CN107515187A - A rapid method for detecting morphological characteristics of vessel cells in lignocellulosic materials - Google Patents

Abstract

The present invention relates to a kind of method of vessel cell morphological feature in quick detection lignocellulosic material, belong to lignocellulosic material microstructure detection technique field.This method is to obtain micro-image first, and sample preparation early stage is carried out to lignocellulosic material；The sample prepared is obtained to the micro-image that can be told each lignocellulosic material and respectively form cell by light microscope；Image pixel is carried out to the micro-image of acquisition and actual size proportionate relationship determines and processing, to ensure the readability and the accuracy of identification and detection of vessel cell morphological feature in micro-image；The parameter of micro-image vessel cell morphological feature detection after setting processing；Vessel cell morphological feature detection is carried out, it is final to obtain vessel cell image and vessel cell characteristic pattern data in the lignocellulosic material for meeting setup parameter requirement.The present invention, which has filled up current method of testing, can not obtain the morphological feature such as lignocellulosic material vessel cell number, area and circularity this blank automatically.

Description

A rapid method for detecting morphological characteristics of vessel cells in lignocellulosic materials

technical field

The invention belongs to the technical field of microstructure detection of lignocellulosic materials, in particular to a method for rapidly detecting the morphological characteristics of cells composed of lignocellulosic materials. The method can quickly and automatically obtain the morphological characteristic parameters of the vessel cells in the microscopic image of the lignocellulosic material by using the image analysis method.

Background technique

Lignocellulosic materials are renewable green building and wood product raw materials, and are also important raw materials for biomass energy materials. Lignocellulosic materials mainly include wood, bamboo and palm rattan. In the process of utilization, the lignocellulosic material mainly utilizes the xylem part inside the bark. The xylem of lignocellulosic materials is composed of fibers or tracheids, vessel cells, parenchyma cells, and xylem rays, among which the fibers or tracheids mainly play a role in mechanical support in the xylem, while the tracheids of coniferous wood have vessel cells In addition to the function of xylem, other lignocellulosic materials have vessel cells in the xylem. Vessel cells have an important function of transporting water during the growth of forest trees, and are also important anatomical factors in the microstructure of forest trees.

Because the xylem of lignocellulosic materials is arranged by various types of cells, it not only forms various wood textures, but also its different microstructures and anatomical factor characteristics also determine the physical and mechanical properties of lignocellulosic materials, which ultimately determine The use of lignocellulosic materials. It can be seen that the microstructural characteristics of different lignocellulosic materials are the basis for identifying wood and understanding its material properties. Therefore, research on the microstructural characteristics of lignocellulosic materials has been continuously carried out since the invention of the microscope. Vessel cells are important anatomical factors in the microstructure of lignocellulosic materials, and it is particularly important to accurately detect the morphological characteristics of vessel cells and their proportion in the microstructure.

At present, the measurement methods for the morphological characteristics of lignocellulosic fibers and vessel cells can be divided into two categories. One method is chemical isolation or physical stripping. This method is to isolate or strip the constituent cells of xylem, and select cells with complete morphology for other The measurement of longitudinal length and transverse width mainly focuses on fiber characteristics, which is especially suitable for the evaluation of pulp wood fiber characteristics; another method is in-situ measurement method, which is currently used to measure the wall thickness of lignocellulosic material fibers and vessel cells in situ The main method of morphological characteristics such as cavity diameter and so on. The in situ measurement method first slices the material or obtains a clear structure on the surface and directly takes pictures to obtain a microscopic image, and then uses the microscope’s own image analysis software to use the line measurement method under the microscope, that is, the contour line of the inner wall located on the same side as the center of the ductal cell The straight-line distance between the previous point and a point on the contour line of the outer wall was taken as the wall thickness of the ductal cell, and the straight-line distance between two points on the contour line of the inner wall located on both sides of the center of the ductal cell was taken as the lumen diameter of the ductal cell. This method can only measure fibers and ductal cells one by one, and because the shape of ductal cells is not a standard round shape, it is impossible to accurately obtain the area value of ductal cells and ductal cells in the entire microscopic image after obtaining the lumen diameter of ductal cells. The proportion of area occupied.

