CN108647713B - Embryo boundary identification and laser track fitting method - Google Patents
Embryo boundary identification and laser track fitting method Download PDFInfo
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- CN108647713B CN108647713B CN201810435730.2A CN201810435730A CN108647713B CN 108647713 B CN108647713 B CN 108647713B CN 201810435730 A CN201810435730 A CN 201810435730A CN 108647713 B CN108647713 B CN 108647713B
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- 210000001161 mammalian embryo Anatomy 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005553 drilling Methods 0.000 claims abstract description 13
- 210000002257 embryonic structure Anatomy 0.000 claims abstract description 4
- 238000005457 optimization Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 238000003708 edge detection Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000003698 laser cutting Methods 0.000 claims description 3
- 210000004027 cell Anatomy 0.000 description 7
- 238000006073 displacement reaction Methods 0.000 description 3
- 210000000170 cell membrane Anatomy 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 210000002308 embryonic cell Anatomy 0.000 description 1
- 230000003631 expected effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
- 238000011534 incubation Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000001000 micrograph Methods 0.000 description 1
- 238000004080 punching Methods 0.000 description 1
- 210000004994 reproductive system Anatomy 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration by the use of histogram techniques
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/181—Segmentation; Edge detection involving edge growing; involving edge linking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- 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
- G06T2207/30044—Fetus; Embryo
Abstract
The invention provides a method for recognizing embryo boundary and fitting laser track, which comprises the following steps: (A1) extracting characteristic graphs of the collected microscopic images of the embryos; (A2) analyzing the characteristic graph to obtain a geometric boundary line of the embryo; (A3) comparing a preset laser drilling track route with the geometric boundary line so as to determine whether to carry out path optimization; if yes, go to step (A4); if not, utilizing a preset laser drilling track route; (A4) and re-optimizing the laser track path to avoid damaging the embryo. The method has the advantages of evaluating the damage risk of the laser path to the embryo, resetting the laser drilling track and the like.
Description
Technical Field
The invention relates to embryo operation, in particular to an embryo boundary identification and laser track fitting method.
Background
In the auxiliary reproductive system, in the operation of embryo incubation implantation, when a user uses a laser membrane-breaking instrument, the position to be drilled is determined on a microscope image, then a proper laser perforation track is selected on a cell transparent belt, the laser perforation track is displayed as a narrow curve on a display, human eyes can hardly distinguish whether the laser aperture can touch the boundary of an embryo cell membrane, the laser track is selected to be far away from the cell and easily cannot achieve the expected effect, the laser track is selected to be too close and easily damages the cell membrane, the embryo cell sample is precious, the operation failure cannot be recovered, the uncertain factors completely depending on the preset perforation track of the human eyes are more, and the operation risk of the experiment is greatly increased. Currently, there is no specific implementation for the assessment and handling of this risk.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the embryo boundary identification and laser track fitting method, which can evaluate the damage risk of the laser path to the embryo and can re-optimize the alleged optimal laser drilling path.
The purpose of the invention is realized by the following technical scheme:
a method for recognizing embryo boundaries and fitting laser tracks comprises the following steps:
(A1) extracting characteristic graphs of the collected microscopic images of the embryos;
(A2) analyzing the characteristic graph to obtain a geometric boundary line of the embryo;
(A3) comparing a preset laser drilling track route with the geometric boundary line so as to determine whether to carry out path optimization;
if yes, go to step (A4);
if not, utilizing a preset laser drilling track route;
(A4) and re-optimizing the laser track path to avoid damaging the embryo.
According to the above method for recognizing embryo boundaries and fitting laser trajectories, optionally, the method for recognizing embryo boundaries and fitting laser trajectories further comprises the following steps:
(A5) and decomposing and converting the optimized pixel coordinate value into a rotation angle quantity and an offset quantity of a motor on the two-dimensional mobile station.
According to the above method for identifying the boundary of the embryo and fitting the laser track, optionally, in step (a1), before extracting the feature pattern, the acquired microscopic image of the embryo is subjected to noise reduction.
According to the method for recognizing the embryo boundary and fitting the laser track, preferably, the noise reduction treatment is performed in a specific manner as follows:
carrying out noise reduction on the collected black and white image pixels by using a wiener filtering method;
wherein I represents a pixel matrix, N represents a horizontal pixel width, M represents a vertical pixel width, μ represents an average value, and δ represents a variance; the denoised result A (i, j) is:
according to the above method for recognizing embryo boundary and fitting laser trajectory, preferably, in step (a1), the feature pattern is extracted by:
(A11) contrast enhancement: counting all pixel points on the microscopic image by using a histogram, and multiplying the gray value of all the pixel points by a stretching coefficient k to enhance the contrast of the image so as to make black more black and white more white;
(A12) gradient detection: extracting the characteristics of image pixel points, and performing convolution processing on the image by using sobel convolution check; and combining the convolution result in the x direction and the convolution result in the y direction by using a Laplace formula to obtain a global edge gradient map:
according to the above method for identifying embryo boundary and fitting laser track, preferably, in step (a2), the geometric boundary line is obtained by:
and clearing all gray values of pixel points of which the gray values are lower than the threshold value in the image obtained after the edge detection, thereby obtaining a geometric boundary line.
