Image-based chloroplast distribution characteristic characterization method and system

Abstract

The invention discloses an image-based chloroplast distribution characteristic characterization method and system, and belongs to the crossing field of plant physiology and image processing technologies. The method solves the problem that a conventional method for chloroplast distribution characteristics does not quantify the spatial distribution characteristics of the chloroplast. The method comprises the following steps: S1, acquiring a chloroplast microscopic image; s2, preprocessing the chloroplast microscopic image; s3, chloroplast microscopic image distribution characteristic parameters are calculated; and S4, constructing chloroplast microscopic image distribution characteristic quantitative indexes to represent chloroplast distribution characteristics. According to the method, a brand new chloroplast distribution characteristic characterization mode is introduced, brand new parameter skewness and kurtosis are introduced, and a quantitative index capable of characterizing chloroplast distribution characteristics is constructed on the basis, so that a unified chloroplast spatial distribution characteristic characterization method is provided, and the method has universality and accuracy.

Description

Technical Field

[0001] The present invention belongs to the cross - field of plant physiology and image processing technology, and particularly relates to a method for characterizing chloroplast distribution characteristics based on images. Background Art

[0002] Chloroplasts are important organelles in plant cells that perform photosynthesis. Their distribution characteristics within cells are closely related to the photosynthesis efficiency of plants, the cell metabolic state, and the response to environmental stresses. In recent years, with the in - depth study of plant physiology, researchers have found that the distribution of chloroplasts shows dynamic changes within cells, and its distribution characteristics are closely related to environmental conditions (light intensity, temperature). However, current research on chloroplast distribution characteristics is mostly based on qualitative observations, lacking a unified quantitative index to accurately describe and standardize the analysis of its distribution characteristics.

[0003] Existing analysis methods, such as microscope observation and image processing technology, are mainly used for the study of chloroplast morphology and quantity, but have not systematically constructed a scientific quantitative method to describe their spatial distribution characteristics. This technical gap limits the in - depth study of chloroplast distribution rules and also cannot provide quantitative support for related plant phenotype analysis and physiological modeling.

[0004] Therefore, developing a method that can scientifically and standardly quantify chloroplast distribution characteristics is of great significance for promoting plant cell biology research and exploring the photosynthesis regulation mechanism. Summary of the Invention

[0005] In order to solve the problem that the previous methods for chloroplast distribution characteristics do not have the ability to quantify their spatial distribution characteristics, the present invention proposes a method for characterizing chloroplast distribution characteristics based on images, and the method includes the following steps: S1. Obtain chloroplast microscopic images; S2. Pre - process the chloroplast microscopic images; S3. Calculate the distribution characteristic parameters of the chloroplast microscopic images; S4. Construct a distribution characteristic quantification index of the chloroplast microscopic images to characterize the chloroplast distribution characteristics.

[0006] Further, the pre - processing is specifically to perform region segmentation on the chloroplast microscopic images and extract the green channel.

[0007] Further, the distribution characteristic parameters of the chloroplast microscopic images include skewness and kurtosis.

[0008] Further, the skewness is obtained by:

[0009] where is the skewness, N is the total number of pixels, is the intensity of the th pixel, and

[0010] is the average value of the intensities of all pixels.

[0011] Furthermore, the kurtosis is obtained by: where

[0012] is the kurtosis.

[0013] Furthermore, the chloroplast microscopic image distribution feature quantification index is obtained by: where represents the coefficient of the sum of skewness and kurtosis, and represents the constant term.

[0014] Furthermore, and are obtained specifically by the following steps: A1. Select the chloroplast distribution feature parameters under , , light intensity as data; A2. Initialize the values of and in to 1, and substitute the chloroplast distribution feature parameters under , , light intensity into the initialized ; A3. Adjust the values of and to make the index value under the given light intensity reach a value that conforms to the actual situation, and finally obtain the numerical values of the parameters and .

[0015] The present invention also provides an image-based chloroplast distribution feature characterization system, which includes: a module for acquiring chloroplast microscopic images; a module for preprocessing chloroplast microscopic images; a module for calculating chloroplast microscopic image distribution feature parameters; a module for constructing a chloroplast microscopic image distribution feature quantification index to characterize chloroplast distribution features.

