CN110427880A - A kind of nematode fatty acid quantitative approach and system based on image procossing - Google Patents
A kind of nematode fatty acid quantitative approach and system based on image procossing Download PDFInfo
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Abstract
The invention discloses a kind of nematode fatty acid quantitative approach and system based on image procossing, belong to technical field of image processing, the technical problem to be solved in the present invention is how conveniently to measure the intracorporal content of fatty acid of nematode, the technical solution of use are as follows: 1. this method is to carry out dyeing processing to nematode with oil red, the intracorporal fat of nematode is dyed to red, color image is shot to the nematode after dyeing simultaneously, the distribution of the intracorporal content of fatty acid of nematode and nematode body fat acid is quantified by processing color image;Specifically include: the red color intensity at each position indicates oil red density, the intracorporal content of fatty acid of oil red density response line worm in color image middle line polypide;The distribution situation of the red distribution response line worm body fat acid at each position in color image middle line polypide.2. the system includes the quantitative distribution subsystem for calculating the intracorporal content of fatty acid subsystem of nematode and quantitatively calculate nematode body fat acid.
Description
Technical field
The present invention relates to technical field of image processing, specifically a kind of nematode fatty acid based on image procossing is quantitative
Method and system.
Background technique
Caenorhabditis elegans abbreviation nematode is using very more model animals, it is small in size, about 1 millimeter of height, is easy to
Culture.Average life span is about 18-20 days to common Caenorhabditis elegans in the lab, after each nematode can produce about 300
Generation.Nematode has been applied in the multiple fields such as development, aging, metabolism as model organism, becomes in life science very
Important model organism.
The content of fatty acid of nematode can reflect the metaboilic level of nematode, and the service life phase with nematode to a certain extent
It closes.Therefore measuring the different intracorporal content of fatty acid of nematode has important scientific meaning.The survey of traditional nematode fatty acid content
The method that amount method is mainly based upon gas-chromatography measures the content of nematode fatty acid.Traditional measurement method is very multiple
It is miscellaneous, be not easy to operate, therefore be highly desirable to the other easy-to-use methods of exploitation to measure the intracorporal content of fatty acid of nematode.
The patent document of Patent No. CN109387585A discloses a kind of gas-chromatography, mass spectrometric hyphenated technique detection nematode
The method of middle content of fatty acid, method includes the following steps: (1) prepare standard items, with gas-chromatography, mass spectrometry combination method into
Row detection, and is compared with mass spectrometry database, obtains the corresponding fatty acid component of different retention times, when similarity >=95%
It is confirmed as this kind of compound;(2) it prepares standard mixed liquor and establishes gas-chromatography, mass spectrometry qualitative, quantitative side with internal standard method
Method;(3) nematode sample is pre-processed, analysis sample is obtained;(4) gas-chromatography, mass spectrometry qualitative, quantitative are established using step (2)
Method carries out analysis detection to analysis sample to be measured;(5) content for determining each fatty acid in nematode sample is calculated.The technical solution
It is to be analyzed and measured using gas-chromatography and mass spectrum, is not easy to operate, cannot conveniently measures the intracorporal rouge of nematode
Fat acid content.
Summary of the invention
Technical assignment of the invention is to provide a kind of nematode fatty acid quantitative approach and system based on image procossing, to solve
Certainly how conveniently measurement nematode intracorporal content of fatty acid the problem of.
Technical assignment of the invention realizes in the following manner, a kind of nematode fatty acid quantitative square based on image procossing
Method, this method are to carry out dyeing processing to nematode with oil red, and the intracorporal fat of nematode is dyed to red, while to the line after dyeing
Worm shoots color image, and the intracorporal content of fatty acid of nematode and nematode body fat acid are quantified by processing color image
Distribution;It specifically includes:
The red color intensity at each position indicates oil red density in color image middle line polypide, in oil red density response line polypide
Content of fatty acid;
The distribution situation of the red distribution response line worm body fat acid at each position in color image middle line polypide.