Image J is a powerful image analysis program that can be downloaded and used for free. At present, in the field of biology, it has been widely used in cell counting, quantitative analysis of fluorescence intensity, blood vessel analysis, and area determination of single irregular objects. Because duct cells are surrounded by various types of cells such as fibroblasts and parenchyma cells, they are different from ordinary single discrete cells. Therefore, existing methods such as cell counting using Image J cannot accurately obtain the morphology of duct cells in the microstructure of lignocellulosic materials. Characteristic Parameters.

Contents of the invention

The main purpose of the present invention is to provide a method for quickly and comprehensively detecting the morphological characteristics of vessel cells in lignocellulosic materials in order to overcome the deficiencies of the prior art. This method can quickly and comprehensively extract the image of the morphological characteristics of the duct cells in the composition of lignocellulosic materials, and at the same time obtain morphological characteristics such as their number, area, roundness, and the proportion of their area in the cells composed of lignocellulosic materials. .

The present invention adopts following technical scheme:

A method for rapidly detecting morphological characteristics of vessel cells in lignocellulosic materials, characterized in that the method firstly prepares samples of lignocellulosic materials in order to obtain microscopic images; the prepared samples can be distinguished through optical microscopy The microscopic images of the cells of each lignocellulosic material; the obtained microscopic images are determined and processed in terms of the ratio between the image pixels and the actual size, in order to ensure the readability of the morphological characteristics of the ductal cells in the microscopic images and the accuracy of identification and detection properties; set the parameters for the detection of ductal cell morphological characteristics of the processed microscopic image; perform the detection of ductal cell morphological characteristics, and finally obtain the ductal cell image and ductal cell morphological characteristic data in the lignocellulosic material that meet the requirements of the set parameters.

The method specifically includes the following steps:

1) In order to obtain microscopic images, pre-sample preparation of lignocellulosic materials:

According to the type and shape of the lignocellulosic material, it is divided into block-shaped materials that are conducive to obtaining microscopic images, and the block-shaped materials are used as samples for the acquisition of their microscopic images;

2) Obtain a microscopic image capable of distinguishing each constituent cell of each lignocellulosic material sample through an optical microscope with the prepared sample:

The sample prepared in step 1) is cut into slices with a thickness of 10-30 μm; then the slices are stained with biological agent dyes to ensure that different types of tissue cells that make up the lignocellulosic material can be distinguished, and the staining time is based on optical microscopy. It depends on the time required for the outline of each tissue cell observed below to be clear; then the tissue cells in the section are dehydrated by graded alcohol, and xylene is used to make the dehydrated tissue cells become transparent, and the mounting medium is used for the dehydration. The sections after each tissue cell became transparent were sealed; the sections after sealing were observed and photographed under an optical microscope to obtain microscopic images of lignocellulosic material samples. For various types of lignocellulosic material samples, preservation of each sample can distinguish A microscopic image of the ductal cells in the sample;

3) Determine the proportional relationship between image pixels and actual size and image processing on the acquired microscopic image to ensure the readability of the morphological features of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including:

3.1) Determining the correlation ratio between the image pixel and the actual size: the actual length of the scale bar on the microscopic image obtained through step 2) corresponds to the pixel of the scale bar, so that the proportional relationship between the image pixel and the actual size can be determined, using To ensure that the actual size of the duct is obtained when detecting the morphological characteristics of duct cells;

3.2) Contrast enhancement processing: by adjusting the image saturation pixel value of the microscopic image of the lignocellulosic material sample whose image pixel and actual size correlation ratio is determined in step 3.1), the ductal cells, fiber cells and other types in the microscopic image are enhanced The contrast of cells helps to identify the morphological characteristics of ductal cells;

3.3) Image type conversion processing: the image after step 3.2) contrast enhancement processing is converted into a binary grayscale image, that is, an image that only has two colors of black and white to distinguish;

3.4) Vessel cell marking processing: the binary grayscale image obtained in step 3.3) is used to mark the vessel cells in the binary grayscale image by selecting a threshold, and the color of each tissue cell of the lignocellulosic material in the binary grayscale image of different thresholds Different, when only the lumen and wall colors of the duct cells are all the specified colors, the optimal threshold is set, and the microscopic image acquired through the optimal threshold has marked the duct cells in the image. Facilitate subsequent detection of ductal cells;