According to the above method for identifying embryo boundary and fitting laser track, preferably, in step (a3), the comparison is performed by:
(A31) inputting a laser hitting track route on a software interface layer, and calculating according to the set aperture of laser drilling and track pixel point coordinates to obtain a boundary coordinate matrix of a laser track;
(A32) and comparing the boundary coordinate matrix with the boundary of the contour map, and if the overlapped part can be detected, optimizing the path.
According to the above method for identifying embryo boundaries and fitting laser trajectories, in step (a4), the path optimization method preferably includes:
selecting a closest geometric path track along the boundary line of the cell; the pixel values on the path satisfy:
According to the embryo boundary identification and laser trajectory fitting method described above, preferably, in step (a5),
let the coordinates of the laser spot present on the screen be (x)s1，ys1) The target coordinate is (x)s2，ys2) The motor coordinate is converted into the current coordinate (x) through the conversion matrix A-1m1，ym1) The target coordinate is (x)m2，ym2) The moving distances of the two motors are | x respectivelym2-xm1|、|ym2-ym1L, direction depends on (x)2-x1)、(y2-y1) Positive and negative, if negative, left/down direction, and regular, right/up direction.
Compared with the prior art, the invention has the beneficial effects that:
1. in the laser membrane rupture instrument, the function can assist a user in setting a laser hitting path and automatically evaluating the damage risk of the laser path to embryonic cells;
2. the scheme can optimize and generate the optimal laser hitting path again for the track with the damage risk, reduces the difficulty of setting the laser punching path for the user, reduces the time cost, and simultaneously improves the success rate of the experiment.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a flow chart of a method of embryo boundary identification and laser trajectory fitting according to an embodiment of the present invention.
Detailed Description
Fig. 1 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and reproduce the invention. Some conventional aspects have been simplified or omitted for the purpose of teaching the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will be within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the following alternative embodiments, but is only limited by the claims and their equivalents.
Example (b):
fig. 1 schematically shows a flowchart of an embryo boundary identification and laser trajectory fitting method according to embodiment 1 of the present invention, and as shown in fig. 1, the embryo boundary identification and laser trajectory fitting method includes the following steps:
(A1) images of embryos were acquired using a Watec black and white camera, which has minimum configuration requirements for the computer:
pentium four-core 1.8GHZ, 1G memory, 80GB hard disk; USB3.0 functions are supported;
and (3) noise reduction treatment: carrying out noise reduction on a black-and-white image acquired by a camera, firstly taking out a picture, and solving the average value and the variance of all pixel gray values in the picture:
carrying out noise reduction on the collected black and white image pixels by using a wiener filtering method;
wherein I represents a pixel matrix, N represents a horizontal pixel width, M represents a vertical pixel width, μ represents an average value, and δ represents a variance; the denoised result A (i, j) is:
characteristic graph extraction:
(A11) contrast enhancement: performing contrast enhancement processing on the noise-reduced image, counting gray values of all pixel points on the noise-reduced image by using a histogram, and multiplying the gray values (the gray values are 0-255) of all the pixel points by a stretching coefficient k to enhance the contrast of the image, so that black is darker and white is whiter;
k is an enhancement coefficient, A (i, j) is a pixel matrix after noise reduction, and B (i, j) is a pixel matrix after contrast enhancement;
in order to reduce the calculation amount of the subsequent image processing, the gray values of the pixels are divided into 10 groups which are respectively [0-10 ], [10-20 ], [20-30) and …;
(A12) gradient detection: performing convolution processing on the stretched image by using a convolution kernel; the convolution calculation can be regarded as a weighted summation process, which makes each pixel of the image domain perform convolution calculation respectively on the following two-dimensional operators:
and combining the convolution result in the x direction and the convolution result in the y direction by using a Laplace formula to obtain a global edge gradient map:
(A2) removing all pixel points with the gray value lower than a certain threshold value in the image obtained after edge detection to obtain the most reasonable embryo cell graph boundary line;
(A3) drawing a laser drilling track path in the software interface layer, wherein the path is a continuous curve;
comparing the track coordinates with the boundary line pixel coordinates obtained in the step (A2), and if the coincidence with the boundary pixel points is detected;
if the overlap exists, the step (A4) is carried out;
if no coincidence exists, the preset laser drilling track path is considered to be reasonable, and the step (A6) is directly executed;
(A4) selecting a closest geometric path track along the boundary line of the embryo; the pixel values on the path satisfy:
(A5) taking out the finally determined pixel coordinates on the laser drilling track path, and converting the coordinates of the pixel points into coordinate points on a laser displacement table through a conversion relation matrix;
if the current coordinates of the track on the screen are (xs1, ys1), the target coordinates are (xs2, ys2), the current coordinates of the motor coordinates are (xm1, ym1) through the conversion matrix A-1, the target coordinates are (xm2, ym2), the moving distances of the two motors are | xm2-xm1|, | ym2-ym1|, the directions depend on the positive and negative polarities of x2-x1 and y2-y1, if the moving distances are negative, the directions are left/down directions, and the regular directions are right/up directions
(A6) In the singlechip system, the moving distance is converted into the rotating step number of the stepping motor by a displacement angle conversion formula
N is the step number of the stepping motor, L is the moving distance of the motor, and deltas is the moving distance of the displacement table corresponding to a single stepping angle.