Preferably, the method for the quantitative intracorporal content of fatty acid of nematode is specific as follows:
Identification nematode body region and the body area for calculating nematode: nematode body region is identified from color image simultaneously
Calculate the area of nematode body;
Pretreatment color image: fall the background in addition to nematode body region using the nematode body area filter identified
Region only retains nematode body region;
Identification red pixel: the red picture in nematode body region is found by filter condition in nematode body region
Vegetarian refreshments;
Screening peony region: wine-colored region is filtered out on the basis of red pixel;
Calculate the average value of nematode body fat acid content: the phenotype in quantitative peony region, and quantitative phenotype is divided
Not divided by the body area of every nematode, to obtain the average value of nematode body fat acid content, nematode body fat is utilized
Body fat acid content between the more different size of nematode of the average value of acid content.
More preferably, it is described identification red pixel filter condition be in RGB color picture, it is desirable that red component it is strong
Degree is greater than the intensity of green component and blue component.
More preferably, the method in the screening peony region is that the intensity value of red component is greater than the threshold values 60 of setting.
More preferably, the quantitative phenotype refers to the area of red area and the intensity of red area.
Preferably, the distribution situation of the nematode body fat acid includes the length direction along nematode body from head
The fatty acid profile of fatty acid profile to tail portion and the width direction along nematode body from body outside to body centre.
More preferably, the calculation method of the fatty acid profile along the length direction of nematode body from head to tail portion is such as
Under:
Automatic identification head and tail portion: according to the difference on nematode head and tail portion, head and the tail of nematode are automatically identified
Portion;
Piecemeal divides nematode body: the body of nematode is divided into several pieces (such as ten pieces) according to length;
It calculates each piece of content of fatty acid: calculating average red color intensity in each piece and contain as the fatty acid of one's respective area
Amount, to calculate fatty acid profile situation of the nematode from head to tail portion.
More preferably, the meter of fatty acid profile of the width direction along nematode body from body outside to body centre
Calculation method is as follows:
Calculate frontier distance: calculate nematode body in each pixel to its body's border distance;
Layering segmentation nematode body: pixel all in nematode body is divided according to its range averaging to body's border
At several layers (such as ten layers);
It calculates each layer of content of fatty acid: calculating average red color intensity in each layer and contain as the fatty acid of one's respective area
Amount, to calculate the fatty acid profile situation of nematode from outside to inside.
A kind of nematode fatty acid quantitative system based on image procossing, the system include quantitatively calculating the intracorporal fat of nematode
Acid content subsystem and the distribution subsystem for quantitatively calculating nematode body fat acid;
Quantitatively calculating the intracorporal content of fatty acid subsystem of nematode includes,
Nematode body region recognition and the body area computing module of nematode, for identifying nematode body from color image
Body region and the area for calculating nematode body;
Color image preprocessing module, for using the nematode body area filter that identifies fall except nematode body region it
Outer background area only retains nematode body region;
Red pixel identification module, for being found in nematode body region in nematode body region by filter condition
Red pixel;
Peony region screening module, for filtering out wine-colored region on the basis of red pixel;
The mean value calculation module of nematode body fat acid content for the phenotype in quantitative peony region, and will quantify
Phenotype respectively divided by the body area of every nematode, to obtain the average value of nematode body fat acid content, utilize nematode
Body fat acid content between the more different size of nematode of the average value of body fat acid content.
Preferably, the distribution subsystem for quantitatively calculating nematode body fat acid includes the length along nematode body
Directional spreding computing module and width direction distribution calculation module along nematode body;
Include along the length direction distribution calculation module of nematode body,
Head and tail portion automatic identification submodule automatically identify nematode for the difference according to nematode head and tail portion
Head and tail portion;
Nematode body piecemeal divides submodule, for the body of nematode to be divided into several pieces (such as ten according to length
Block);
Each piece of content of fatty acid computational submodule, for calculating average red color intensity in each piece as one's respective area
Content of fatty acid, to calculate fatty acid profile situation of the nematode from head to tail portion;
Include along the width direction distribution calculation module of nematode body,
Frontier distance computational submodule, the distance for calculating each pixel in nematode body to its body's border;
Nematode body layering segmentation submodule, for pixel all in nematode body to be arrived body's border according to it
Range averaging is divided into several layers (such as ten layers);
Each layer of content of fatty acid computational submodule, for calculating average red color intensity in each layer as one's respective area
Content of fatty acid, to calculate the fatty acid profile situation of nematode from outside to inside.