4) Set the detection parameters for the morphological characteristics of the ductal cells in the processed microscopic image, specifically including:

4.1) Parameter setting of ductal cell area: select the larger ductal cells and smaller ductal cells in the microscopic image processed in step 3), measure their diameters respectively, and calculate the area _S'max of the larger ductal cells in the fiber image and the area of smaller ductal cells S'_min; the set lower limit of ductal cell area S _min <S' _min , the set upper limit of ductal cell area S _max >S' _max , and the two take an integer; then The set area S of ductal cells to be detected satisfies: S _min ≤ S ≤ S _max ;

4.2) Setting of ductal cell roundness parameters: setting the ductal cell roundness parameter c to be detected according to the roundness of the ductal cell to be detected in the microscopic image processed in step 3), 0<c≤1, wherein , 1 means a regular circle;

5) Detection of ductal cell morphological characteristics: after the ductal cell morphological characteristic detection parameters set in step 4), the ductal cell image and ductal cell morphological characteristic data meeting the detection parameter requirements are obtained; the ductal cell morphological characteristic data include: ductal cell The overall information of the morphological data, the number of a single vessel cell and the corresponding area size and roundness morphological information, and finally complete the detection of the morphological characteristics of the vessel cell of the lignocellulosic material.

Features and beneficial effects of the present invention:

The present invention provides a method for quickly detecting the morphological characteristics of vessel cells in lignocellulosic materials. This method can quickly extract images of vessel cell morphology in the composition of lignocellulosic materials, and at the same time can quickly, automatically and accurately analyze leaflets. Or multiple micrographs of lignocellulosic materials for vessel cell morphological information, to obtain its number, area, roundness and other morphological characteristics, as well as the area ratio of vessel cells in the cells composed of woody materials. It fills the gap that the current traditional testing method cannot automatically obtain the morphological characteristics such as the number, area and roundness of lignocellulosic vessel cells. The method of the invention has important practical application significance for the rapid mass screening of transgenic forest trees and directional cultivated forest trees.

Description of drawings

Fig. 1 is a flowchart of the present invention.

Fig. 2 is the binary grayscale figure of three kinds of lignocellulosic material cross-section microstructures that the embodiment of the present invention provides; Fig. 2 (a) is poplar, Fig. 2 (b) is moso bamboo, and Fig. 2 (c) is yellow Rattan.

Fig. 3 is a diagram of vessel cell labeling in the microstructure of three kinds of lignocellulosic materials corresponding to Fig. 2 .

Fig. 4 is a diagram of detection and extraction of vessel cells in three kinds of lignocellulosic materials corresponding to Fig. 3 .

detailed description

The content, characteristics and technical effects of the method for rapidly detecting vessel cell morphology in lignocellulosic materials and the technical effects in application will be further described in detail below through specific examples and accompanying drawings, but the present invention is not limited thereby.

The invention provides a method for rapidly detecting the morphological characteristics of vessel cells in lignocellulosic materials. The method firstly prepares samples of lignocellulosic materials in the early stage in order to obtain microscopic images; The microscopic images of the cells of each lignocellulosic material; the obtained microscopic images are determined and processed in terms of the ratio between the image pixels and the actual size, in order to ensure the readability of the morphological characteristics of the ductal cells in the microscopic images and the accuracy of identification and detection properties; set the parameters for the detection of ductal cell morphological characteristics of the processed microscopic image; perform the detection of ductal cell morphological characteristics, and finally obtain the ductal cell image and ductal cell morphological characteristic data in the lignocellulosic material that meet the requirements of the set parameters.

The flowchart of the inventive method is referring to Fig. 1, specifically comprises the following steps:

1) In order to obtain microscopic images, pre-sample preparation of lignocellulosic materials:

According to the type and shape of lignocellulosic materials (usually including wood, bamboo and rattan), cut them into block-shaped materials that are conducive to obtaining microscopic images (for example, cutting lignocellulosic materials into cross-sectional areas less than or equal to 10mm ² and longitudinal lengths of 15mm cylinder material), the bulk material is used as a sample for its microscopic image acquisition.

