improved Biopore method. By improving the "scaling" range parameter in Biopore, the plant root pore image includes root pores in the sample resolution, thereby achieving the effect of extracting the root structure.

[0017] In one embodiment, the plant root pore extraction method uses ImageJ to quickly extract the main root pores.

[0018] In one scheme, the main root pores in the upper part of the soil column are first selected, and then all the pores connected to them are extracted using the "InteractiveMorphologicalRestructuring3D" method. Finally, the main root pore structure is obtained by repeatedly removing non-root pores.

[0019] In one embodiment, the root and non-root pore parameters are quantified using an ImageJ built-in plug-in.

[0020] In one approach, the total pore volume is first quantified using “analysispartical”, and the average diameter and pore connectivity of each pore are calculated using “ParticleAnalyser”; the pore branch density, node density, pore length, and pore curvature are calculated using “Skeletonise3D”.

[0021] Beneficial effects of the present invention:

[0022] The present invention proposes a method for analyzing the pore structure of large soil columns using the open source software ImageJ. This method can effectively solve the unique axis deviation phenomenon in the large soil column image analysis process, provide a method suitable for large soil column pore segmentation, and solve the existing technical difficulties such as disturbed pore removal and biological pore extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Flow chart of the method of the present invention;

[0024] Figure 2 Figure 1 shows the method of adjusting the soil column axis tilt before (left) and after (right);

[0025] Figure 3 The figure shows the process before (left) and after (right) the removal of disturbed macropores according to the present invention;

[0026] Figure 4 This is the effect diagram of extracting biological pores based on the improved biopore (left) and graphic continuity method (right) of the present invention. DETAILED DESCRIPTION

[0027] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate exemplary embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0028] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0029] like Figure 1 As shown in FIG, a method for analyzing the pore structure of a large soil column by X-ray imaging is based on analyzing the pore structure of a large soil column by X-ray imaging using ImageJ;

[0030] S1. Preprocessing of large soil column image samples before binary segmentation; In order to maximize the selection of soil sample research area (ROI) and improve the soil binary segmentation effect, it is first necessary to adjust the soil column axis tilt for the reconstructed TIFF format image. This application uses the "untiltstack" method in the ImageJ open source software to remove the tilt in the stacked image, so that the image of the longitudinal object is parallel to the Z axis of the stacked image, and solve the problem of different voxel depth, pixel width and height ( Figure 2 Using the calibrated axis stacked images, the largest ROI was selected, the area outside the ROI was cleared, and an octal grayscale image was obtained using a 3D filtering method.

[0031] S2. Binary segmentation method for large soil column samples based on local threshold method; Image segmentation (Segmentation) is a key step in extracting soil pore structure. Currently, there is no unified segmentation method in the field of soil science, which makes it difficult to compare data. For large soil columns, since a sample contains soils of different depths, there is a problem of grayscale value differences between the upper and lower soil columns, which increases the difficulty of image binary segmentation. This application uses the Phansalkar method in the local threshold method (Autolocalthreshold) and proposes an optimal Phansalkar improved algorithm for general large soil columns. The calculation formula is as follows:

[0032] t=mean*(1+p*exp(-q*mean)+k*((stdev / r)-1))

[0033] where mean and stdev are the local mean and standard deviation, respectively. p and q are fixed values, where p = 2 and q = 10. k and r are parameters 1 and 2, respectively, where k = 0.25 and r = 7.

[0034] Here, the radius determines the threshold method, which takes into account the area of ​​each local region. The smaller the radius, the more sensitive it is to the threshold, which may lead to over-segmentation and take longer time. The solution of this application is to choose a complex method with a larger radius. Therefore, the radius is selected as 7 here, which shows the best segmentation effect with the original image.

[0035] S3. Denoising of binary images after segmentation. The binary segmented images often contain artificially disturbed non-target pore structures that need to be removed, such as large cracks, large pores at the edges, and single noise points formed by resolution. This application proposes to use the "InteractiveMorphologicalRestructuring3D" method in the MorphoLibJ plug-in to extract the disturbed pores, and remove the large disturbed pores by removing the extracted disturbed pores from the original image (see the effect). Figure 3ImageJ’s built-in denoising method was used to remove noise caused by resolution. For large low-resolution soil columns, “iterations = 1 count = 8 black” was used for closing operations (close).