Of the invention nematode fatty acid quantitative approach and system based on image procossing has the advantage that
(1), the present invention measures the intracorporal content of fatty acid of nematode by the method that designed image is handled, and solves
Traditional nematode fatty acid content measuring method is extremely complex, is not easy to the difficulty of operation, is quantified using image processing program
Red color intensity simultaneously reflects that the method for content of fatty acid is simpler compared with traditional quantitative approach, conveniently;
(2), the present invention quantify to the intracorporal content of fatty acid of model organism nematode and distribution, from oil red dyeing
Quantitative content of fatty acid and distribution in nematode color image, thus to determine the metaboilic level of quantifier elimination nematode and disclosing metabolism
Influence of the level to aging rate provides the index of quantization.
Detailed description of the invention
The following further describes the present invention with reference to the drawings.
Attached drawing 1 is the flow diagram of the method for the quantitative intracorporal content of fatty acid of nematode;
Attached drawing 2 is the process of the calculation method of the fatty acid profile along the length direction of nematode body from head to tail portion
Block diagram;
Attached drawing 3 is the calculating side of the fatty acid profile along the width direction of nematode body from body outside to body centre
The flow diagram of method;
Attached drawing 4 is schematic diagram of the nematode after oil red dyes;
Attached drawing 5 is the schematic diagram that nematode is identified from picture;
Attached drawing 6 is the schematic diagram for filtering out the intracorporal red area of nematode;
Attached drawing 7 is the schematic diagram for identifying the intracorporal lipochondrion of nematode.
Specific embodiment
It is quantitative to a kind of nematode fatty acid based on image procossing of the invention referring to Figure of description and specific embodiment
Method and system are described in detail below.
Embodiment 1
Nematode fatty acid quantitative approach based on image procossing of the invention, this method are to be dyed with oil red to nematode
Processing, as shown in Fig. 4, the intracorporal fat of nematode are dyed to red, while shooting color image to the nematode after dyeing, pass through
Color image is handled to quantify the distribution of the intracorporal content of fatty acid of nematode and nematode body fat acid;It specifically includes:
The red color intensity at each position indicates oil red density in color image middle line polypide, in oil red density response line polypide
Content of fatty acid;
The distribution situation of the red distribution response line worm body fat acid at each position in color image middle line polypide.
As shown in Fig. 1, the method for the quantitative intracorporal content of fatty acid of nematode is specific as follows:
S1, as shown in Fig. 5, identifies nematode body region and calculates the body area of nematode: identifying from color image
Nematode body region and calculate the area of nematode body out;
S2, pretreatment color image: fallen in addition to nematode body region using the nematode body area filter identified
Background area only retains nematode body region;
S3, as shown in Fig. 6, identify red pixel: nematode body is found by filter condition in nematode body region
Red pixel in body region;
S4, as shown in Fig. 7, screen peony region: wine-colored region is filtered out on the basis of red pixel;
S5, the average value for calculating nematode body fat acid content: the phenotype in quantitative peony region, and by quantitative phenotype
Respectively divided by the body area of every nematode, to obtain the average value of nematode body fat acid content, rouge in nematode body is utilized
Body fat acid content between the more different size of nematode of the average value of fat acid content.
Wherein, the filter condition for red pixel being identified in step S3 is in RGB color picture, it is desirable that red component
Intensity is greater than the intensity of green component and blue component.
The method that peony region is screened in step S4 is that the intensity value of red component is greater than the threshold values 60 of setting.
Quantitative phenotype refers to the area of red area and the intensity of red area in step S5.
The distribution situation of nematode body fat acid includes the fat along the length direction of nematode body from head to tail portion
Acid distribution and along nematode body width direction on the outside of the body to the fatty acid profile of body centre.