2) Obtain a microscopic image capable of distinguishing each constituent cell of each lignocellulosic material sample through an optical microscope with the prepared sample:

The sample prepared in step 1) is cut into slices with a thickness of 10-30 μm; then the slices are stained with biological agent dyes (for example, using safranin, fast green or toluidine blue), so as to ensure that the components of the lignocellulosic material can be distinguished. For different types of tissue cells, the staining time depends on the time required for the outline of each tissue cell to be observed under an optical microscope; then the tissue cells in the section are dehydrated by gradient alcohol, and xylene is used to dehydrate Each tissue cell becomes transparent, and the section after each tissue cell becomes transparent is sealed with a mounting agent; the section after the sealing treatment is observed and photographed under an optical microscope, and a microscopic image of the lignocellulosic material sample is obtained. For each type of lignocellulosic material sample, saving each sample allows the microscopic images of the vessel cells in that sample to be distinguished. However, the method of the present invention for obtaining microscopic images is not limited thereby.

3) Determine the proportional relationship between image pixels and actual size and image processing on the acquired microscopic image to ensure the readability of the morphological features of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including:

3.1) Determining the correlation ratio between the image pixel and the actual size: the actual length of the scale bar on the microscopic image obtained through step 2) corresponds to the pixel of the scale bar, so that the proportional relationship between the image pixel and the actual size can be determined, using In order to ensure that the actual size of the catheter is obtained when detecting the morphological characteristics of the duct cells (for example, input the actual length of the scale bar on the microscopic image when setting the scale bar in the IMAGE J image analysis program, and set the length unit to "μm" at the same time, and detect The morphological data of the ductal cells obtained at the time will be in "μm");

3.2) Contrast enhancement processing: by adjusting the image saturation pixel value of the microscopic image of the lignocellulosic material sample whose image pixel and actual size correlation ratio is determined in step 3.1), the ductal cells, fiber cells and other types in the microscopic image are enhanced The contrast of the cells is helpful for the identification of the morphological characteristics of the ductal cells (for example, the contrast enhancement process is performed through the IMAGE J image analysis program, and the saturation pixel value range of the selected image is 0.2-0.8, and the contrast between ductal cells and other types of cells can be obtained after processing. Image);

3.3) Image type conversion processing: convert the image after step 3.2) contrast enhancement processing into a binary grayscale image, that is, an image that only has two colors of black and white to distinguish (such as selecting the image type as 8-bit type by IMAGE J image analysis program );

3.4) Vessel cell marking processing: the binary grayscale image obtained in step 3.3) is used to mark the vessel cells in the binary grayscale image by selecting a threshold, and the color of each tissue cell of the lignocellulosic material in the binary grayscale image of different thresholds Different, when only the lumen and wall colors of the duct cells are all the specified colors, the optimal threshold is set, and the microscopic image acquired through the optimal threshold has marked the duct cells in the image. Facilitate the subsequent detection of ductal cells (for example, through the IMAGE J image analysis program, the optimal threshold value of the grayscale image is selected in the range of 50-255, when the optimal threshold value, the color of the cavity and wall of ductal cells is all red);

4) Set the detection parameters for the morphological characteristics of the ductal cells in the processed microscopic image, specifically including:

Before performing the detection of the morphological characteristics of the vessel in the microscopic image of the cross-section of the lignocellulosic material sample, it is necessary to set the morphological characteristic parameters of the vessel cells to be detected, which are divided into the parameter setting of the vessel area and the setting of the roundness parameter:

4.1) Parameter setting of ductal cell area: select larger ductal cells and smaller ductal cells in the microscopic image processed in step 3) and measure their diameters d (since the correlation ratio between image pixels and actual size is known, over The center of the circle draws a straight line on the inner walls of both sides of the ductal cells to obtain the actual size of its diameter). Assuming that the ductal cells are in a regular circle, roughly calculate the area of the larger ductal cells S' _max and the area of the smaller ductal cells S' _min in the microscopic image according to the formula π×(d/2) ² , and preliminarily determine the range of the ductal cell area [S' _min , S' _max ], in order not to miss the smallest ductal cell and the largest ductal cell, the lower limit of the ductal cell area is usually set S _min <S' _min , and the upper limit of the ductal cell area is set S _max >S' _max , usually the two take an integer; then the set ductal cell area S to be detected satisfies: S _min ≤ S ≤ S _max , and S is usually an integer;

