CN110427880B - Nematode fatty acid quantification method and system based on image processing - Google Patents
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- 241000244206 Nematoda Species 0.000 title claims abstract description 249
- 235000014113 dietary fatty acids Nutrition 0.000 title claims abstract description 145
- 229930195729 fatty acid Natural products 0.000 title claims abstract description 145
- 239000000194 fatty acid Substances 0.000 title claims abstract description 145
- 150000004665 fatty acids Chemical class 0.000 title claims abstract description 145
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012545 processing Methods 0.000 title claims abstract description 22
- 238000011002 quantification Methods 0.000 title claims abstract description 11
- 238000004043 dyeing Methods 0.000 claims abstract description 5
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- 230000011218 segmentation Effects 0.000 claims description 5
- 238000004817 gas chromatography Methods 0.000 description 6
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- 241000244203 Caenorhabditis elegans Species 0.000 description 2
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- MCSXGCZMEPXKIW-UHFFFAOYSA-N 3-hydroxy-4-[(4-methyl-2-nitrophenyl)diazenyl]-N-(3-nitrophenyl)naphthalene-2-carboxamide Chemical group Cc1ccc(N=Nc2c(O)c(cc3ccccc23)C(=O)Nc2cccc(c2)[N+]([O-])=O)c(c1)[N+]([O-])=O MCSXGCZMEPXKIW-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a nematode fatty acid quantification method and system based on image processing, which belong to the technical field of image processing, and the invention aims to solve the technical problem of how to conveniently and rapidly measure the fatty acid content in a nematode body, and adopts the following technical scheme: (1) the method comprises the steps of dyeing the nematode with oil red, dyeing fat in the nematode body to be red, shooting a color picture on the dyed nematode body, and quantifying the content of fatty acid in the nematode body and the distribution of fatty acid in the nematode body by processing the color picture; the method specifically comprises the following steps: red intensity of each part in the nematode body in the color picture represents oil red density, and the oil red density reflects fatty acid content in the nematode body; the red distribution of each part in the nematode body in the color picture reflects the distribution condition of fatty acid in the nematode body. (2) The system comprises a subsystem for quantitatively calculating the fatty acid content in the nematode body and a distribution subsystem for quantitatively calculating the fatty acid in the nematode body.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a nematode fatty acid quantification method and system based on image processing.
Background
The caenorhabditis elegans is a nematode for short, is a model animal with very many applications, has a small volume and a length of about 1 mm, and is easy to culture. Common caenorhabditis elegans have an average life span in the laboratory of about 18 to 20 days, with about 300 offspring per nematode. Nematodes have been used as model organisms in a variety of fields including development, aging, metabolism, etc., and are very important model organisms in life science research.
The fatty acid content of nematodes reflects to some extent the metabolic level of the nematode and is related to the life of the nematode. Therefore, the measurement of fatty acid content in different wireworms has important scientific significance. The traditional method for measuring the content of the fatty acid of the nematode is mainly based on a gas chromatography method for measuring the content of the fatty acid of the nematode. Conventional measurement methods are very complex and not easy to operate, and therefore development of other convenient and easy-to-use methods for measuring fatty acid content in nematodes is highly desirable.
Patent document CN109387585a discloses a method for detecting fatty acid content in nematodes by gas chromatography and mass spectrometry, which comprises the following steps: (1) Preparing a standard substance, detecting by using a gas chromatography and mass spectrometry method, and comparing with a mass spectrometry database to obtain fatty acid components corresponding to different retention times, wherein the fatty acid components are confirmed to be the compound when the similarity is more than or equal to 95%; (2) Preparing a standard mixed solution, and establishing a gas chromatography and mass spectrometry combined qualitative and quantitative method by using an internal standard method; (3) pre-treating the nematode sample to obtain an analytical sample; (4) Adopting the step (2) to establish a gas chromatography and mass spectrometry combined qualitative and quantitative method to analyze and detect the sample to be analyzed; (5) calculating and determining the content of each fatty acid in the nematode sample. According to the technical scheme, gas chromatography and mass spectrometry are used for analysis and measurement, the operation is not easy, and the fatty acid content in the nematode body cannot be measured conveniently and rapidly.
Disclosure of Invention
The invention aims to provide a nematode fatty acid quantification method and system based on image processing, which are used for solving the problem of how to conveniently and rapidly measure the fatty acid content in a nematode body.
The invention aims to realize the following technical task, namely an image processing-based nematode fatty acid quantification method, which is characterized in that oil red is used for dyeing nematodes, fat in the nematodes is dyed red, meanwhile, color pictures are taken for the dyed nematodes, and the fatty acid content in the nematodes and the distribution of fatty acids in the nematodes are quantified by processing the color pictures; the method specifically comprises the following steps:
red intensity of each part in the nematode body in the color picture represents oil red density, and the oil red density reflects fatty acid content in the nematode body;
the red distribution of each part in the nematode body in the color picture reflects the distribution condition of fatty acid in the nematode body.
Preferably, the method for quantifying fatty acid content in nematodes is specifically as follows:
identifying a nematode body area and calculating a nematode body area: identifying a nematode body area from the color picture and calculating the area of the nematode body;
preprocessing a color picture: filtering out background areas except the nematode body area by using the identified nematode body area, and only retaining the nematode body area;
identifying red pixels: finding red pixel points in the nematode body area through filtering conditions;
screening dark red areas: screening out a dark red region on the basis of the red pixel points;
calculating the average value of fatty acid content in the nematode body: and quantifying the phenotype of the dark red region, dividing the quantified phenotype by the body area of each nematode, thereby obtaining the average value of the fatty acid content in the nematode, and comparing the fatty acid content in the nematodes with the average value of the fatty acid content in the nematodes.
Preferably, the filtering condition for identifying the red pixel point is that in the RGB color picture, the intensity of the red component is required to be greater than the intensities of the green component and the blue component.
More preferably, the method of screening the dark red region is that the intensity value of the red component is greater than a set threshold 60.
More preferably, the quantitative phenotype refers to the area of the red region and the intensity of the red region.
Preferably, the distribution of fatty acids in the nematode body includes a fatty acid distribution from the head to the tail along the length of the nematode body and a fatty acid distribution from the outside of the body to the center of the body along the width of the nematode body.
More preferably, the fatty acid distribution from head to tail along the length of the nematode body is calculated as follows:
automatically identifying the head and tail: according to the difference between the head and the tail of the nematode, the head and the tail of the nematode are automatically identified;
segmentation of nematode bodies: dividing the body of the nematode into a plurality of blocks (such as ten blocks) according to the length average;
the fatty acid content of each block was calculated: the average red intensity was calculated as the fatty acid content of the present region in each block, and thus the fatty acid distribution of the wire worms from head to tail was calculated.
More preferably, the fatty acid distribution from the outside to the center of the body along the width direction of the nematode body is calculated as follows:
calculating boundary distance: calculating the distance from each pixel point in the nematode body to the body boundary;
layering segments the nematode body: dividing all pixel points in the nematode body into a plurality of layers (such as ten layers) averagely according to the distance from the pixel points to the body boundary;
the fatty acid content of each layer was calculated: the average red intensity was calculated in each layer as the fatty acid content of the present region, and thus the fatty acid distribution of nematodes from outside to inside was calculated.
An image processing-based nematode fatty acid quantifying system comprises a fatty acid content quantifying subsystem and a fatty acid distribution subsystem, wherein the fatty acid content quantifying subsystem and the fatty acid distribution subsystem are used for quantifying fatty acids in a nematode body;
the subsystem for quantitatively calculating the fatty acid content in the nematode body comprises,
the nematode body area identification and nematode body area calculation module is used for identifying nematode body areas from the color pictures and calculating nematode body areas;
the color picture preprocessing module is used for filtering out background areas except the nematode body areas by utilizing the identified nematode body areas, and only retaining the nematode body areas;
the red pixel point identification module is used for finding red pixel points in the nematode body area through filtering conditions in the nematode body area;
the dark red region screening module is used for screening dark red regions on the basis of red pixel points;
and the average value calculation module is used for quantifying the phenotype of the dark red area, dividing the quantified phenotype by the body area of each nematode, so as to obtain the average value of the fatty acid content in the nematode, and comparing the fatty acid content in the nematodes with different sizes by using the average value of the fatty acid content in the nematode.
Preferably, the distribution subsystem for quantitatively calculating fatty acid in the nematode body comprises a distribution calculation module along the length direction of the nematode body and a distribution calculation module along the width direction of the nematode body;
the distribution calculation module along the length of the nematode body comprises,
the head and tail automatic identification submodule is used for automatically identifying the head and tail of the nematode according to the difference of the head and tail of the nematode;
the nematode body is divided into a plurality of blocks (such as ten blocks) according to the length;
the fatty acid content calculation operator module of each block is used for calculating the average red intensity in each block as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the line worms from the head to the tail;
the distribution calculation module along the width direction of the nematode body comprises,
the boundary distance calculating sub-module is used for calculating the distance from each pixel point in the nematode body to the boundary of the nematode body;
the nematode body layering segmentation submodule is used for equally dividing all pixel points in the nematode body into a plurality of layers (such as ten layers) according to the distance from the pixel points to the body boundary;
and the fatty acid content calculation operator module of each layer is used for calculating the average red intensity in each layer as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the nematodes from outside to inside.
The nematode fatty acid quantification method and system based on image processing have the following advantages:
(1) The invention measures the fatty acid content in the nematode body by designing the image processing method, solves the problems that the traditional nematode fatty acid content measuring method is very complex and is not easy to operate, and the method for quantifying the red intensity and reflecting the fatty acid content by using the image processing program is simpler and more convenient than the traditional quantifying method;
(2) The invention quantifies the fatty acid content and distribution in the model organism nematode, and quantifies the fatty acid content and distribution from the oil red dyed nematode color image, thereby providing quantified indexes for quantitatively researching the metabolic level of the nematode and revealing the influence of the metabolic level on the aging speed.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for quantifying fatty acid content in a nematode body;
FIG. 2 is a block flow diagram of a method of calculating the fatty acid profile from head to tail along the length of a nematode body;
FIG. 3 is a block flow diagram of a method of calculating fatty acid distribution along the width of a nematode body from outside the body to the body center;
FIG. 4 is a schematic representation of nematodes after oil red staining;
FIG. 5 is a schematic representation of nematodes identified from a picture;
FIG. 6 is a schematic view of the red area within the screening of the line worms;
fig. 7 is a schematic diagram identifying fat particles within a nematode body.
Detailed Description
The nematode fatty acid quantification method and system based on the image processing of the present invention will be described in detail below with reference to the accompanying drawings and specific examples.
Example 1
The invention relates to a nematode fatty acid quantification method based on image processing, which comprises the steps of dyeing nematodes with oil red, wherein fat in the nematodes is dyed red as shown in figure 4, shooting a color picture on the dyed nematodes, and quantifying the fatty acid content in the nematodes and the distribution of fatty acids in the nematodes by processing the color picture; the method specifically comprises the following steps:
red intensity of each part in the nematode body in the color picture represents oil red density, and the oil red density reflects fatty acid content in the nematode body;
the red distribution of each part in the nematode body in the color picture reflects the distribution condition of fatty acid in the nematode body.
As shown in fig. 1, the method for quantifying fatty acid content in nematodes is specifically as follows:
s1, as shown in fig. 5, identifying a nematode body area and calculating the body area of the nematode: identifying a nematode body area from the color picture and calculating the area of the nematode body;
s2, preprocessing a color picture: filtering out background areas except the nematode body area by using the identified nematode body area, and only retaining the nematode body area;
s3, as shown in fig. 6, identifying red pixel points: finding red pixel points in the nematode body area through filtering conditions;
s4, as shown in the attached figure 7, screening dark red areas: screening out a dark red region on the basis of the red pixel points;
s5, calculating the average value of fatty acid content in the nematode body: and quantifying the phenotype of the dark red region, dividing the quantified phenotype by the body area of each nematode, thereby obtaining the average value of the fatty acid content in the nematode, and comparing the fatty acid content in the nematodes with the average value of the fatty acid content in the nematodes.
The filtering condition for identifying the red pixel point in step S3 is that the intensity of the red component is required to be greater than the intensities of the green component and the blue component in the RGB color picture.
The method of screening the deep red region in step S4 is that the intensity value of the red component is greater than the set threshold 60.
The phenotype quantified in step S5 refers to the area of the red region and the intensity of the red region.