As shown in Fig. 2, the calculation method of the fatty acid profile along the length direction of nematode body from head to tail portion
It is as follows:
M1, automatic identification head and tail portion: according to the difference on nematode head and tail portion, automatically identify nematode head and
Tail portion;
M2, piecemeal divide nematode body: the body of nematode is divided into several pieces (such as ten pieces) according to length;Piecemeal
Number can be set according to the length of nematode body;
M3, the content of fatty acid for calculating each piece: average fat of the red color intensity as one's respective area is calculated in each piece
Acid content, to calculate fatty acid profile situation of the nematode from head to tail portion.
As shown in Fig. 3, along the width direction of nematode body on the outside of the body to the fatty acid profile of body centre
Calculation method is as follows:
R1, calculate frontier distance: calculate nematode body in each pixel to its body's border distance;
R2, layering segmentation nematode body: pixel all in nematode body is put down according to its distance to body's border
It is divided into several layers (such as ten layers);The number of plies of layering can be set according to the width of nematode body;
R3, the content of fatty acid for calculating each layer: average fat of the red color intensity as one's respective area is calculated in each layer
Acid content, to calculate the fatty acid profile situation of nematode from outside to inside.
Embodiment 2:
Nematode fatty acid quantitative system based on image procossing of the invention, the system are intracorporal including quantitatively calculating nematode
Content of fatty acid subsystem and the distribution subsystem for quantitatively calculating nematode body fat acid;
Quantitatively calculating the intracorporal content of fatty acid subsystem of nematode includes,
Nematode body region recognition and the body area computing module of nematode, for identifying nematode body from color image
Body region and the area for calculating nematode body;
Color image preprocessing module, for using the nematode body area filter that identifies fall except nematode body region it
Outer background area only retains nematode body region;
Red pixel identification module, for being found in nematode body region in nematode body region by filter condition
Red pixel;
Peony region screening module, for filtering out wine-colored region on the basis of red pixel;
The mean value calculation module of nematode body fat acid content for the phenotype in quantitative peony region, and will quantify
Phenotype respectively divided by the body area of every nematode, to obtain the average value of nematode body fat acid content, utilize nematode
Body fat acid content between the more different size of nematode of the average value of body fat acid content.
Preferably, the distribution subsystem for quantitatively calculating nematode body fat acid includes the length along nematode body
Directional spreding computing module and width direction distribution calculation module along nematode body;
Include along the length direction distribution calculation module of nematode body,
Head and tail portion automatic identification submodule automatically identify nematode for the difference according to nematode head and tail portion
Head and tail portion;
Nematode body piecemeal divides submodule, for the body of nematode to be divided into several pieces (such as ten according to length
Block);
Each piece of content of fatty acid computational submodule, for calculating average red color intensity in each piece as one's respective area
Content of fatty acid, to calculate fatty acid profile situation of the nematode from head to tail portion;
Include along the width direction distribution calculation module of nematode body,
Frontier distance computational submodule, the distance for calculating each pixel in nematode body to its body's border;
Nematode body layering segmentation submodule, for pixel all in nematode body to be arrived body's border according to it
Range averaging is divided into several layers (such as ten layers);
Each layer of content of fatty acid computational submodule, for calculating average red color intensity in each layer as one's respective area
Content of fatty acid, to calculate the fatty acid profile situation of nematode from outside to inside.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations;To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement;And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (10)
1. a kind of nematode fatty acid quantitative approach based on image procossing, which is characterized in that this method be with oil red to nematode into
Row dyeing processing, the intracorporal fat of nematode is dyed to red, while shooting color image to the nematode after dyeing, color by processing
Chromatic graph picture quantifies the distribution of the intracorporal content of fatty acid of nematode and nematode body fat acid;It specifically includes:
The red color intensity at each position indicates oil red density, the intracorporal rouge of oil red density response line worm in color image middle line polypide
Fat acid content;
The distribution situation of the red distribution response line worm body fat acid at each position in color image middle line polypide.
