CN110874821B - Image processing method for automatically filtering non-sperm components in semen - Google Patents
Image processing method for automatically filtering non-sperm components in semen Download PDFInfo
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- 210000000582 semen Anatomy 0.000 title claims abstract description 53
- 238000001914 filtration Methods 0.000 title claims abstract description 31
- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 239000012535 impurity Substances 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000012935 Averaging Methods 0.000 claims abstract description 4
- 230000003321 amplification Effects 0.000 claims description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 210000004027 cell Anatomy 0.000 description 19
- 238000000034 method Methods 0.000 description 8
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- 238000011161 development Methods 0.000 description 4
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- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
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- 238000004220 aggregation Methods 0.000 description 2
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- 238000001514 detection method Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
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- 230000007547 defect Effects 0.000 description 1
- 238000011496 digital image analysis Methods 0.000 description 1
- 210000002919 epithelial cell Anatomy 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000009027 insemination Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000011160 research Methods 0.000 description 1
- 230000035899 viability Effects 0.000 description 1
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- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
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- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
An image processing method for automatically filtering non-sperm components in semen, comprising the following steps: acquiring a semen image; filtering a round cell area in the semen image to generate a filtered image; carrying out averaging treatment on the filtered image to obtain an average image; the mean value image and the semen image are subjected to difference to obtain a difference value image; superposing a grid template on the difference image; deleting a bright spot area which is not overlapped with the grid lines of the grid template in the difference image, and generating an intermediate image; performing expansion processing on the intermediate image to generate an expanded image; filling a hole area in the expanded image to generate a filled image; a final image is generated. On the basis of realizing the filtration of round cells, the filtering of impurities or bubbles in semen images is realized by the arrangement of the grid templates and the expansion treatment of the images and the template filtration, so that the sperm images in discrete states in semen are accurately detected, and the influence on the accuracy of sperm cell counting due to the fact that sperms gather in the impurity or bubble areas is avoided.
Description
Technical Field
The invention relates to the field of sperm detection, in particular to an image processing method for automatically filtering non-sperm components in semen.
Background
Computer aided analysis technology (CASA) based on sperm quality was rapidly developed at the end of the last 80 th century. It has been found that the automatic measurement and evaluation of various data of sperm by using a computer image analysis technology has many advantages, such as simple operation, high analysis speed, high calculation accuracy, good repeatability, providing accurate reference data for artificial insemination, improving the inspection level of inspection doctors, reducing the workload of the inspection doctors, and overcoming the defects of the traditional measurement method, such as time consumption, poor measurement accuracy, strong artificial subjectivity, etc.
In the prior art, most of image analysis methods for semen are as follows: firstly, amplifying and imaging semen by a phase-contrast microscope, then secondarily amplifying and collecting dynamic images under the microscope by a camera system, and counting or identifying sperms in the images. However, semen contains a large amount of non-sperm components including round cells, epithelial cells, bubbles, impurities and the like, and sperm in semen can concentrate at the positions of massive impurities and bubbles, so that the phenomenon of sperm aggregation is caused.
If all sperm are analyzed, the results of the detection will be greatly affected, so that calculation of sperm concentration and viability in discrete states is generally used clinically. If only simple image enhancement and binarization analysis techniques are used, all bright parts of the semen image will be identified as sperm, and the result will be several times different from the actual clinical result and not be credible. If the filtering of a large area of the object is simply performed using conventional methods, instability occurs, and this method is not very reliable because the large area is not continuous in all cases.
Disclosure of Invention
The invention aims to provide an image processing method for automatically filtering non-sperm components in semen, which has the advantage of accurately detecting sperm images in discrete states in semen.
The technical aim of the invention is realized by the following technical scheme:
an image processing method for automatically filtering non-sperm components in semen, comprising the following steps:
step 1: acquiring a semen image, and acquiring the semen image by using an image amplifying device;
step 2: filtering a round cell area in the semen image to generate a filtered image;
step 3: generating a mean image, and carrying out averaging treatment on the filtered image to obtain the mean image;
step 4: generating a difference image, and obtaining the difference image by making a difference between the mean value image and the semen image;
step 5: setting a grid template, and superposing the grid template on the difference image;
step 6: generating an intermediate image, deleting a bright spot area which is not overlapped with the grid lines of the grid template in the difference image, and generating the intermediate image;
step 7: generating an expanded image, and performing expansion processing on the intermediate image to generate an expanded image;
step 8: generating a filling image, filling a cavity area in the expansion image, and generating the filling image;
step 9: generating a final image, selecting an image area with a single area exceeding a preset area in the filling image as an impurity template, deleting an image area corresponding to the impurity template in the filtered image, and generating the final image.
By adopting the technical scheme, as the round cells in the semen are mostly the same in shape, the filtering of the round cell area is a simple filtering method in the image processing process, but the shapes of the impurities and the bubbles are irregular, and a large number of spermatids can be gathered at the positions of the impurities and the bubbles, and the gathered spermatids cannot be used as an object for researching the semen quality. After the difference image is generated, the bright spot areas in the semen image are all highlighted, the bright spot areas are deleted through the grid template, a large number of cells are collected only at the positions where massive impurities or bubbles are located to form dense bright spot areas, the dense bright spot areas form a whole area in an image expansion mode, the whole area is used as a template to filter out the corresponding area in the semen image, the filtering of the cell aggregation area formed by the impurities or the bubbles in the semen can be realized, only free sperms remain in the semen image, and the semen image in a discrete state in the semen is accurately detected.
As an improvement of the present invention, the image magnification device is configured as a phase-contrast microscope.
As an improvement of the present invention, the step 8 includes:
step 8-1: different areas in the expansion image are marked in a distinguishing mode;
step 8-2: filling the hole area of a single image area in the expansion image, and generating a filling image.
As an improvement of the invention, the distinguishing mark is set as a color mark.
As an improvement of the present invention, the step 4 includes: and performing brightness amplification after the difference between the mean value image and the semen image to generate the difference image.
As an improvement of the present invention, the method for calculating the gray value in the difference image includes:
g’=(g1-g2)*Mult+Add
wherein g1 is the gray value of a coordinate point in the semen image, g2 is the gray value of a corresponding coordinate point in the mean image, mult is the amplification factor, add is the offset, and g' is the gray value of a corresponding point in the difference image.
In summary, the invention has the following beneficial effects:
as the round cells, impurities and bubbles are filtered, only residual spermatids in the obtained final image can be detected accurately by collecting small-area samples in the actual research process.
Drawings
FIG. 1 is a flow chart of an image processing method for automatically filtering non-sperm components of semen;
FIG. 2 is a schematic diagram of a filtered image;
FIG. 3 is a schematic representation of a mean image;
FIG. 4 is a schematic diagram of a difference image;
FIG. 5 is a difference image after addition of a grid template;
FIG. 6 is an intermediate image schematic;
FIG. 7 is a schematic illustration of an inflated image;
FIG. 8 is a schematic illustration of a fill image;
fig. 9 is a schematic diagram of the final image.
Detailed Description
The present invention will be described in further detail below with reference to the drawings, wherein like parts are designated by like reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower", "bottom" and "top" used in the following description refer to directions in the drawings, and the words "inner" and "outer" refer to directions toward or away from, respectively, the geometric center of a particular component.
An image processing method for automatically filtering non-sperm components in semen, as shown in figure 1, comprises the following steps:
step 1: a semen image is acquired, using an image magnification device, preferably a phase contrast microscope.
Step 2: the round cell area is filtered, and the round cell area in the semen image is filtered, so that a filtered image shown in fig. 2 is generated.
Step 2-1: intercepting an image area corresponding to the round cells in the semen image by using image processing software such as Photoshop, and generating a corresponding round cell filtering template; under the Delphi development environment, the OPEN CV development package was used to convert the round cell filtration templates into template files. When the semen image is required to be subjected to round cell filtration, an OPEN CV development kit and a corresponding template file are utilized to filter an image area with the same shape as the template file in the semen image, so that a filtered image is generated.
Because the round cells are the same in shape and are round, after the round cell filtering template is generated through image processing software, template files generated according to the round cell filtering template can be compared with all areas in the semen image one by one, and the round image areas in the semen image are filtered through an OPEN CV development and inherent image processing technology, so that the filtering of the round cell image areas is realized.
Step 3: and (3) carrying out averaging treatment on the filtered image to generate an average image shown in fig. 3.
Step 4: and generating a difference image, and performing brightness amplification after the difference between the mean value image and the semen image to generate the difference image shown in fig. 4.
The calculation method for generating the gray value in the difference image comprises the following steps:
g’=(g1-g2)*Mult+Add
wherein g1 is the gray value of a coordinate point in the semen image, g2 is the gray value of a corresponding coordinate point in the mean image, mult is the amplification factor of brightness, add is the offset, and g' is the gray value of a corresponding point in the difference image.
Step 5: as shown in fig. 5, a grid template is set, which is preferably composed of a grid of 3*3 pixels, superimposed on the difference image.
Step 6: an intermediate image is generated, and bright spot areas which are not overlapped with grid lines of the grid template in the difference image are deleted, so that the intermediate image shown in fig. 6 is generated.
Step 7: the expansion process performs the expansion process on the intermediate image, and generates an expanded image as shown in fig. 7.
Step 8: and filling the image, namely filling the hole area in the expanded image, and generating a filling image.
Step 8-1: different areas in the dilated image are marked differently. Wherein the distinguishing mark comprises but is not limited to a color mark or a depth mark, preferably a color mark, and adjacent areas are marked with mutually different colors so as to facilitate a researcher to clearly view the layout of the expansion image.
Step 8-2: filling the hole area of the single image area in the dilated image generates a filled image as shown in fig. 8.
Step 9: and filtering the template, selecting an image area with a single area exceeding a preset area in the filling image as an impurity template, deleting an image area corresponding to the impurity template in the filtered image, and generating a final image as shown in fig. 9.
In summary, on the basis of realizing the filtration of the round cells, the arrangement of the grid template is matched with the expansion treatment of the image and the template filtration, so that the filtration of impurities or bubbles in the semen image is realized, the sperm image in a discrete state in the semen is accurately detected, and the influence on the accuracy of sperm cell counting due to the fact that sperm is gathered in the impurity or bubble area is avoided.
The present embodiment is only for explanation of the present invention and is not to be construed as limiting the present invention, and modifications to the present embodiment, which may not creatively contribute to the present invention as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present invention.
Claims (3)
1. An image processing method for automatically filtering non-sperm components in semen, which is characterized by comprising the following steps:
step 1: acquiring a semen image, and acquiring the semen image by using an image amplifying device;
step 2: filtering a round cell area in the semen image to generate a filtered image;
step 3: generating a mean image, and carrying out averaging treatment on the filtered image to obtain the mean image;
step 4: generating a difference image, and obtaining the difference image by making a difference between the mean value image and the semen image;
step 5: setting a grid template, and superposing the grid template on the difference image;
step 6: generating an intermediate image, deleting a bright spot area which is not overlapped with the grid lines of the grid template in the difference image, and generating the intermediate image;
step 7: generating an expanded image, and performing expansion processing on the intermediate image to generate an expanded image;
step 8: generating a filling image, filling a cavity area in the expansion image, and generating the filling image;
step 9: generating a final image, selecting an image area with a single area exceeding a preset area in the filling image as an impurity template, deleting an image area corresponding to the impurity template in the filtered image, and generating the final image;
the image magnification device is configured as a phase-contrast microscope;
the step 8 includes:
step 8-1: different areas in the expansion image are marked in a distinguishing mode;
step 8-2: filling a hole area of a single image area in the expanded image to generate a filled image;
the distinguishing mark is set as a color mark.
2. An image processing method for automatically filtering non-sperm components in semen as described in claim 1, wherein said step 4 comprises: and performing brightness amplification after the difference between the mean value image and the semen image to generate the difference image.
3. An image processing method for automatically filtering non-sperm components in semen as described in claim 2, wherein: the calculation method for generating the gray value in the difference image comprises the following steps:
g’=(g1-g2)*Mult+Add
wherein g1 is the gray value of a coordinate point in the semen image, g2 is the gray value of a corresponding coordinate point in the mean image, mult is the amplification factor, add is the offset, and g' is the gray value of a corresponding point in the difference image.
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| CN202075277U (en) * | 2011-05-10 | 2011-12-14 | 北京华方神火科技有限公司 | Sperm detecting room |
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| CN108428214A (en) * | 2017-02-13 | 2018-08-21 | 阿里巴巴集团控股有限公司 | A kind of image processing method and device |
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| US9316645B2 (en) * | 2011-10-07 | 2016-04-19 | Brown University | Methods, compositions and kits for imaging cells and tissues using nanoparticles and spatial frequency heterodyne imaging |
| US9322767B2 (en) * | 2012-04-17 | 2016-04-26 | i-calQ, LLC | Device for performing a blood, cell, and/or pathogen count and methods for use thereof |
| CN102800101A (en) * | 2012-08-09 | 2012-11-28 | 西北工业大学 | Satellite-borne infrared remote sensing image airport ROI rapid detection method |
| EP3071704B1 (en) * | 2013-11-20 | 2022-03-30 | Brigham and Women's Hospital, Inc. | System and method for sperm sorting |
| CN104933679B (en) * | 2015-07-06 | 2018-07-24 | 福州瑞芯微电子股份有限公司 | A kind of method and its correspondence system of enlarged drawing |
| EP3236418B1 (en) * | 2016-04-13 | 2020-10-28 | Canon Kabushiki Kaisha | Image processing apparatus, image processing method, and storage medium |
| CN106056118B (en) * | 2016-06-12 | 2018-08-24 | 合肥工业大学 | A kind of identification method of counting for cell |
| CN107545557A (en) * | 2016-06-23 | 2018-01-05 | 爱威科技股份有限公司 | Egg detecting method and device in excrement image |
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| CN202075277U (en) * | 2011-05-10 | 2011-12-14 | 北京华方神火科技有限公司 | Sperm detecting room |
| CN108229098A (en) * | 2016-12-09 | 2018-06-29 | 深圳市瀚海基因生物科技有限公司 | Monomolecular identification, method of counting and device |
| CN108428214A (en) * | 2017-02-13 | 2018-08-21 | 阿里巴巴集团控股有限公司 | A kind of image processing method and device |
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