The distribution of fatty acids in the nematode body includes a fatty acid distribution from the head to the tail along the length of the nematode body and a fatty acid distribution from the outside of the body to the center of the body along the width of the nematode body.
As shown in fig. 2, the fatty acid distribution along the length of the nematode body from head to tail was calculated as follows:
m1, automatically identifying a head part and a tail part: according to the difference between the head and the tail of the nematode, the head and the tail of the nematode are automatically identified;
m2, dividing the nematode body in blocks: dividing the body of the nematode into a plurality of blocks (such as ten blocks) according to the length average; the number of the blocks can be set according to the length of the nematode body;
m3, calculating the fatty acid content of each block: the average red intensity was calculated as the fatty acid content of the present region in each block, and thus the fatty acid distribution of the wire worms from head to tail was calculated.
As shown in fig. 3, the fatty acid distribution from the outside to the center of the body along the width direction of the nematode body was calculated as follows:
r1, calculating boundary distance: calculating the distance from each pixel point in the nematode body to the body boundary;
r2, dividing nematode bodies in layers: dividing all pixel points in the nematode body into a plurality of layers (such as ten layers) averagely according to the distance from the pixel points to the body boundary; the number of layers of the layering can be set according to the width of the nematode body;
r3, calculating the fatty acid content of each layer: the average red intensity was calculated in each layer as the fatty acid content of the present region, and thus the fatty acid distribution of nematodes from outside to inside was calculated.
Example 2:
the invention relates to a nematode fatty acid quantitative system based on image processing, which comprises a fatty acid content quantitative calculation subsystem in a nematode body and a fatty acid distribution subsystem in the nematode body;
the subsystem for quantitatively calculating the fatty acid content in the nematode body comprises,
the nematode body area identification and nematode body area calculation module is used for identifying nematode body areas from the color pictures and calculating nematode body areas;
the color picture preprocessing module is used for filtering out background areas except the nematode body areas by utilizing the identified nematode body areas, and only retaining the nematode body areas;
the red pixel point identification module is used for finding red pixel points in the nematode body area through filtering conditions in the nematode body area;
the dark red region screening module is used for screening dark red regions on the basis of red pixel points;
and the average value calculation module is used for quantifying the phenotype of the dark red area, dividing the quantified phenotype by the body area of each nematode, so as to obtain the average value of the fatty acid content in the nematode, and comparing the fatty acid content in the nematodes with different sizes by using the average value of the fatty acid content in the nematode.
Preferably, the distribution subsystem for quantitatively calculating fatty acid in the nematode body comprises a distribution calculation module along the length direction of the nematode body and a distribution calculation module along the width direction of the nematode body;
the distribution calculation module along the length of the nematode body comprises,
the head and tail automatic identification submodule is used for automatically identifying the head and tail of the nematode according to the difference of the head and tail of the nematode;
the nematode body is divided into a plurality of blocks (such as ten blocks) according to the length;
the fatty acid content calculation operator module of each block is used for calculating the average red intensity in each block as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the line worms from the head to the tail;
the distribution calculation module along the width direction of the nematode body comprises,
the boundary distance calculating sub-module is used for calculating the distance from each pixel point in the nematode body to the boundary of the nematode body;
the nematode body layering segmentation submodule is used for equally dividing all pixel points in the nematode body into a plurality of layers (such as ten layers) according to the distance from the pixel points to the body boundary;
and the fatty acid content calculation operator module of each layer is used for calculating the average red intensity in each layer as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the nematodes from outside to inside.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (6)
1. The nematode fatty acid quantification method based on image processing is characterized in that oil red is used for dyeing nematodes, fat in the nematodes is dyed red, meanwhile, color pictures are taken for the dyed nematodes, and the fatty acid content in the nematodes and the distribution of fatty acids in the nematodes are quantified by processing the color pictures; the method specifically comprises the following steps:
red intensity of each part in the nematode body in the color picture represents oil red density, and the oil red density reflects fatty acid content in the nematode body;
red distribution of each part in the nematode body in the color picture reflects the distribution condition of fatty acid in the nematode body;
wherein the distribution of fatty acids in the nematode body includes a fatty acid distribution from the head to the tail along the length direction of the nematode body and a fatty acid distribution from the outside of the body to the center of the body along the width direction of the nematode body;
the fatty acid profile along the length of the nematode body from head to tail was calculated as follows:
automatically identifying the head and tail: according to the difference between the head and the tail of the nematode, the head and the tail of the nematode are automatically identified;
segmentation of nematode bodies: dividing the body of the nematode into a plurality of blocks according to the length;
the fatty acid content of each block was calculated: calculating the average red intensity in each block as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the line worms from the head to the tail;
the fatty acid distribution from the outside of the body to the center of the body along the width direction of the nematode body was calculated as follows:
calculating boundary distance: calculating the distance from each pixel point in the nematode body to the body boundary;
layering segments the nematode body: dividing all pixel points in the nematode body into a plurality of layers according to the distance from the pixel points to the body boundary;
the fatty acid content of each layer was calculated: the average red intensity was calculated in each layer as the fatty acid content of the present region, and thus the fatty acid distribution of nematodes from outside to inside was calculated.