2. the nematode fatty acid quantitative approach according to claim 1 based on image procossing, which is characterized in that described quantitative
The method of the intracorporal content of fatty acid of nematode is specific as follows:
Identification nematode body region and the body area for calculating nematode: nematode body region is identified from color image and is calculated
The area of nematode body;
Pretreatment color image: fall the background area in addition to nematode body region using the nematode body area filter identified
Domain only retains nematode body region;
Identification red pixel: the red pixel in nematode body region is found by filter condition in nematode body region
Point;
Screening peony region: wine-colored region is filtered out on the basis of red pixel;
Calculate the average value of nematode body fat acid content: the phenotype in quantitative peony region, and quantitative phenotype is removed respectively
With the body area of every nematode, to obtain the average value of nematode body fat acid content, contained using nematode body fat acid
Body fat acid content between the more different size of nematode of the average value of amount.
3. the nematode fatty acid quantitative approach according to claim 2 based on image procossing, which is characterized in that the identification
The filter condition of red pixel is in RGB color picture, it is desirable that the intensity of red component is greater than green component and blue
The intensity of component.
4. the nematode fatty acid quantitative approach according to claim 2 or 3 based on image procossing, which is characterized in that described
The method in screening peony region is that the intensity value of red component is greater than the threshold values 60 of setting.
5. the nematode fatty acid quantitative approach according to claim 4 based on image procossing, which is characterized in that described quantitative
Phenotype refer to the area of red area and the intensity of red area.
6. the nematode fatty acid quantitative approach according to claim 1 based on image procossing, which is characterized in that the nematode
The distribution situation of body fat acid includes the fatty acid profile and edge along the length direction of nematode body from head to tail portion
Nematode body width direction on the outside of the body to the fatty acid profile of body centre.
7. the nematode fatty acid quantitative approach according to claim 6 based on image procossing, which is characterized in that it is described along
The calculation method of fatty acid profile of the length direction of nematode body from head to tail portion is as follows:
Automatic identification head and tail portion: according to the difference on nematode head and tail portion, the head and tail portion of nematode are automatically identified;
Piecemeal divides nematode body: the body of nematode is divided into several pieces according to length;
It calculates each piece of content of fatty acid: calculating average content of fatty acid of the red color intensity as one's respective area in each piece,
To calculate fatty acid profile situation of the nematode from head to tail portion.
8. the nematode fatty acid quantitative approach according to claim 6 based on image procossing, which is characterized in that it is described along
The calculation method of the width direction of nematode body from the fatty acid profile on the outside of body to body centre is as follows:
Calculate frontier distance: calculate nematode body in each pixel to its body's border distance;
Layering segmentation nematode body: if pixel all in nematode body is divided into according to its range averaging to body's border
Dried layer;
It calculates each layer of content of fatty acid: calculating average content of fatty acid of the red color intensity as one's respective area in each layer,
To calculate the fatty acid profile situation of nematode from outside to inside.
9. a kind of nematode fatty acid quantitative system based on image procossing, which is characterized in that the system includes quantitatively calculating nematode
Intracorporal content of fatty acid subsystem and the distribution subsystem for quantitatively calculating nematode body fat acid;
Quantitatively calculating the intracorporal content of fatty acid subsystem of nematode includes,
Nematode body region recognition and the body area computing module of nematode, for identifying nematode body area from color image
Domain and the area for calculating nematode body;
Color image preprocessing module, for being fallen in addition to nematode body region using the nematode body area filter identified
Background area only retains nematode body region;
Red pixel identification module, it is red in nematode body region for being found in nematode body region by filter condition
Colour vegetarian refreshments;
Peony region screening module, for filtering out wine-colored region on the basis of red pixel;
The mean value calculation module of nematode body fat acid content, for the phenotype in quantitative peony region, and by quantitative table
Type is respectively divided by the body area of every nematode, so that the average value of nematode body fat acid content is obtained, using in nematode body
Body fat acid content between the more different size of nematode of the average value of content of fatty acid.