Claims (8)
1. A method for recognizing embryo boundary and fitting laser track is characterized in that: the embryo boundary identification and laser track fitting method comprises the following steps:
(A1) extracting characteristic graphs of the collected microscopic images of the embryos;
(A2) analyzing the characteristic graph to obtain a geometric boundary line of the embryo;
(A3) comparing the track coordinates of the preset laser drilling track route with the pixel coordinates of the geometric boundary line so as to determine whether to carry out path optimization;
if the track coordinates and the pixel coordinates are coincident, entering a step (A4);
if the track coordinates and the pixel coordinates are not overlapped, a preset laser drilling track route is utilized;
(A4) and re-optimizing the laser track path to avoid damaging the embryo in a specific mode:
and selecting a closest geometric path track along the boundary line of the embryo, wherein the pixel values on the path satisfy the following conditions:
2. The method for embryo boundary recognition and laser trajectory fitting according to claim 1, wherein: the embryo boundary identification and laser track fitting method further comprises the following steps:
(A5) and decomposing and converting the optimized pixel coordinate value into a rotation angle quantity and an offset quantity of a motor on the two-dimensional mobile station.
3. The method for embryo boundary recognition and laser trajectory fitting according to claim 1, wherein: in step (a1), before extracting the feature pattern, the acquired microscopic image of the embryo is subjected to noise reduction.
4. The method of claim 3, wherein the method comprises the steps of: the specific way of the noise reduction treatment is as follows:
carrying out noise reduction on the collected black and white image pixels by using a wiener filtering method;
wherein I represents a pixel matrix, N represents a horizontal pixel width, M represents a vertical pixel width, μ represents an average value, and δ represents a variance; the denoised result A (i, j) is:
5. The method for embryo boundary recognition and laser trajectory fitting according to claim 1, wherein: in the step (a1), the feature pattern is extracted in a manner that:
(A11) contrast enhancement: counting all pixel points on the microscopic image by using a histogram, and multiplying the gray value of all the pixel points by a stretching coefficient k to enhance the contrast of the image so as to make black more black and white more white;
(A12) gradient detection: extracting the characteristics of image pixel points, and performing convolution processing on the image by using sobel convolution check; and combining the convolution result in the x direction and the convolution result in the y direction by using a Laplace formula to obtain a global edge gradient map:
6. The method of claim 5, wherein the step of identifying the boundary of the embryo and fitting the laser track comprises the steps of: in step (a2), the geometric boundary line is obtained by:
and clearing all gray values of pixel points of which the gray values are lower than the threshold value in the image obtained after the edge detection, thereby obtaining a geometric boundary line.
7. The method for embryo boundary recognition and laser trajectory fitting according to claim 1, wherein: in step (a3), the alignment is performed by:
(A31) inputting a laser hitting track route on a software interface layer, and calculating according to the set aperture of laser drilling and track pixel point coordinates to obtain a boundary coordinate matrix of a laser track;
(A32) and comparing the boundary coordinate matrix with the boundary of the contour map, and if the overlapped part can be detected, optimizing the path.
8. The method of claim 2, wherein the step of identifying the boundary of the embryo and fitting the laser track comprises the steps of: in the step (a5),
let the coordinates of the laser spot present on the screen be (x)s1，ys1) The target coordinate is (x)s2，ys2) The motor coordinate is converted into the current coordinate (x) through the conversion matrix A-1m1，ym1) The target coordinate is (x)m2，ym2) The moving distances of the two motors are | x respectivelym2-xm1|、|ym2-ym1L, direction depends on (x)m2-xm1) And (y)m2-ym1) Positive or negative of (e.g., (x)m2-xm1) Is negative and the direction of movement is left, e.g. (x)m2-xm1) Positive, the moving direction is right; such as (y)m2-ym1) Is negative and the direction of movement is downward, e.g. (y)m2-ym1) Positive, the direction of movement is upward.
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| CN112656542A (en) * | 2020-12-17 | 2021-04-16 | 南京师范大学 | Method for assisting blastocyst incubation at specific site |
| CN112734782A (en) * | 2021-01-25 | 2021-04-30 | 中科和光（天津）应用激光技术研究所有限公司 | Laser path planning visual algorithm |
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