[0036] S4. Plant root pore extraction method based on pore connectivity and morphology; Plant roots are one of the main factors affecting pore structure. Plant roots affect soil pores through various mechanisms, including direct generation of pores, or indirectly affecting soil pore formation and distribution through the increase of soil organic matter, exudation of secretions, and absorption and utilization of water. Therefore, accurate extraction of plant root pores has become one of the hot topics in the current research on soil structure using X-ray CT imaging technology. However, due to the small difference between the grayscale value of the root system and the grayscale of the pores, extracting root pores has become a difficulty in this field. At present, several extraction methods based on root morphological characteristics have been formed at home and abroad. However, the existing methods have the problems of cumbersome operation and unsuitability for large soil column (low resolution) images.

[0037] This application proposes two methods for extracting root pores from large soil column images, based on the improved Biopore method and the graphic continuity method for extracting biopores. The Biopore method is more suitable for extracting biopores from soil samples with large pore differences, but since this method is not suitable for soil samples with relatively uniform biopore sizes at low resolution, this application improves the range parameter of "scaling" in Biopore so that it includes root pores in the sample resolution, thereby achieving the effect of extracting root structure (see the effect). Figure 4 Left).

[0038] In addition, this application proposes a simple method to quickly extract the main root pores using ImageJ. The specific operation is to first select the main root pores in the upper part of the soil column, then use the "InteractiveMorphologicalRestructuring3D" method to extract all the pores connected to it, and finally obtain the main root pore structure by removing non-root pores multiple times (see the effect). Figure 4 right).

[0039] S5. Quantification of root and non-root pore parameters: This application uses ImageJ's built-in plugin to quantify root and non-root pore parameters. The specific steps are as follows: first, use "analysispartical" to quantify total pore parameters, then use "ParticleAnalyser" to calculate the average diameter and pore connectivity of each pore. Then, use "Skeletonise3D" to calculate parameters such as pore branch density, node density, pore length, and pore curvature.

[0040] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0041] It should be understood that the detailed description of the technical solutions of the present invention using the preferred embodiments above is illustrative and not restrictive. A person skilled in the art, after reading the present specification, may modify the technical solutions described in the embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the pore structure of a large soil column by X-ray imaging, characterized by: The method includes: The large soil column image sample binary segmentation method based on local threshold method is used for preprocessing before binary segmentation. Denoising of binary images after segmentation; Plant root pore extraction method based on pore connectivity and morphology; Quantification of root and non-root pore parameters; The large soil column sample binary segmentation method based on the local threshold method includes: The Phansalkar method in the local threshold method is used, and the optimal Phansalkar improved algorithm for general large soil columns is proposed. The calculation formula is as follows: t=mean*(1+p*exp(-q*mean)+k*((stdev / r)-1)) Where mean and stdev are the local mean and standard deviation respectively; the values ​​of p and q are fixed, where p = 2 and q = 10; k and r are parameters 1 and 2 respectively, where k = 0.25 and r = 7; The binary image denoising after segmentation is performed by using the "InteractiveMorphologicalRestructuring3D" method in the MorphoLibJ plug-in to extract the disturbed pores and perform image removal between the extracted disturbed pores and the original image; ImageJ's built-in denoising method was used to remove noise caused by resolution. For large low-resolution soil columns, "iterations = 1 count = 8 black" was used for closing operations. The plant root pore extraction method adopts an improved Biopore method. By improving the "scaling" range parameter in Biopore, the plant root pore image contains root pores in the sample resolution, thereby achieving the effect of extracting the root structure.

2. The method for analyzing pore structure of a large soil column by X-ray imaging according to claim 1, characterized in that: The plant root pore extraction method uses ImageJ to quickly extract the main root pores.

3. The method for analyzing pore structure of a large soil column by X-ray imaging according to claim 2, characterized in that: First, the main root pores at the top of the soil column were selected. Then, the "Interactive Morphological Restructuring 3D" method was used to extract all pores connected to them. Finally, the main root pore structure was obtained by repeatedly removing non-root pores.

4. The method for analyzing pore structure of a large soil column by X-ray imaging according to claim 1, characterized in that: The root and non-root pore parameters were quantified using ImageJ's own plug-in.

5. The method for analyzing pore structure of a large soil column by X-ray imaging according to claim 4, characterized in that: The total pore volume was quantified using "analysispartical" and the average pore diameter and pore connectivity were calculated using "ParticleAnalyser". The pore branch density, node density, pore length, and pore curvature were calculated using "Skeletonise3D".

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