4.2) Circularity parameter setting of ductal cells: set the circularity parameter c of ductal cells to be detected according to the circularity of ductal cells to be detected in the microscopic image processed in step 3), 0<c≤ 1, wherein, 1 represents a regular circle (such as using IMAGE J image analysis program, set the appropriate duct cell roundness range in the roundness range between 0-1 and c, 0<c≤1, as selected 1. It can be detected that the duct cells are standard round; the IMAGE J image analysis program determines the roundness value of each duct cell according to the formula 4π×[duct cell area]/[duct cell perimeter] ² );

5) Detection of ductal cell morphological characteristics: after the ductal cell morphological characteristic detection parameters set in step 4), the ductal cell image and ductal cell morphological characteristic data meeting the detection parameter requirements are obtained; the ductal cell morphological characteristic data include: ductal cell Overall information on morphological data (including the total number of detected ductal cells, total area, average area, average roundness, and the proportion of all detected ductal cells in the entire microscopic image), the number of individual ductal cells and the corresponding Area size and roundness morphological information, and finally complete the detection of lignocellulosic material vessel cell morphological characteristics (such as according to the detection parameters input in steps 4.1) and 4.2) by executing the IMAGE J image analysis particle program to obtain the detection results).

Example 1

1) In order to obtain the microscopic image of poplar wood, pre-sample preparation of poplar wood material:

According to the shape of poplar wood, a lignocellulosic material, poplar wood with a cross-sectional area of less than 10 mm ² is cut into a column material with a longitudinal length of 15 mm, and the block material is used as a sample for its microscopic image acquisition.

2) The prepared sample is obtained through an optical microscope to obtain a microscopic image that can distinguish each component cell of the poplar sample:

The sample prepared in step 1) was cut into slices with a thickness of 20 μm by conventional wood sectioning method; then the slices were dyed with 1% safranin aqueous solution dye, and the staining time was 10-12 hours; Each tissue cell was dehydrated, and xylene was used to make the dehydrated tissue cells transparent, and the sections after the tissue cells became transparent were sealed with Canada gum mounting agent; Observe and take pictures under observation, acquire and save the microscopic image of the cross-section of the above-mentioned poplar wood sample.

3) Determine the proportional relationship between image pixels and actual size and image processing on the obtained poplar microscopic image, so as to ensure the readability of the morphological characteristics of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including:

3.1) Determine the correlation ratio between image pixels and actual size: Open the poplar microscopic image obtained through step 2) through the image analysis program IMAGE J, and use the straight line tool at the window to draw a straight line with the same length as the scale on the image, which can be automatically obtained The pixel distance value of the straight line, and then input the actual length value of the scale to determine the proportional relationship between the image pixel of the microscopic image and the actual size. Will be in "μm" as the unit;

3.2) Contrast enhancement processing: adjust the image saturation pixel value of the microscopic image of the poplar sample whose correlation ratio between the image pixel and the actual size is determined by step 3.1) through the IMAGE J image analysis program, and enhance the vessel cells and fibers in the microscopic image For the contrast of cells and other types of cells, the saturation pixel value of the selected image is 0.6. After processing, images with obvious contrast between ductal cells and other types of cells can be obtained;

3.3) Image type conversion processing: convert the image after step 3.2) contrast enhancement processing into an 8-bit binary grayscale image through the IMAGE J image analysis program, that is, an image that only has two colors of black and white, as shown in Figure 2 (a) shown;

3.4) Marking processing of ductal cells: the binary grayscale image obtained in step 3.3) is used to mark the ductal cells in the binary grayscale image through the image analysis program IMAGE J, and the optimal threshold range of the microscopic image of the poplar sample is 100 -255, at this time, the lumen and wall coloring of the duct cells are all red, and the duct cell labeling process has been completed, which is converted into a non-color image as shown in Figure 3 (a) (the dark gray in the figure indicates the marked duct cells);