2. The method for quantifying fatty acid content of nematodes based on image processing according to claim 1, wherein the method for quantifying fatty acid content in nematodes is specifically as follows:
identifying a nematode body area and calculating a nematode body area: identifying a nematode body area from the color picture and calculating the area of the nematode body;
preprocessing a color picture: filtering out background areas except the nematode body area by using the identified nematode body area, and only retaining the nematode body area;
identifying red pixels: finding red pixel points in the nematode body area through filtering conditions;
screening dark red areas: screening out a dark red region on the basis of the red pixel points;
calculating the average value of fatty acid content in the nematode body: and quantifying the phenotype of the dark red region, dividing the quantified phenotype by the body area of each nematode, thereby obtaining the average value of the fatty acid content in the nematode, and comparing the fatty acid content in the nematodes with the average value of the fatty acid content in the nematodes.
3. The method of claim 2, wherein the filtering condition for identifying red pixels is that the red component is required to have a higher intensity than the green and blue components in the RGB color picture.
4. A method of quantifying nematode fatty acids based on image processing according to claim 2 or 3, wherein the method of screening for dark red areas is such that the intensity value of the red component is greater than a set threshold value of 60.
5. The image processing-based nematode fatty acid quantification method of claim 4, wherein the quantified phenotype refers to the area of the red region and the intensity of the red region.
6. The nematode fatty acid quantitative system based on image processing is characterized by comprising a fatty acid content quantitative calculation subsystem and a fatty acid distribution subsystem in the nematode body;
the subsystem for quantitatively calculating the fatty acid content in the nematode body comprises,
the nematode body area identification and nematode body area calculation module is used for identifying nematode body areas from the color pictures and calculating nematode body areas;
the color picture preprocessing module is used for filtering out background areas except the nematode body areas by utilizing the identified nematode body areas, and only retaining the nematode body areas;
the red pixel point identification module is used for finding red pixel points in the nematode body area through filtering conditions in the nematode body area;
the dark red region screening module is used for screening dark red regions on the basis of red pixel points;
the average value calculation module of the fatty acid content in the nematodes is used for quantifying the phenotype of the dark red area, dividing the quantified phenotype by the body area of each nematode respectively, so as to obtain the average value of the fatty acid content in the nematodes, and comparing the fatty acid content in the nematodes with the average value of the fatty acid content in the nematodes; the distribution subsystem for quantitatively calculating fatty acid in the nematode body comprises a distribution calculation module along the length direction of the nematode body and a distribution calculation module along the width direction of the nematode body;
the distribution calculation module along the length of the nematode body comprises,
the head and tail automatic identification submodule is used for automatically identifying the head and tail of the nematode according to the difference of the head and tail of the nematode;
the nematode body is divided into a plurality of blocks according to the length;
the fatty acid content calculation operator module of each block is used for calculating the average red intensity in each block as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the line worms from the head to the tail;
the distribution calculation module along the width direction of the nematode body comprises,
the boundary distance calculating sub-module is used for calculating the distance from each pixel point in the nematode body to the boundary of the nematode body;
the nematode body layering segmentation submodule is used for equally dividing all pixel points in the nematode body into a plurality of layers according to the distance from the pixel points to the body boundary;
and the fatty acid content calculation operator module of each layer is used for calculating the average red intensity in each layer as the fatty acid content of the area, so as to calculate the fatty acid distribution condition of the nematodes from outside to inside.
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