CN110930345A - Sperm tail recognition method - Google Patents
Sperm tail recognition method Download PDFInfo
- Publication number
- CN110930345A CN110930345A CN201811009206.5A CN201811009206A CN110930345A CN 110930345 A CN110930345 A CN 110930345A CN 201811009206 A CN201811009206 A CN 201811009206A CN 110930345 A CN110930345 A CN 110930345A
- Authority
- CN
- China
- Prior art keywords
- image
- area
- tail
- sperm
- filtering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 210000000582 semen Anatomy 0.000 claims abstract description 49
- 238000001914 filtration Methods 0.000 claims abstract description 37
- 230000003321 amplification Effects 0.000 claims description 8
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000003672 processing method Methods 0.000 claims description 2
- 239000012535 impurity Substances 0.000 abstract description 12
- 238000011160 research Methods 0.000 abstract description 3
- 238000010187 selection method Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010186 staining Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011496 digital image analysis Methods 0.000 description 1
- 238000004043 dyeing Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000009027 insemination Effects 0.000 description 1
- 238000009612 semen analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
A sperm tail recognition method comprises the following steps: step 1: acquiring an image, and step 2: acquiring a difference image: and step 3: acquiring a tail image; and 4, step 4: acquiring a sperm head area image; and 5: merging the tail image and the sperm head area image; step 6: and filtering the bright spot area without the long tail area within the preset distance to obtain a fine sub-area image. By obtaining the difference image, the bright spot area in the semen sample image is filtered, so that the non-long tail area is conveniently filtered, and the image of the impurity or the cell which is greatly different from the sperm structure in the semen is realized. By the distance selection method, the bright spot image without a long tail-shaped area nearby can be filtered, and further the bright spot area of the non-sperm cell can be filtered. The method realizes accurate filtration of impurities in the semen and is convenient for researchers to count and research the semen.
Description
Technical Field
The invention relates to the field of sperm detection, in particular to a sperm tail identification method.
Background
Computer-assisted analysis technology (CASA) based on sperm quality has evolved rapidly by the end of the last 80 th century. People find that the automatic measurement and evaluation of various data of sperms by using a computer image analysis technology has a plurality of advantages, and the method has the advantages of simple operation, high analysis speed, high calculation precision and good repeatability, provides accurate reference data for artificial insemination, improves the inspection level of inspection doctors, reduces the workload of the inspection doctors, and can overcome the defects of the traditional determination method, such as time consumption, poor measurement precision, strong manual subjectivity and the like.
In the prior art, methods for analyzing semen are mostly as follows: firstly, sperm in an image is counted or identified by staining a semen sample, amplifying the semen sample by a microscope and a camera system and acquiring a dynamic image under the microscope. However, this method has the following problems:
firstly, after a semen sample is dyed, the activity of sperms can be influenced to a certain extent, so that the analysis of semen quality by answer is influenced;
secondly, the semen contains a lot of non-sperm components (round cells, impurities, etc.), which can affect the judgment of the number of sperm.
Aiming at the problems, the existing semen analysis process omits the step of dyeing the semen sample, the semen sample is directly detected through a high-power microscope, in the detection process, a phase contrast microscope is used for obtaining cell images in the semen, the sperm cells in the semen can be displayed into bright spots under the microscope, and therefore inspectors can count the bright spots automatically through a CASA system to obtain the number of the sperms.
Although the method can avoid the influence of the staining process on the activity of the semen, a lot of impurities under a microscope can also be displayed as bright spots, and the bright spots have a display effect similar to that of the sperm, so that the calculation of the number of the sperm by the CASA system is influenced.
Disclosure of Invention
The invention aims to provide a sperm tail identification method, which has the advantages that non-sperm cells or impurities in semen can be filtered out through the structural form of the tail of the sperm, and the quantity of the sperm in the semen can be clearly shown.
The technical purpose of the invention is realized by the following technical scheme:
a sperm tail recognition method is characterized by comprising the following steps:
step 1: acquiring an image, and acquiring a semen sample image by using image amplification equipment;
step 2: obtaining a difference image, and filtering a bright spot area in the semen sample image to obtain the difference image;
and step 3: acquiring a tail image, and filtering a non-long tail region in the difference image to acquire the tail image;
and 4, step 4: acquiring a sperm head area image, and filtering a non-bright point area in the sperm sample image to obtain a sperm head area image;
and 5: merging the tail image and the sperm head area image;
step 6: and acquiring a fine sub-area image, and filtering a bright spot area without a long tail area within a preset distance to acquire the fine sub-area image.