4) Set the detection parameters for the morphological characteristics of the ductal cells in the processed microscopic image, specifically including:

4.1) Setting of ductal cell area parameters: use the IMAGE J particle analysis program to select larger ductal cells and smaller ductal cells in the microscopic image processed in step 3), measure their diameters respectively, and then use the formula π×(d/ 2) ² Roughly calculate the area of larger ductal cells and smaller ductal cells in the microscopic image, and finally determine the range of ductal cell area as [400,2000] according to the size of ducts in the cross-section of the poplar sample. This setting can Detect ductal cells with a ductal cell area in the range of 400-2000 μm2 ^;

4.2) parameter setting of duct cell roundness: according to the roundness shape of duct cells to be detected in the microscopic image processed in step 3), the roundness parameters of duct cells to be detected are set. It is relatively irregular, so the roundness parameter is selected as 0.3-1, wherein the larger the roundness parameter, the closer the catheter shape is to a circle, and 1 means a regular circle.

5) Detection of morphological characteristics of ductal cells: After the above-mentioned detection parameters of morphological characteristics of ductal cells have been set for the microscopic images of poplar wood samples, the image of ductal cells and ductal cell For the morphological feature data, select OUTLINES, DISPLAY SESULTS, SUMMARIZE and IN SITU SHOW to set the output result form during the detection output. The microscopic image of the duct shape extraction of the cross-section of the poplar sample obtained by the final detection is shown in Figure 4(a). In Fig. 4 (a), there is a record of the duct cell number in the middle of the duct cells (that is, the black dot in the middle of each duct cell shown in the figure, and its specific value can be seen clearly after zooming in); the total number and total area of the poplar wood duct cells obtained , average area, average roundness, and the area ratio of all detected ductal cells in the entire microscopic image and other morphological data are shown in Table 1; the number of a single ductal cell and the corresponding area size and roundness morphological information As shown in Table 2, the detection of the morphological characteristics of poplar duct cells was finally completed.

Table 1: Overall values of vessel cell area and roundness in poplar wood materials

Table 2: Single vessel cell area and roundness values in poplar wood materials

Example 2

1) In order to obtain the microscopic image of the moso bamboo wood, the pre-sample preparation of the moso bamboo wood was carried out:

According to the shape of the lignocellulosic material Moso bamboo, cut the stem ring of Moso bamboo with a cross-sectional area greater than 10 mm into a cuboid material of 10 mm (radial direction) × 10 mm (chord direction) × 15 mm (longitudinal direction). The bulk material was used as a sample for its microscopic image acquisition.

2) The prepared sample is obtained by an optical microscope to be able to distinguish the microscopic image of each component cell of the moso bamboo sample:

With the sample prepared in step 1), except that the section is stained with 2% safranin alcohol dye, and the staining time is 2-4 hours, other operations are carried out the same as in Example 1.

3) Determine and process the relationship between the image pixel and the actual size ratio of the obtained microscopic image of moso bamboo, so as to ensure the readability of the morphological characteristics of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including:

3.1) Determining the correlation ratio between image pixels and actual size: same as embodiment 1;

3.2) contrast enhancement processing: same as embodiment 1;

3.3) image type conversion processing: the same as in embodiment 1, obtain the binary grayscale image of the cross-section of the moso bamboo sample as shown in Figure 2 (b);

3.4) Vessel cell labeling process: the optimal threshold range of the microscopic image of the moso bamboo sample is 170-255, and other operations are carried out the same as in Example 1, and after the labeling, it is converted into an achromatic image as shown in Figure 3 (b) (in the figure Dark gray indicates labeled ductal cells).

