A method and system for characterizing chloroplast distribution features based on images

Abstract

A method and system for characterizing chloroplast distribution features based on images belongs to the interdisciplinary field of plant physiology and image processing technology. This invention solves the problem that previous methods for chloroplast distribution feature characterization lacked the ability to quantify its spatial distribution characteristics. The method includes the following steps: S1, acquiring chloroplast microscopic images; S2, preprocessing the chloroplast microscopic images; S3, calculating the chloroplast microscopic image distribution feature parameters; S4, constructing a quantification index of chloroplast microscopic image distribution features to characterize chloroplast distribution features. This invention introduces a novel method for characterizing chloroplast distribution features by introducing new parameters skewness and kurtosis, and based on these, constructing a quantification index that can characterize chloroplast distribution features, thus providing a unified method for characterizing chloroplast spatial distribution features. This method has both universality and accuracy.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of plant physiology and image processing technology, and specifically relates to an image-based method for characterizing chloroplast distribution features. Background Technology

[0002] Chloroplasts are crucial organelles in plant cells that perform photosynthesis. Their intracellular distribution is closely related to photosynthetic efficiency, cellular metabolic state, and response to environmental stresses. In recent years, with the deepening of plant physiology research, researchers have discovered that chloroplast distribution within cells exhibits dynamic changes, and its characteristics are closely related to environmental conditions (light intensity, temperature). However, current research on chloroplast distribution characteristics is mostly based on qualitative observations, lacking unified quantitative indicators for accurate description and standardized analysis of these distribution features.

[0003] Existing analytical methods, such as microscopic observation and image processing techniques, are mainly used to study chloroplast morphology and quantity, but they have failed to systematically construct a scientific quantitative method to describe their spatial distribution characteristics. This technological gap limits in-depth research on chloroplast distribution patterns and also fails to provide quantitative support for related plant phenotypic analysis and physiological modeling.

[0004] Therefore, developing a scientific and standardized method to quantify chloroplast distribution characteristics is of great significance for promoting plant cell biology research and exploring the regulatory mechanisms of photosynthesis. Summary of the Invention

[0005] To address the problem that previous methods for chloroplast distribution features lacked the ability to quantify their spatial distribution characteristics, this invention proposes an image-based method for characterizing chloroplast distribution features, comprising the following steps:

[0006] S1. Obtain chloroplast microscopic images;

[0007] S2. Preprocess the chloroplast microscopic images;

[0008] S3. Calculate the distribution characteristic parameters of chloroplast microscopic images;

[0009] S4. Construct a quantitative index of chloroplast microscopic image distribution characteristics to characterize chloroplast distribution characteristics.

[0010] Furthermore, the preprocessing specifically involves region segmentation and extraction of the green channel from the chloroplast microscopic image.

[0011] Furthermore, the chloroplast microscopic image distribution characteristic parameters include skewness and kurtosis.

[0012] Furthermore, the skewness is achieved through:

[0013]

[0014] Obtain, among which Where is the skewness, and N is the total number of pixels. For the first Intensity of each pixel This is the average intensity of all pixels.

[0015] Furthermore, the kurtosis is determined by:

[0016]

[0017] Obtain, among which For peak value.

[0018] Furthermore, the quantification index of the chloroplast microscopic image distribution characteristics is obtained through:

[0019]

[0020] Obtain, among which A quantification index representing the distribution characteristics of chloroplasts in a microscopic image. The coefficient representing the sum of skewness and kurtosis. This represents a constant term.

[0021] further, and The specific steps to obtain it are as follows:

[0022] A1, Select , , The chloroplast distribution characteristics under light intensity were used as data;

[0023] A2. Initialization middle and The value is 1, , , Substitute the chloroplast distribution characteristic parameters under light intensity into the initialization. middle;

[0024] A3. Adjustment and The value of is determined so that the exponent value under a given light intensity reaches a value that conforms to the actual situation, and the parameter is finally obtained. and The value.

[0025] The present invention also provides an image-based chloroplast distribution feature characterization system, the system comprising:

[0026] A module for acquiring chloroplast microscopic images;

[0027] A module for preprocessing chloroplast microscopic images;

[0028] A module for calculating the distribution characteristic parameters of chloroplast microscopic images;

[0029] A module for constructing a quantitative index of chloroplast microscopic image distribution characteristics to characterize chloroplast distribution features.