By adopting the technical scheme, the impurity types in the semen are too many, and impurities can be displayed into bright spot images which are the same as sperm cells under a microscope, so that the interference is generated when the sperm is counted or the images are sampled. By obtaining the difference image, the bright spot area in the semen sample image is filtered, so that the non-long tail area is conveniently filtered, and the image of the impurity or the cell which is greatly different from the sperm structure in the semen is realized. Because no long tail-shaped area is arranged near the non-sperm bright spot image, the distance selection method can realize the filtration of the bright spot image without the long tail-shaped area near the filtration, thereby realizing the filtration of the bright spot area of the non-sperm cells. Therefore, the method realizes accurate filtration of the impurities in the semen and is convenient for researchers to count and research the semen.
As a refinement of the present invention, said step 2 comprises:
step 2-1: carrying out averaging processing on the semen sample image to obtain a mean value image;
step 2-2: and carrying out difference on the average image and the semen sample image to generate the difference image.
As an improvement of the present invention, the method of the equalization processing is:
wherein, Sxy represents that the central point is at (x, y), a filter window with the size of m multiplied by n is selected, g (s, t) represents an original image, and f (x, y) represents an image obtained after mean filtering.
As a refinement of the present invention, the step 2-2 further comprises: and carrying out brightness amplification after the difference is carried out on the mean value image and the semen sample image to generate the difference image.
As an improvement of the present invention, the brightness amplification method comprises:
g’＝(g1-g2)*Mult+Add
wherein g1 is the semen sample image, g2 is the mean image, Mult is the magnification factor, Add is the offset, and g' is the difference image.
As a refinement of the present invention, said step 3 comprises:
step 3-1: carrying out binarization processing on the difference image to generate a binarization image;
step 3-2: and filtering the non-long tail-shaped area in the binary image to obtain the tail image.
As a modification of the present invention, the step 3-2 comprises:
step 3-2-1: selecting a roundness range and a convexity range;
step 3-2-2: and filtering the image area which cannot simultaneously meet the roundness range and the convexity range to obtain the tail image.
As an improvement of the present invention, a method of filtering an image region that cannot satisfy both a roundness range and a convexity range includes:
wherein p is the central point of the selected image area, pi is any point on the contour of the selected image area, and Roundness is the Roundness of the selected image area;
n＝{f(R-＜Roundness＜R+)andf(C-＜C＜C+)}
wherein n is the tail image, the roundness range is, and the convexity range is.
In conclusion, the invention has the following beneficial effects:
the method has the advantages that the filtering precision is high, the filtering of impurities, cells and non-sperm bright spot areas in the semen can be respectively realized by filtering the non-long tail-shaped areas and selecting the distance, and the obtained sperm area images can clearly show the number of the sperms in the semen.
And secondly, selecting the long tail-shaped area by adopting a selection mode of roundness and convexity, so that the complexity of data calculation processing is reduced while the long tail-shaped area is selected.
Drawings
FIG. 1 is a flow chart of a sperm tail identification process;
FIG. 2 is a schematic diagram of an image of a semen sample;
FIG. 3 is a schematic diagram of a mean image;
FIG. 4 is a schematic diagram of a difference image;
FIG. 5 is a schematic view of a tail image;
FIG. 6 is a schematic representation of a sperm head region image;
fig. 7 is a schematic diagram of a blended image.
Detailed Description
The present invention will be described in further detail with reference to the attached drawings, wherein like parts are designated by like reference numerals. It should be noted that as used in the following description, the terms "front," "rear," "left," "right," "upper" and "lower," "bottom" and "top" refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
A sperm tail recognition method, as shown in fig. 1, comprising the following steps:
step 1: an image is acquired and an image of the semen sample as shown in figure 2 is acquired using an image magnification device, preferably a phase contrast microscope.
Step 2: and obtaining a difference image, and filtering a bright spot area in the semen sample image to obtain the difference image.
Step 2-1: the semen sample image is averaged to obtain a mean image as shown in fig. 3.
Step 2-2: and (3) performing difference on the average image and the semen sample image to generate a difference image shown in figure 4.
Step 2-1: the semen sample image is averaged to obtain a mean image as shown in fig. 3.
The equalization processing method comprises the following steps:
wherein, Sxy represents that the central point is at (x, y), a filter window with the size of m multiplied by n is selected, g (s, t) represents an original image, and f (x, y) represents an image obtained after mean filtering.
Step 2-2: and carrying out brightness amplification by carrying out difference on the mean image and the semen sample image to generate a difference image. The brightness amplification method comprises the following steps:
g’＝(g1-g2)*Mult+Add
wherein g1 is the semen sample image, g2 is the mean image, Mult is the magnification factor, Add is the offset, g' is the difference image.
And step 3: and acquiring a tail image, and filtering a non-long tail region in the difference image to acquire the tail image.
Step 3-1: and carrying out binarization processing on the difference image to generate a binarized image.
Step 3-2: and filtering the non-long tail-shaped area in the binary image to obtain a tail image.
Step 3-2-1: and selecting a roundness range and a convexity range, wherein the roundness range is [ R-, R + ], and the convexity range is [ C-, C + ].