4) Set the parameters for detecting the morphological characteristics of the duct cells in the microscopic image of the processed bamboo wood sample, specifically including:

4.1) Vessel cell area parameter setting: finally determine the value of the vessel cell area of Moso bamboo wood as [8000,20000]; this setting can detect vessel cells with a vessel cell area within the range of 8000-20000 μm ² , other operations are implemented with Embodiment 1 is identical;

4.2) Setting of roundness parameters of vessel cells: the roundness parameter of the selected moso bamboo sample is 0.5-1, and other operations are carried out the same as in Example 1;

5) Detection of morphological characteristics of vessel cells: the detection process is the same as in Example 1, and the microscopic image extracted from the vessel morphology detection of the cross-section of the moso bamboo sample obtained in the final detection is shown in Figure 4 (b), and the vessel cells in Figure 4 (b) The number of vessel cells is recorded in the middle (that is, the black dot in the middle of each vessel cell shown in the figure, and its specific value can be seen clearly after zooming in); the total number, total area, average area, average roundness and The overall morphological information such as the area ratio of all detected ductal cells in the entire microscopic image is shown in Table 3; the number of a single ductal cell and the corresponding area size and roundness morphological information are shown in Table 4, and the final completion The morphological characteristics of the vessel cells of Moso bamboo were detected.

Table 3: Overall values of conduit area and roundness in moso bamboo

Table 4: Area and roundness values of individual tubes in moso bamboo

Example 3

1) In order to obtain the microscopic image of the yellow rattan wood, the pre-sample preparation of the yellow rattan wood:

The shape of the cut rattan material sample is the same as in Example 2. The bulk material was used as a sample for its microscopic image acquisition.

2) Obtain a microscopic image capable of distinguishing each constituent cell of the rattan material sample through an optical microscope: the acquisition procedure of the microscopic image of the rattan material is the same as in Example 2.

3) Determine and process the relationship between the image pixel and the actual size ratio of the obtained microscopic image of the rattan wood, so as to ensure the readability of the morphological characteristics of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including:

3.1) Determining the correlation ratio between image pixels and actual size: same as embodiment 1;

3.2) contrast enhancement processing: same as embodiment 1;

3.3) image type conversion processing: same as embodiment 1, obtain the binary grayscale image of the cross-section of the rattan wood as shown in Figure 2 (c);

3.4) Labeling treatment of ductal cells: the optimal threshold range of the microscopic image of the rattan wood sample is 153-255, and other operations are carried out in the same manner as in Example 1. After marking, it is converted into a non-color image as shown in Figure 3 (c) (Fig. Medium dark gray indicates labeled ductal cells).

4) Set the parameters for detection of ductal cell morphological characteristics in the microscopic image of the treated rattan wood sample, specifically including:

4.1) Parameter setting of ductal cell area: finally determine the value of the area of ductal cells in the yellow rattan material as [20000, 50000]; this setting can detect ductal cells with an area of ductal cells within the range of ^20000-50000 μm2, and perform other operations Same as embodiment 1;

4.2) Setting of roundness parameters of duct cells: the roundness parameter of the selected rattan material sample is 0.8-1, and other operations are carried out the same as in Example 1;

5) Detection of ductal cell morphological characteristics: the detection process is the same as in Example 1, and the microscopic image extracted from the ductal morphology detection of the cross-section of the rattan material sample obtained in the final inspection is shown in Figure 4(c). The number of ductal cells is recorded in the middle of the cells (that is, the black dots in the middle of each ductal cell shown in the figure, and the specific value can be seen clearly after zooming in); the total number, total area, average area, and average roundness of the obtained yellow rattan ductal cells The general information of the morphological data such as the area ratio of all detected ductal cells in the entire microscopic image is shown in Table 5; the number of a single ductal cell and the corresponding area size and roundness morphological information are shown in Table 6, and finally The detection of the morphological characteristics of the vessel cells of the rattan cane was completed.

Table 5: Overall values of conduit area and roundness in yellow rattan materials

Table 6: Single conduit area and roundness values in yellow rattan material

Claims (2)

1. A method for rapid detection of vessel cell morphological characteristics in lignocellulosic materials, characterized in that, the method is at first to obtain microscopic images, and the lignocellulosic materials are prepared in the early stage of samples; the prepared samples are obtained by optical microscope to obtain energy Distinguish the microscopic images of the cells that make up each lignocellulosic material; determine and process the obtained microscopic images in terms of the ratio between the image pixels and the actual size, in order to ensure the readability, recognition and detection of the morphological characteristics of the ductal cells in the microscopic images The accuracy; set the parameters of the morphological characteristics of the ductal cells in the processed microscopic image; detect the morphological characteristics of the ductal cells, and finally obtain the image of the ductal cells in the lignocellulosic material and the data of the morphological characteristics of the ductal cells that meet the requirements of the set parameters. 2. The method according to claim 1, characterized in that the method specifically comprises the following steps: 1) In order to obtain microscopic images, pre-sample preparation of lignocellulosic materials: According to the type and shape of the lignocellulosic material, it is divided into block-shaped materials that are conducive to obtaining microscopic images, and the block-shaped materials are used as samples for the acquisition of their microscopic images; 2) Obtain a microscopic image capable of distinguishing each constituent cell of each lignocellulosic material sample through an optical microscope with the prepared sample: The sample prepared in step 1) is cut into slices with a thickness of 10-30 μm; then the slices are stained with biological agent dyes to ensure that different types of tissue cells that make up the lignocellulosic material can be distinguished, and the staining time is based on optical microscopy. It depends on the time required for the outline of each tissue cell observed below to be clear; then the tissue cells in the section are dehydrated by graded alcohol, and xylene is used to make the dehydrated tissue cells become transparent, and the mounting medium is used for the dehydration. The sections after each tissue cell became transparent were sealed; the sections after sealing were observed and photographed under an optical microscope to obtain microscopic images of lignocellulosic material samples. For various types of lignocellulosic material samples, preservation of each sample can distinguish A microscopic image of the ductal cells in the sample; 3) Determine the proportional relationship between image pixels and actual size and image processing on the acquired microscopic image to ensure the readability of the morphological features of the ductal cells in the microscopic image and the accuracy of identification and detection, specifically including: 3.1) Determining the correlation ratio between the image pixel and the actual size: the actual length of the scale bar on the microscopic image obtained through step 2) corresponds to the pixel of the scale bar, so that the proportional relationship between the image pixel and the actual size can be determined, using To ensure that the actual size of the duct is obtained when detecting the morphological characteristics of duct cells; 3.2) Contrast enhancement processing: by adjusting the image saturation pixel value of the microscopic image of the lignocellulosic material sample whose image pixel and actual size correlation ratio is determined in step 3.1), the ductal cells, fiber cells and other types in the microscopic image are enhanced The contrast of cells helps to identify the morphological characteristics of ductal cells; 3.3) Image type conversion processing: the image after step 3.2) contrast enhancement processing is converted into a binary grayscale image, that is, an image that only has two colors of black and white to distinguish; 3.4) Marking processing of ductal cells: the binary grayscale image obtained in step 3.3) is used to mark the ductal cells in the binary grayscale image by selecting a threshold, and when only the lumen and wall colors of the ductal cells are all the specified colors, it is set as the best color. Optimum threshold value, the microscopic image acquired through the optimal threshold value has already carried out labeling process to the ductal cells in the image, and this labeling is convenient for the subsequent detection of ductal cells; 4) Set the detection parameters for the morphological characteristics of the ductal cells in the processed microscopic image, specifically including: 4.1) Parameter setting of ductal cell area: select the larger ductal cells and smaller ductal cells in the microscopic image processed in step 3), measure their diameters respectively, and calculate the area _S'max of the larger ductal cells in the fiber image and the area of smaller ductal cells S'_min; the set lower limit of ductal cell area S _min <S' _min , the set upper limit of ductal cell area S _max >S' _max , and the two take an integer; then The set area S of ductal cells to be detected satisfies: S _min ≤ S ≤ S _max ; 4.2) Setting of ductal cell roundness parameters: setting the ductal cell roundness parameter c to be detected according to the roundness of the ductal cell to be detected in the microscopic image processed in step 3), 0<c≤1, wherein , 1 means a regular circle; 5) Detection of ductal cell morphological characteristics: after the ductal cell morphological characteristic detection parameters set in step 4), the ductal cell image and ductal cell morphological characteristic data meeting the detection parameter requirements are obtained; the ductal cell morphological characteristic data include: ductal cell The overall information of the morphological data, the number of a single vessel cell and the corresponding area size and roundness morphological information, and finally complete the detection of the morphological characteristics of the vessel cell of the lignocellulosic material.