[0030] The beneficial effects of the method described in this invention are as follows:

[0031] Current research on chloroplast distribution characteristics is mostly based on qualitative observation, lacking a unified quantitative index to accurately describe and standardize its distribution characteristics. Existing analytical methods, such as microscopic observation and image processing techniques, are mainly used to study chloroplast morphology and quantity, but have failed to systematically construct a scientific quantitative method to describe its spatial distribution characteristics. This invention introduces a novel method for characterizing chloroplast distribution characteristics by introducing new parameters skewness and kurtosis, and based on these, constructing a quantitative index that can characterize chloroplast distribution characteristics, thus providing a unified method for characterizing chloroplast spatial distribution characteristics. This method is both universal and accurate. Attached Figure Description

[0032] Figure 1 This is a flowchart of the image-based chloroplast distribution feature characterization method described in this embodiment of the invention;

[0033] Figure 2 In the embodiments of the present invention , , Image of lower chloroplast distribution;

[0034] Figure 3 This is a function image of the chloroplast distribution characteristic quantification index in an embodiment of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0036] Example 1

[0037] This embodiment provides an image-based method for characterizing chloroplast distribution features, such as... Figure 1 As shown, the method includes the following steps:

[0038] S1. Obtain chloroplast microscopic images;

[0039] First, select healthy, disease-free plant specimens. During collection, prioritize intact leaves that are still growing. Carefully cut the leaves using sterile scissors and group them. Then, wash the leaves to remove dust and impurities from the leaf sample surface. Gently pat the washed leaves dry and wrap the petiole with moistened absorbent cotton to ensure freshness. Use tweezers to extract mesophyll cells from the leaves, prepare microscopic slides, and place them under an optical microscope in a darkroom. Different groups are treated with different light intensities, and microscopic images of the chloroplasts are acquired via a computer connected to the microscope.

[0040] S2. Preprocess the chloroplast microscopic images;

[0041] The obtained image was preprocessed using Photoshop software to extract a PNG image of a single cell, and the green channel of the extracted PNG image was then extracted.

[0042] S3. Calculate the distribution characteristic parameters of chloroplast microscopic images;

[0043] The chloroplast microscopic image distribution characteristic parameters include skewness and kurtosis.

[0044] The skewness is achieved through:

[0045] Obtain, among which Where is the skewness, and N is the total number of pixels. For the first Intensity of each pixel This is the average intensity of all pixels.

[0046] The kurtosis is obtained through:

[0047] Obtain, among which The kurtosis is 0, and subtracting 3 is to make the kurtosis of the normal distribution zero.

[0048] S4. Construct a quantitative index of chloroplast microscopic image distribution characteristics to characterize chloroplast distribution characteristics.

[0049] The quantification index of chloroplast microscopic image distribution features is obtained through:

[0050] Obtain, among which A quantification index representing the distribution characteristics of chloroplasts in a microscopic image. The coefficient representing the sum of skewness and kurtosis. This represents a constant term.

[0051] and The specific steps to obtain it are as follows:

[0052] A1, Select , , The chloroplast distribution characteristics under light intensity were used as data;

[0053] A2. Initialization middle and The value is 1, , , Substitute the chloroplast distribution characteristic parameters under light intensity into the initialization. middle;

[0054] A3. Adjustment and The value of is determined so that the exponent value under a given light intensity reaches a value that conforms to the actual situation, and the parameter is finally obtained. and The value.

[0055] Example 2

[0056] This embodiment further defines Embodiment 1, and describes Embodiment 1 with specific implementation methods.

[0057] Acquire light intensity data and chloroplast distribution image data of the leaves.

[0058] First, select healthy, disease-free chili pepper plants and collect intact leaves during growth. Carefully cut the leaves using sterile scissors and divide them into three groups. Then, wash the leaves to remove dust and impurities from the leaf sample surface. Gently pat the washed leaves dry, wrap the petiole with moistened absorbent cotton, and use tweezers to extract mesophyll cells from the leaves. Prepare microscopic slides and place them under an optical microscope in a darkroom. Each group is then examined separately using... , , The light intensity was processed, and microscopic images of its chloroplasts were acquired via a computer connected to the microscope. The acquired microscopic images are as follows: Figure 2 As shown.

[0059] The obtained image was preprocessed using Photoshop software to extract a PNG image of a single cell, and the green channel of the extracted PNG image was then extracted.

[0060] The extracted pixel intensities are used to calculate chloroplast distribution characteristic parameters.

[0061] The calculation methods for different chloroplast distribution characteristic parameters are as follows:

[0062] The formula for calculating skewness is:

[0063] ;

[0064] The skewness values ​​obtained from the experiment were 0.7142, 0.4365, and 0.0977, respectively.

[0065] The formula for calculating kurtosis is:

[0066] ;

[0067] The kurtosis values ​​obtained in the experiment were -0.1579, -0.7352, and -1.2095, respectively.

[0068] Based on the trends of skewness and kurtosis and the actual situation, it is inferred that the skewness range is 0-1 and the kurtosis range is -1.5-0.