10. the nematode fatty acid quantitative system according to claim 9 based on image procossing, which is characterized in that described fixed
The distribution subsystem that amount calculates nematode body fat acid includes along the length direction distribution calculation module of nematode body and edge
The width direction distribution calculation module of nematode body;
Include along the length direction distribution calculation module of nematode body,
Head and tail portion automatic identification submodule automatically identify the head of nematode for the difference according to nematode head and tail portion
Portion and tail portion;
Nematode body piecemeal divides submodule, for the body of nematode to be divided into several pieces according to length;
Each piece of content of fatty acid computational submodule, for calculating average rouge of the red color intensity as one's respective area in each piece
Fat acid content, to calculate fatty acid profile situation of the nematode from head to tail portion;
Include along the width direction distribution calculation module of nematode body,
Frontier distance computational submodule, the distance for calculating each pixel in nematode body to its body's border;
Nematode body layering segmentation submodule, for pixel all in nematode body to be arrived to the distance of body's border according to it
It is divided into several layers;
Each layer of content of fatty acid computational submodule, for calculating average rouge of the red color intensity as one's respective area in each layer
Fat acid content, to calculate the fatty acid profile situation of nematode from outside to inside.
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Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101881726A (en) * | 2010-06-18 | 2010-11-10 | 北京农业智能装备技术研究中心 | Nondestructive detection method for comprehensive character living bodies of plant seedlings |
CN102213672A (en) * | 2010-04-03 | 2011-10-12 | 李红玉 | Method and kit for setting survival rate of nematode quickly |
CN104928208A (en) * | 2015-04-30 | 2015-09-23 | 江苏紫石微康生物科技有限公司 | Lactobacillus plantarum Lp90, and screening method and application thereof |
US20160216209A1 (en) * | 2013-09-26 | 2016-07-28 | Kinica Minolta, Inc. | Method for determining quantity of biological material in tissue section |
CN107561264A (en) * | 2017-09-05 | 2018-01-09 | 齐鲁工业大学 | The identification of beta-amyloyd patch and measuring method based on image procossing |
CN107808396A (en) * | 2017-11-01 | 2018-03-16 | 齐鲁工业大学 | It is easy to nematode recognition methods and the system of image segmentation |
CN109387585A (en) * | 2018-10-23 | 2019-02-26 | 江汉大学 | The method of content of fatty acid in gas-chromatography, mass spectrometric hyphenated technique detection nematode |
CN109872301A (en) * | 2018-12-26 | 2019-06-11 | 浙江清华长三角研究院 | A kind of color image preprocess method counted for rice pest identification |
CN110148125A (en) * | 2019-05-21 | 2019-08-20 | 苏州大学 | Adaptive skin oil and fat detection method based on color detection |
-
2019
- 2019-08-01 CN CN201910706312.7A patent/CN110427880B/en active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102213672A (en) * | 2010-04-03 | 2011-10-12 | 李红玉 | Method and kit for setting survival rate of nematode quickly |
CN101881726A (en) * | 2010-06-18 | 2010-11-10 | 北京农业智能装备技术研究中心 | Nondestructive detection method for comprehensive character living bodies of plant seedlings |
US20160216209A1 (en) * | 2013-09-26 | 2016-07-28 | Kinica Minolta, Inc. | Method for determining quantity of biological material in tissue section |
CN104928208A (en) * | 2015-04-30 | 2015-09-23 | 江苏紫石微康生物科技有限公司 | Lactobacillus plantarum Lp90, and screening method and application thereof |
CN107561264A (en) * | 2017-09-05 | 2018-01-09 | 齐鲁工业大学 | The identification of beta-amyloyd patch and measuring method based on image procossing |
CN107808396A (en) * | 2017-11-01 | 2018-03-16 | 齐鲁工业大学 | It is easy to nematode recognition methods and the system of image segmentation |
CN109387585A (en) * | 2018-10-23 | 2019-02-26 | 江汉大学 | The method of content of fatty acid in gas-chromatography, mass spectrometric hyphenated technique detection nematode |
CN109872301A (en) * | 2018-12-26 | 2019-06-11 | 浙江清华长三角研究院 | A kind of color image preprocess method counted for rice pest identification |
CN110148125A (en) * | 2019-05-21 | 2019-08-20 | 苏州大学 | Adaptive skin oil and fat detection method based on color detection |
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