Step 3-2-2: the image area which cannot satisfy both the roundness range and the convexity range is filtered, and the tail image as shown in fig. 5 is obtained.
The method for filtering the image area which cannot satisfy the roundness range and the convexity range simultaneously comprises the following steps:
wherein p is the central point of the selected image area, pi is any point on the contour of the selected image area, and Roundness is the Roundness of the selected image area;
n＝{f(R-＜Roundness＜R+)andf(C-＜C＜C+)}
and 4, step 4: and (3) acquiring a sperm head area image, and filtering the non-bright point area in the semen sample image by adopting the methods from the step 1 to the step 2 to obtain the sperm head area image shown in the figure 6.
Further, the image is subjected to binarization processing after the step 2 in the process of acquiring the image of the head area of the sperm, so that the display of the bright spot area in the image of the head area of the sperm is clearer.
And 5: the tail image and the sperm head region image were merged to obtain a blended image as shown in fig. 7.
Step 6: and obtaining a fine sub-area image, and filtering a bright spot area without a long tail area within a preset distance in the mixed image to obtain the fine sub-area image.
According to the content, the bright spot area in the semen sample image is filtered by obtaining the difference image, so that the filtering of the non-long tail-shaped area is facilitated, and the image of the impurity or the cell which is greatly different from the sperm structure in the semen is realized. Because no long tail-shaped area is arranged near the non-sperm bright spot image, the distance selection method can realize the filtration of the bright spot image without the long tail-shaped area near the filtration, thereby realizing the filtration of the bright spot area of the non-sperm cells. Therefore, the method realizes accurate filtration of the impurities in the semen and is convenient for researchers to count and research the semen.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (8)
1. A sperm tail recognition method is characterized by comprising the following steps:
step 1: acquiring an image, and acquiring a semen sample image by using image amplification equipment;
step 2: obtaining a difference image, and filtering a bright spot area in the semen sample image to obtain the difference image;
and step 3: acquiring a tail image, and filtering a non-long tail region in the difference image to acquire the tail image;
and 4, step 4: acquiring a sperm head area image, and filtering a non-bright point area in the sperm sample image to obtain a sperm head area image;
and 5: merging the tail image and the sperm head area image;
step 6: and acquiring a fine sub-area image, and filtering a bright spot area without a long tail area within a preset distance to acquire the fine sub-area image.
2. A method of sperm tail identification as defined in claim 1, wherein: the step 2 comprises the following steps:
step 2-1: carrying out averaging processing on the semen sample image to obtain a mean value image;
step 2-2: and carrying out difference on the average image and the semen sample image to generate the difference image.
3. A sperm tail identifying method as defined in claim 2, wherein: the equalization processing method comprises the following steps:
wherein, Sxy represents that the central point is at (x, y), a filter window with the size of m multiplied by n is selected, g (s, t) represents an original image, and f (x, y) represents an image obtained after mean filtering.
4. A method of sperm tail identification as defined in claim 3, wherein: the step 2-2 further comprises: and carrying out brightness amplification after the difference is carried out on the mean value image and the semen sample image to generate the difference image.
5. A method of sperm tail identification as defined in claim 4, wherein: the brightness amplification method comprises the following steps: g' ═ g1-g2 mutt + Add
Wherein g1 is the semen sample image, g2 is the mean image, Mult is the magnification factor, Add is the offset, and g' is the difference image.
6. A method of sperm tail identification as defined in claim 5, wherein: the step 3 comprises the following steps:
step 3-1: carrying out binarization processing on the difference image to generate a binarization image;
step 3-2: and filtering the non-long tail-shaped area in the binary image to obtain the tail image.
7. A method of sperm tail identification as defined in claim 6, wherein: the step 3-2 comprises the following steps:
step 3-2-1: selecting a roundness range and a convexity range;
step 3-2-2: and filtering the image area which cannot simultaneously meet the roundness range and the convexity range to obtain the tail image.