[0069] An index model was constructed based on the distribution patterns of chloroplasts in images and the data patterns of chloroplast distribution characteristic parameters. In order to make the index value conform to the actual situation and reasonably control it between 0 and 1, the parameters of the model were adjusted, and finally a chloroplast distribution characteristic index model with a = 3 and b = 0.9 was obtained.

[0070] ;

[0071] In the formula, the distribution index values ​​obtained from the experiment are 0.93, 0.50, and 0.08, respectively.

[0072] like Figure 3 The image shown is a function graph of the chloroplast distribution characteristic quantification index, where the horizontal axis represents the sum of kurtosis and skewness, and the vertical axis represents the chloroplast distribution characteristic quantification index.

[0073] Example 3

[0074] This embodiment provides an image-based chloroplast distribution feature characterization system, the system comprising:

[0075] A module for acquiring chloroplast microscopic images.

[0076] A module for preprocessing chloroplast microscopic images, wherein the preprocessing specifically involves region segmentation and extraction of the green channel from the chloroplast microscopic images.

[0077] A module for calculating the distribution characteristic parameters of chloroplast microscopic images, wherein the chloroplast microscopic image distribution characteristic parameters include skewness and kurtosis, and the skewness is determined by:

[0078]

[0079] Obtain, among which Where is the skewness, and N is the total number of pixels. For the first Intensity of each pixel The kurtosis is the average of all pixel intensities; the kurtosis is determined by:

[0080]

[0081] Obtain, among which For peak value.

[0082] A module for constructing a quantification index of chloroplast microscopic image distribution features to characterize chloroplast distribution features, wherein the quantification index of chloroplast microscopic image distribution features is obtained through:

[0083]

[0084] Obtain, among which A quantification index representing the distribution characteristics of chloroplasts in a microscopic image. The coefficient representing the sum of skewness and kurtosis. Represents a constant term;

[0085] and The specific steps to obtain it are as follows:

[0086] A1, Select , , The chloroplast distribution characteristics under light intensity were used as data;

[0087] A2. Initialization middle and The value is 1, , , Substitute the chloroplast distribution characteristic parameters under light intensity into the initialization. middle;

[0088] A3. Adjustment and The value of is determined so that the exponent value under a given light intensity reaches a value that conforms to the actual situation, and the parameter is finally obtained. and The value.

Claims

1. A method for characterizing chloroplast distribution features based on images, characterized in that, The method includes the following steps: S1. Obtain chloroplast microscopic images; S2. Preprocess the chloroplast microscopic images; S3. Calculate the distribution characteristic parameters of chloroplast micrographs, including skewness. and kurtosis ; S4. Construct a quantification index of chloroplast microscopic image distribution characteristics to characterize chloroplast distribution features. The quantification index of chloroplast microscopic image distribution characteristics is obtained through: Obtain, among which A quantification index representing the distribution characteristics of chloroplasts in a microscopic image. The coefficient representing the sum of skewness and kurtosis. This represents a constant term.

2. The image-based chloroplast distribution feature characterization method according to claim 1, characterized in that, The preprocessing specifically involves region segmentation and extraction of the green channel from the chloroplast microscopic image.

3. The image-based chloroplast distribution feature characterization method according to claim 2, characterized in that, The skewness is achieved through: Obtain, among which Where is the skewness, and N is the total number of pixels. For the first Intensity of each pixel This is the average intensity of all pixels.

4. The image-based chloroplast distribution feature characterization method according to claim 3, characterized in that, The kurtosis is obtained through: Obtain, among which For peak value.

5. The image-based chloroplast distribution feature characterization method according to claim 4, characterized in that, and The specific steps to obtain it are as follows: A1, Select , , The chloroplast distribution characteristics under light intensity were used as data; A2. Initialization middle and The value is 1, , , Substitute the chloroplast distribution characteristic parameters under light intensity into the initialization. middle; A3. Adjustment and The value of is determined so that the exponent value under a given light intensity reaches a value that conforms to the actual situation, and the parameter is finally obtained. and The value.

6. An image-based chloroplast distribution feature characterization system, characterized in that, The system includes: A module for acquiring chloroplast microscopic images; A module for preprocessing chloroplast microscopic images; A module for calculating the distribution characteristic parameters of chloroplast micrographs, including skewness. and kurtosis ; A module for constructing a quantification index of chloroplast microscopic image distribution features to characterize chloroplast distribution features, wherein the quantification index of chloroplast microscopic image distribution features is obtained through: Obtain, among which A quantification index representing the distribution characteristics of chloroplasts in a microscopic image. The coefficient representing the sum of skewness and kurtosis. This represents a constant term.

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