[0016] The beneficial effects of the method of the present invention are: Currently, the research on the distribution characteristics of chloroplasts is mostly based on qualitative observations, lacking a unified quantitative index to accurately describe and standardize the analysis of its distribution characteristics. Existing analysis methods, such as microscopic observation and image processing technology, are mainly used for the research of chloroplast morphology and quantity, but fail to systematically construct a scientific quantitative method to describe its spatial distribution characteristics. The present invention introduces a new way to characterize the distribution characteristics of chloroplasts. By introducing new parameters skewness and kurtosis, and based on this, a quantitative index that can characterize the distribution characteristics of chloroplasts is constructed to provide a unified method for characterizing the spatial distribution characteristics of chloroplasts. The method has universality and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method for characterizing the distribution characteristics of chloroplasts based on images in the embodiment of the present invention; Figure 2 In the embodiment of the present invention , , The chloroplast distribution image; Figure 3 It is a function image of the quantitative index of the chloroplast distribution characteristics in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1, This embodiment provides a method for characterizing the distribution characteristics of chloroplasts based on images. As Figure 1 shown, the method includes the following steps: S1. Obtain microscopic images of chloroplasts; First, select healthy and disease-free plant plants. During the collection process, preferably select intact leaves during growth. When collecting leaves, carefully cut them with sterile scissors and group them. Then wash the leaves to remove dust and impurities on the surface of the leaf samples. Gently blot the washed leaves dry, wrap wet absorbent cotton around the petiole to ensure the freshness of the leaves. Use tweezers to take the mesophyll cells of the leaves, make microscopic slides, place them under an optical microscope in a dark box, and process different groups with different light intensities. Obtain microscopic images of their chloroplasts through a computer connected to the microscope.

[0020] S2. Preprocess the microscopic images of chloroplasts; Preprocess the obtained images using Photoshop software, intercept the png images of single cells, and extract the green channels of the intercepted png images.

[0021] S3. Calculate the distribution characteristic parameters of the chloroplast microscopic images; The distribution characteristic parameters of the chloroplast microscopic images include skewness and kurtosis.

[0022] The skewness is obtained by: where is the skewness, N is the total number of pixels, is the intensity of the th pixel, is the average value of all pixel intensities.

[0023] The kurtosis is obtained by: where is the kurtosis, and subtracting 3 is to make the kurtosis of the normal distribution equal to 0.

[0024] S4. Construct a quantification index of the chloroplast microscopic image distribution characteristics to characterize the chloroplast distribution characteristics.

[0025] The quantification index of the chloroplast microscopic image distribution characteristics is obtained by: where represents the quantification index of the chloroplast microscopic image distribution characteristics, represents the coefficient of the sum of skewness and kurtosis, represents the constant term.

[0026] and The acquisition steps are specifically as follows: A1. Select the chloroplast distribution characteristic parameters under , , light intensities as data; A2. Initialize the values of in and to 1, and substitute the chloroplast distribution characteristic parameters under , , light intensities into the initialized ; A3. Adjust the values of and to make the index value under the given light intensity reach a value that conforms to the actual situation, and finally obtain the numerical values of the parameters and .

[0027] Example 2 This example further limits Example 1 and illustrates Example 1 in a specific implementation manner.

[0028] Obtain the light intensity data and chloroplast distribution image data of the leaves.

[0029] First, select healthy and disease-free pepper plants, collect intact leaves during growth, carefully cut the leaves with sterile scissors and divide them into 3 groups. Then wash the leaves to remove dust and impurities on the surface of the leaf samples. Gently blot the washed leaves dry, wrap wet absorbent cotton around the petiole, use forceps to pick the mesophyll cells of the leaves, make microscopic slides, and place them under an optical microscope in a dark box. Each group is treated with , , light intensity, and obtain the microscopic images of their chloroplasts through the computer connected to the microscope. The obtained microscopic images are as shown in Figure 2 .

[0030] Use Photoshop software to preprocess the obtained images, intercept the png images of single cells, and extract the green channels of the intercepted png images.

[0031] Calculate the chloroplast distribution characteristic parameters for the extracted pixel intensities.

[0032] The calculation methods for different chloroplast distribution characteristic parameters are as follows: The calculation formula for skewness is: ; The skewness values obtained from the experiment are 0.7142, 0.4365, and 0.0977 respectively.

[0033] The calculation formula for kurtosis is: ; The kurtosis values obtained from the experiment are -0.1579, -0.7352, and -1.2095 respectively.

[0034] Based on the trends of skewness and kurtosis and the actual situation, it is speculated that the skewness range is 0 - 1, and the kurtosis range is -1.5 - 0.

[0035] Construct an exponential model based on the chloroplast distribution image law and the chloroplast distribution characteristic parameter data law. In order to make the exponential value conform to the actual situation and be reasonably controlled between 0 and 1, adjust the parameters of the model, and finally obtain the chloroplast distribution characteristic index model with a = 3 and b = 0.9.

[0036] ; Among them, the experimentally obtained distribution index values are 0.93, 0.50, and 0.08 respectively.

[0037] As Figure 3 shown is the function image of the chloroplast distribution characteristic quantization index, where the abscissa represents the sum of kurtosis and skewness, and the ordinate represents the chloroplast distribution characteristic quantization index.

[0038] Example 3 This example provides an image-based chloroplast distribution characteristic characterization system, and the system includes: A module for acquiring chloroplast microscopic images.

[0039] A module for preprocessing the chloroplast microscopic images, and the preprocessing is specifically to perform region segmentation and extract the green channel on the chloroplast microscopic images.

[0040] A module for calculating the chloroplast microscopic image distribution characteristic parameters, and the chloroplast microscopic image distribution characteristic parameters include skewness and kurtosis. The skewness is obtained through:

[0041] where is the skewness, N is the total number of pixels, is the th pixel intensity, is the average value of all pixel intensities; the kurtosis is obtained through:

[0042] where is the kurtosis.

[0043] A module for constructing a chloroplast microscopic image distribution characteristic quantization index to characterize the chloroplast distribution characteristic. The chloroplast microscopic image distribution characteristic quantization index is obtained through:

[0044] where represents the chloroplast microscopic image distribution characteristic quantization index, represents the coefficient of the sum of skewness and kurtosis, represents the constant term; and The acquisition steps of A1. Select , , The chloroplast distribution characteristic parameters under the light intensity as data; A2. Initialize in and The values of are 1, and , , The characteristic parameters of chloroplast distribution under light intensity are substituted into the initialized ; A3. Adjust and the values so that the exponential value under the given light intensity reaches a value that conforms to the actual situation, and finally obtain the values of the parameters and .

Claims

1. A method for characterizing chloroplast distribution characteristics based on an image, characterized in that: The method comprises the following steps: S1. Obtain chloroplast microscopic images; S2, preprocessing chloroplast microscopic images; S3, calculating the distribution characteristic parameters of chloroplast microscopic images; S4. Construct a quantitative index of chloroplast microscopic image distribution characteristics to characterize chloroplast distribution characteristics.

2. The image-based chloroplast distribution feature characterization method according to claim 1, characterized in that: The preprocessing specifically includes performing regional segmentation on the chloroplast microscopic image and extracting the green channel.

3. The image-based chloroplast distribution feature characterization method according to claim 2, characterized in that: The chloroplast microscopic image distribution characteristic parameters include skewness and kurtosis.

4. The image-based chloroplast distribution feature characterization method according to claim 3, characterized in that: The skewness is given by: Obtain, among which is the skewness, N is the total number of pixels, For the The intensity of a pixel, is the average of all pixel intensities.

5. The method for characterizing chloroplast distribution characteristics based on images according to claim 4, characterized in that: The kurtosis is given by: Obtain, among which is the kurtosis.

6. The image-based chloroplast distribution feature characterization method according to claim 5, characterized in that: The chloroplast microscopic image distribution characteristic quantitative index is: Obtain, among which Represents the quantitative index of chloroplast microscopic image distribution characteristics, The coefficient representing the sum of skewness and kurtosis, Represents a constant term.

7. The image-based chloroplast distribution feature characterization method according to claim 6, characterized in that: and The specific steps for obtaining are: A1. Select , , The characteristic parameters of chloroplast distribution under light intensity were used as data; A2. Initialization middle and The value of is 1, , , Substitute the chloroplast distribution characteristic parameters under light intensity into the initialized middle; A3. Adjustment and The value of , so that the index value under a given light intensity reaches a value that conforms to the actual situation, and finally the parameter and The numerical value of .

8. A chloroplast distribution feature characterization system based on an image, characterized in that: The system comprises: Module for acquiring microscopic images of chloroplasts; A module for preprocessing chloroplast microscopy images; A module for calculating the distribution characteristic parameters of chloroplast microscopic images; A module is built to characterize the distribution characteristics of chloroplasts by constructing a quantitative index of the distribution characteristics of chloroplast microscopic images.

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