8. A method of sperm tail identification as defined in claim 7, wherein: the method for filtering the image area which cannot satisfy the roundness range and the convexity range simultaneously comprises the following steps:
wherein p is the central point of the selected image area, pi is any point on the contour of the selected image area, and Roundness is the Roundness of the selected image area;
n＝{f(R-＜Roundness＜R+)andf(C-＜C＜C+)}
wherein n is a tail image, the roundness range is [ R-, R + ], and the convexity range is [ C-, C + ].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811009206.5A CN110930345B (en) | 2018-08-31 | 2018-08-31 | Sperm tail identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811009206.5A CN110930345B (en) | 2018-08-31 | 2018-08-31 | Sperm tail identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110930345A true CN110930345A (en) | 2020-03-27 |
| CN110930345B CN110930345B (en) | 2023-05-30 |
Family
ID=69854957
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811009206.5A Active CN110930345B (en) | 2018-08-31 | 2018-08-31 | Sperm tail identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110930345B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563550A (en) * | 2020-04-30 | 2020-08-21 | 北京百度网讯科技有限公司 | Sperm morphology detection method and device based on image technology |
| CN112330660A (en) * | 2020-11-24 | 2021-02-05 | 成都朴华科技有限公司 | Sperm tail detection method and system based on neural network |
| CN113221860A (en) * | 2021-07-07 | 2021-08-06 | 深圳市瑞图生物技术有限公司 | DNA fragment recognition method, device, computer equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150031019A1 (en) * | 2013-03-14 | 2015-01-29 | Cellcura As | Computer Assisted Sperm Profile Analysis and Recognition |
| US20170091926A1 (en) * | 2014-03-12 | 2017-03-30 | Livestock Improvement Association Of Japan, Inc. | Sperm inspection method and device |
-
2018
- 2018-08-31 CN CN201811009206.5A patent/CN110930345B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150031019A1 (en) * | 2013-03-14 | 2015-01-29 | Cellcura As | Computer Assisted Sperm Profile Analysis and Recognition |
| US20170091926A1 (en) * | 2014-03-12 | 2017-03-30 | Livestock Improvement Association Of Japan, Inc. | Sperm inspection method and device |
Non-Patent Citations (4)
| Title |
|---|
| 宋继清等: "改进的动物精子图像分割算法" * |
| 林玉狮: "人类精子细胞图像的检测和追踪" * |
| 汪创: "基于计算机视觉的动物精子形态分析系统" * |
| 谭征等: "一种密度聚类的染色精子医学图像识别算法" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563550A (en) * | 2020-04-30 | 2020-08-21 | 北京百度网讯科技有限公司 | Sperm morphology detection method and device based on image technology |
| CN111563550B (en) * | 2020-04-30 | 2023-08-25 | 北京百度网讯科技有限公司 | Sperm morphology detection method and device based on image technology |
| CN112330660A (en) * | 2020-11-24 | 2021-02-05 | 成都朴华科技有限公司 | Sperm tail detection method and system based on neural network |
| CN112330660B (en) * | 2020-11-24 | 2024-02-02 | 成都朴华科技有限公司 | Sperm tail detection method and system based on neural network |
| CN113221860A (en) * | 2021-07-07 | 2021-08-06 | 深圳市瑞图生物技术有限公司 | DNA fragment recognition method, device, computer equipment and storage medium |
| CN113221860B (en) * | 2021-07-07 | 2021-10-22 | 深圳市瑞图生物技术有限公司 | DNA fragment recognition method, device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110930345B (en) | 2023-05-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110930345A (en) | Sperm tail recognition method | |
| US9934571B2 (en) | Image processing device, program, image processing method, computer-readable medium, and image processing system | |
| US20070298454A1 (en) | Analysis Of Cell Morphology And Motility | |
| CN109166111B (en) | Electronic ink screen defect detection method and system | |
| CN108921819B (en) | Cloth inspecting device and method based on machine vision | |
| CN109859160B (en) | Aluminum magnesium alloy casting internal defect image identification method based on machine vision | |
| US20090324050A1 (en) | Cell image processor and cell image processing method | |
| CN103984939B (en) | A kind of sample visible component sorting technique and system | |
| CN108537787B (en) | Quality judgment method for face image | |
| CN109182081A (en) | A kind of unicellular separation system based on image processing model | |
| CN108802052A (en) | A kind of detecting system and its detection method about slide fastener defect | |
| CN109341524A (en) | A kind of optical fiber geometric parameter detection method based on machine vision | |
| CN107525768A (en) | A kind of method of quality control of DNA ploidy body analytical equipment | |
| CN110007493A (en) | Broken bright spot detection method in liquid crystal display | |
| CN104866838B (en) | A kind of front vehicles automatic testing method of view-based access control model | |
| CN112819844A (en) | Image edge detection method and device | |
| CN106960424B (en) | Tubercle bacillus image segmentation and identification method and device based on optimized watershed algorithm | |
| CN109948544B (en) | Automatic positioning and identifying method for target bacterial colony | |
| CN109003228B (en) | Dark field microscopic large-view-field automatic stitching imaging method | |
| CN116563298B (en) | Cross line center sub-pixel detection method based on Gaussian fitting | |
| CN109540890A (en) | A kind of DNA quantitative analysis method based on microcytoscope image | |
| US20170169557A1 (en) | Process and device for direct measurements of plant stomata | |
| CN113552126A (en) | Reticulocyte detection method and system | |
| CN113657335A (en) | Mineral phase identification method based on HSV color space | |
| CN111583186A (en) | Pathological ER/PR (ER/PR) cell nucleus counting method and system for clinical application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |