CN109559823B - DVS data processing method beneficial to sperm activity analysis - Google Patents
DVS data processing method beneficial to sperm activity analysis Download PDFInfo
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Abstract
The invention relates to the technical field of machine vision, and discloses a DVS data processing method beneficial to sperm motility analysis. By the invention, after acquiring the sperm motility event record file obtained by recording fresh semen under a microscope through the DVS camera, reading the activation position information and the activation time information of all sperm motility events from the sperm motility event record file, and locally outputting and displaying the read information through the three-dimensional view and the two-dimensional view, so that medical staff or researchers can intuitively feel the sperm motility condition, thereby greatly facilitating the qualitative and quantitative analysis of sperm motility according to the multi-dimensional view, being beneficial to quickly and accurately obtaining the sperm motility analysis result and promoting the full development of the reproductive medicine field.
Description
Technical Field
The invention belongs to the technical field of machine vision, and particularly relates to a DVS data processing method beneficial to sperm motility analysis.
Background
More and more machine vision in the 21 st century replaces human eyes, helps people to acquire visual information, and advanced machines are continuously available, so that the machine vision system is applied to various industries, such as industrial automatic detection to replace manual detection, use of fire-fighting unmanned aerial vehicles to carry out disaster patrol and auxiliary rescue on forests, and the like. In any field, the figure of machine vision can appear. In order to enable a machine to process data more quickly and track an object more accurately, people often continuously upgrade an algorithm, but in reality, when the machine is used in an intricate environment, a computer still faces huge calculation amount during data processing, and a plurality of problems such as large errors, large power consumption and the like are caused. To address these problems, a new concept of camera/camcorder comes across the world-event-based cameras. The DVS camera (Dynamic Vision Sensor) is one of the types, and changes the way of recording images by the conventional camera, starting from the source, only records useful data, thereby solving a series of problems of large power consumption, slow response and the like of the conventional camera.
The dynamic vision sensor is a retina-like device that records images by simulating the characteristics of the retina, and is essentially different from the conventional vision sensor in the manner of acquiring image data. The conventional video camera records each frame, but the DVS video camera is similar to the human eye, records the change in the picture, and does not generate new data when the field of view is unchanged. That is, when an object in the picture moves or the light changes, a pulse is generated at the current pixel position, which is equivalent to that for each pixel point on the picture, only when the light intensity changes, that is, the gray level changes, the signal of the event is output. For example, when recording a still picture, the DVS camera does not leave any record, but when a light beam is projected at a position in the picture, the brightness of the position increases and when a threshold value is exceeded, the DVS camera generates a time slice to record the event, and the event includes position information, light intensity information, and occurrence time information of the light change. Therefore, compared with the traditional camera, the DVS camera has great advantages in the aspects of response time, data transmission speed, dynamic range and the like, and can greatly save the expenses of post analysis, calculation and energy consumption resources.
On the other hand, the environmental pollution is caused while the technology is developed, wherein a chemical substance with estrogen-like characteristics is introduced into the human body, and if the chemical substance enters the human body, the activity of sperms is damaged in each stage of the development of the sperms, and unhealthy work and rest habits of people are also adopted, so that male sperm development malformation is caused by multiple factors, the quantity and the quality of the sperms are influenced, and in severe cases, the male fertility can be completely lost. According to the evaluation of the World Health Organization (WHO), the ratio of a couple with normal fertility to a couple with a reproductive disorder is 6:1, namely, one seventh of couples have the reproductive disorder. This makes human assisted reproductive technology (i.e. tube baby technology) available, and the study of sperm motility is naturally the most important link in tube baby technology. After the sperm activity is deeply researched, the death rate of the test-tube infants can be reduced, the cultivation cost can also be reduced, and the sperm activity research is closely related to the human survival.
Sperm motility (sperm motility) refers to the percentage of sperm in the semen that are in progressive motion. Since only the sperm with forward movement may have normal viability and fertilization capability, the vitality of the sperm is closely related to the female conception rate, and is one of the main indicators for evaluating the quality of the sperm. The sperm motility detection is mainly carried out by manual qualitative observation under a microscope at present, and has slower detection speed and low detection flux. The currently introduced automatic detection is mainly based on traditional camera photographing, image recognition is performed through each frame of a photo or a video screen, and then qualitative and quantitative analysis is performed on the image. Therefore, the problems of data redundancy, large processing resource requirement, slow output and processing result and the like exist in both the recording process and the later activity analysis process, and the sperm activity analysis work or research is not facilitated for people.
Disclosure of Invention
In order to solve the problems of low detection speed, data redundancy, large processing resource requirement, low output processing result and the like in the conventional sperm motility analysis process, the invention aims to provide a DVS data processing method which is favorable for sperm motility analysis based on data acquisition of a DVS camera.
The technical scheme adopted by the invention is as follows:
a DVS data processing method for facilitating sperm motility analysis comprising the steps of:
s101, importing a sperm motility event recording file obtained by recording fresh semen under a microscope through a DVS (digital video system) camera;
s102, aiming at each time slice in the sperm activity event record file, reading sperm activity event column addresses recorded in the time slices
Sperm motility event row address
And activation time point of sperm motility event
；
S103, addressing all sperm motility events
Sperm motility event row address
And activation time point of sperm motility event
Sequentially written into the memory in time sequence
In a data matrix of individual elements, wherein,
is a natural number not less than 1 ten thousand;
s104, outputting all sperm activity event column addresses which are displayed in the data matrix and are continuously recorded in a first target time period through a three-dimensional view
Sperm motility event row address
And activation time point of sperm motility event
Wherein, the three-dimensional coordinate axes in the three-dimensional view are respectively the addresses of the corresponding sperm motility events
Axis of abscissas
Corresponding sperm motility event row address
Axis of ordinates
And corresponding activation time point of sperm motility event
Time axis of
；
S105, outputting all sperm activity event column addresses which are displayed in the data matrix and are continuously recorded in a second target time period through a two-dimensional view
And sperm motility event row address
Wherein, the two-dimensional coordinate axes in the two-dimensional view are respectively the addresses of the corresponding sperm motility event columns
Axis of abscissas
And corresponding sperm motility event row address
Axis of ordinates
The number of the two-dimensional views is multiple, and the two-dimensional views correspond to multiple second target time periods which are equal in time length and are at different times in a one-to-one mode.
Preferably, after the step S105, the following steps are further included:
s106, searching the sperm motility area in the two-dimensional view by applying a clustering algorithm, and identifying the sperm motility area in the corresponding two-dimensional view after the sperm motility area is searched.
Further preferably, the step S106 further includes the following steps:
and S600, if at least one sperm activity area is found, taking the sperm activity area with the largest total number of sperm activity events as the most active sperm area, and identifying the most active sperm area in the corresponding two-dimensional view.
In a detailed optimization, in each two-dimensional view, all of the spermatozoa events in the most active regions of spermatozoa are identified in a first color, all of the spermatozoa events in other active regions of spermatozoa are identified in a second color, and all of the remaining spermatozoa events are identified in a third color.
In a detailed optimization, the total number of the sperm motility events displayed in each two-dimensional view and further in the corresponding most active sperm region are output by a line graph.
Further preferably, the clustering algorithm is a Mean Shift algorithm, and includes the following steps:
s601, setting one coordinate as a starting point in a two-dimensional view;
s602, determining a preset area by taking the starting point as a center, and counting the deviation mean value of all sperm activity event coordinates in the preset area relative to the starting point;
s603, moving the starting point to a coordinate position corresponding to the deviation mean value, and then returning to execute the step S602 until the distribution density function of the sperm motility event in the preset area reaches the maximum value;
and S604, if the total number of the sperm motility events in the last preset area exceeds a preset sperm motility threshold value, taking the last preset area as a sperm motility area.
In detail, the steps S601 to S604 are performed for each coordinate in the two-dimensional view, and then only the spermatozoa active region with a large total number of spermatozoa active events is continuously used as the spermatozoa active region for the two spermatozoa active regions with partially overlapped regions.
Optimally, in step S102, denoising each time slice in the sperm motility event record file is performed according to the following steps:
s201, reading a row event field in a time slice to obtain a sperm activity event row address
And activation time point of sperm motility event
；
S202, reading a column event field in a time slice to acquire a sperm activity event row address
；
S203, if the sperm activity event column address x of the current time slice is not equal to the sperm activity event column address xprev of the previous time slice and/or the sperm activity event row address y of the current time slice is not equal to the sperm activity event row address yprev of the previous time slice, searching whether the coordinates of the sperm activity events of other time slices fall in a noise point detection interval [ x-1-x +1, y-1-y +1] in a time period t-delta tau-t + delta tau, wherein delta tau is a preset time threshold value;
and S204, if the data do not exist, taking the current time slice as noise, and removing the data from the sperm motility event record file.
Optimally, the duration of the first target time period is between 3 and 10 seconds.
Optimally, the duration of the second target time period is between 0.015 and 0.025 seconds.
The invention has the beneficial effects that:
(1) the invention has created and provided a DVS data processing method based on collection data realization of DVS camera and is favorable to carrying on the analysis of sperm activity, namely after obtaining the record file of sperm activity incident that is got to record the fresh semen under the microscope through DVS camera, read activation position information and activation time information of all sperm activity incident from the record file of the sperm activity incident, and carry on the local output display to the read information through three-dimensional view and two-dimensional view, make medical staff or researcher can feel the sperm activity situation directly, facilitate people to carry on the qualitative and quantitative analysis of sperm activity according to the aforesaid multidimensional view greatly, help to get the analysis result of sperm activity fast and accurately, promote the long-standing development in the medical field of reproduction;
(2) the DVS data processing method also has the advantages of detailed output display data, automatic denoising, easy realization and the like, and is convenient for practical popularization and application.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a DVS data processing method according to the present invention.
Fig. 2 is a schematic data structure diagram of a file for recording sperm motility events provided by the present invention.
Fig. 3 is a first exemplary illustration of a three-dimensional view provided by the present invention.
Fig. 4 is a second exemplary illustration of a three-dimensional view provided by the present invention.
Fig. 5 is a first exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 6 is a second exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 7 is a third exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 8 is a fourth exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 9 is a fifth exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 10 is a sixth exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 11 is a seventh exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 12 is an eighth exemplary illustration of a two-dimensional view provided by the present invention.
Fig. 13 is an exemplary illustration of a line graph provided by the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, B exists alone, and A and B exist at the same time, and the term "/and" is used herein to describe another association object relationship, which means that two relationships may exist, for example, A/and B, may mean: a alone, and both a and B alone, and further, the character "/" in this document generally means that the former and latter associated objects are in an "or" relationship.
Example one
As shown in fig. 1 to 13, the DVS data processing method for facilitating sperm motility analysis provided in this embodiment includes the following steps.
S101, importing a sperm motility event recording file obtained by recording fresh semen under a microscope through a DVS camera.
In step S101, the DVS camera may be, but not limited to, a cell series camera from Hillhouse company, and after the DVS camera is connected to a computer, by running a recording software (the recording software provides three data display modes: Full picture, which displays a real scene seen by the camera, but the picture is reconstructed from an Event data stream, Event only, which directly displays the camera original stream data, that is, only the scene change appears, the brightness is calculated and normalized by the count/number of the change occurrence, the more the change is in a short time, the brighter the pixel is, Event marker Full picture, which displays the whole scene and the change, the green mark is the original data actually generated at present, and the Full picture background is the data generated before), and the sperm motility event record file can be obtained by exporting.
In addition, the whole recording process before the step S101 may be, but is not limited to, as follows: (1) taking out the tail part of the epididymis of an adult male mouse after the adult male mouse is killed at the neck-broken part, damaging the surface of the mouse epididymis, and sucking a small amount of seminal fluid to place the seminal fluid in a surface dish; (2) opening the DVS camera to aim at the eyepiece of the microscope, and then placing the watch glass under the microscope for dynamic event recording. The ambient temperature may be maintained at around 25 degrees celsius throughout the recording process.
S102, aiming at each time slice in the sperm activity event record file, reading sperm activity event column addresses recorded in the time slices
Sperm motility event row address
And activation time point of sperm motility event
。
In the step S102, the book is readAs known from the development documents of the DVS camera, the recording file is obtained by dividing a time axis into a plurality of time slices and recording the spermatozoa activity events one by one. As shown in FIG. 2, each time slice contains three events to represent a pixel change (i.e., a spermatozoa activity event) in the shot capture: a row event-english name row event, corresponding to the row event field in fig. 2, which records the spermatozoa motility event row address and the point in time of activation of the spermatozoa motility event with 4 bytes; column event-english name column event, corresponding to the column event field in fig. 2, which records pixel brightness information of the spermatozoa motility event column address and the activated position of the spermatozoa motility event with 4 bytes; the special event-the english name special event-corresponds to the special event field in fig. 2, which indicates the end of the current time slice with 4 bytes. In addition, as shown in fig. 2, before the line event field, the duration of the current time slice is also recorded with a time slice duration field having a length of 6 bytes. The addresses of the columns of spermatozoa motility events recorded in the respective time slices can thus be read by means of the existing conventional identification codes
Sperm motility event row address
And activation time point of sperm motility event
。
Considering the inevitable interference noise present during the original recording process, it is therefore necessary to denoise each time slice in the sperm motility event log file prior to reading. I.e. optimized, the method of denoising may include, but is not limited to, the steps of: s201, reading a row event field in a time slice to obtain a sperm activity event row address
And activation time point of sperm motility event
(ii) a S202, reading a column event field in a time slice to acquire a sperm activity event row address
(ii) a S203, if the sperm activity event column address x of the current time slice is not equal to the sperm activity event column address xprev of the previous time slice and/or the sperm activity event row address y of the current time slice is not equal to the sperm activity event row address yprev of the previous time slice, whether the sperm activity event coordinates of other time slices fall in a noise point detection interval [ x-1-x +1, y-1-y +1] is searched in the time period t-delta tau-t + delta tau]Wherein, Δ τ is a preset time threshold; and S204, if the data do not exist, taking the current time slice as noise, and removing the data from the sperm motility event record file. The time threshold may be manually set in advance or default to an integral multiple of the duration of the current time slice.
S103, addressing all sperm motility events
Sperm motility event row address
And activation time point of sperm motility event
Sequentially written into the memory in time sequence
In a data matrix of individual elements, wherein,
is a natural number not less than 1 ten thousand.
S104, outputting all sperm activity event columns which are displayed in the data matrix and are continuously recorded in a first target time period through a three-dimensional viewAddress
Sperm motility event row address
And activation time point of sperm motility event
Wherein, the three-dimensional coordinate axes in the three-dimensional view are respectively the addresses of the corresponding sperm motility events
Axis of abscissas
Corresponding sperm motility event row address
Axis of ordinates
And corresponding activation time point of sperm motility event
Time axis of
。
In the step S104, the first target time period may be a manually preset time period or a default time period, for example, as shown in fig. 3, specifically, a time period of 7 th to 8 th seconds, or as shown in fig. 4, a time period of 37 th to 42 th seconds. As shown in fig. 3 and 4, by outputting these three-dimensional views, people can easily observe that the sperm keeps a certain activity in the corresponding first target time period, and keeps moving in a small range in each place, and is distributed in a cluster manner, and meanwhile, the migration condition of the sperm cluster can be observed by rotating the three-dimensional views, thereby facilitating medical staff or researchers to rapidly perform qualitative analysis of sperm activity. In addition, the duration of the first target time period is preferably between 3 and 10 seconds; of course, a plurality of three-dimensional views corresponding to different first target time periods may be output for performing a fragmented qualitative analysis of the full recording period.
S105, outputting all sperm activity event column addresses which are displayed in the data matrix and are continuously recorded in a second target time period through a two-dimensional view
And sperm motility event row address
Wherein, the two-dimensional coordinate axes in the two-dimensional view are respectively the addresses of the corresponding sperm motility event columns
Axis of abscissas
And corresponding sperm motility event row address
Axis of ordinates
The number of the two-dimensional views is multiple, and the two-dimensional views correspond to multiple second target time periods which are equal in time length and are at different times in a one-to-one mode.
In the step S105, the second target time period may be a manually preset time period or a default time period, for example, as shown in fig. 5, specifically, a partial time period in the 5 th second; as shown in fig. 6, is a partial time period in the 10 th second; as shown in fig. 7, specifically, a partial time period in the 15 th second; as shown in fig. 8, a partial time period in the 20 th second; as shown in fig. 9, specifically, a partial period in the 25 th second; as shown in fig. 10, a partial time period in the 30 th second; as shown in fig. 11, specifically, a partial time period in 35 th second; as shown in fig. 12, is a partial time period in the 40 th second. As shown in fig. 5 to 12, by outputting the two-dimensional views, people can easily observe the movement state and distribution condition of the sperm, and further medical staff or researchers can rapidly perform quantitative analysis on sperm activity. In addition, the time duration of the second target time period is preferably between 0.015 and 0.025 seconds, so that sufficient samples of the sperm motility events can be obtained in each two-dimensional view, and quantitative analysis is convenient.
Therefore, through the detailed description of the steps S101 to S105, a DVS data processing method which is based on the collected data of the DVS camera and is beneficial to the sperm activity analysis can be provided, namely after a sperm activity event record file obtained by recording fresh semen under a microscope through the DVS camera is obtained, the activation position information and the activation time information of all sperm activity events are read from the sperm activity event record file, and the read information is locally output and displayed through a three-dimensional view and a two-dimensional view, so that medical staff or researchers can intuitively feel the sperm activity condition, the qualitative and quantitative analysis of the sperm activity can be greatly facilitated according to the multi-dimensional view, the sperm activity analysis result can be rapidly and accurately obtained, and the long-term development of the reproductive medicine field can be promoted.
Preferably, after the step S105, the following steps are further included: s106, searching the sperm motility area in the two-dimensional view by applying a clustering algorithm, and identifying the sperm motility area in the corresponding two-dimensional view after the sperm motility area is searched.
In the step S106, the clustering algorithm may be, but is not limited to, a Mean Shift algorithm, and specifically may include the following steps: s601, setting one coordinate as a starting point in a two-dimensional view; s602, determining a preset area by taking the starting point as a center, and counting the deviation mean value of all sperm activity event coordinates in the preset area relative to the starting point; s603, moving the starting point to a coordinate position corresponding to the deviation mean value, and then returning to execute the step S602 until the distribution density function of the sperm motility event in the preset area reaches the maximum value; and S604, if the total number of the sperm motility events in the last preset area exceeds a preset sperm motility threshold value, taking the last preset area as a sperm motility area. Wherein, the sperm motility threshold value can be manually set in advance or default to 80. Therefore, through the steps S106 and S601 to S604, people can further observe the sperm motility center, and know the sperm motility condition in detail and in a short term.
In order to ensure the accuracy and avoid the overlapping condition of the sperm motility areas, the steps S601 to S604 are respectively executed for each coordinate in the two-dimensional view, and then only the sperm motility area with more total number of the sperm motility events is continuously used as the sperm motility area for the two sperm motility areas with the overlapped partial areas, namely the sperm motility area with less total number of the sperm motility events is cancelled as the sperm motility area.
In step S106, the method further includes the following steps: and S600, if at least one sperm activity area is found, taking the sperm activity area with the largest total number of sperm activity events as the most active sperm area, and identifying the most active sperm area in the corresponding two-dimensional view. Therefore, the step S600 is further beneficial to people to quickly observe the most active sperm center. Furthermore, to facilitate the identification of different regions of spermatozoa events, it is optimized in detail that in each two-dimensional view all spermatozoa events in the most active regions of spermatozoa are identified in a first color, all spermatozoa events in other spermatozoa regions are identified in a second color, and all remaining spermatozoa events are identified in a third color. Wherein the first, second and third colors are different colors, such as red, yellow and gray, respectively.
In a detailed optimization, the total number of spermatozoa motile events displayed in each two-dimensional view and further in the corresponding most motile zone of spermatozoa is output by a line graph. As shown in fig. 13, the content of the output display data can be enriched, so that people can observe the sperm motility more accurately.
In summary, the DVS data processing method for analyzing sperm motility provided by this embodiment has the following technical effects:
(1) the embodiment provides a DVS data processing method which is beneficial to sperm activity analysis based on the collected data of a DVS camera, namely, after acquiring a sperm activity event record file obtained by recording fresh semen under a microscope through the DVS camera, reading activation position information and activation time information of all sperm activity events from the sperm activity event record file, and locally outputting and displaying the read information through a three-dimensional view and a two-dimensional view, so that medical staff or researchers can intuitively feel the sperm activity condition, thereby greatly facilitating the qualitative and quantitative analysis of sperm activity by people according to the multi-dimensional view, being beneficial to quickly and accurately obtaining sperm activity analysis results and promoting the full development of the reproductive medicine field;
(2) the DVS data processing method also has the advantages of detailed output display data, automatic denoising, easy realization and the like, and is convenient for practical popularization and application.
The present invention is not limited to the above-described alternative embodiments, and various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. A DVS data processing method for facilitating sperm motility analysis, comprising the steps of:
s101, importing a sperm motility event recording file obtained by recording fresh semen under a microscope through a DVS (digital video system) camera;
s102, aiming at each time slice in the sperm activity event record file, reading sperm activity event column addresses recorded in the time slices
Sperm motility event row address
And activation time point of sperm motility event
；
S103, addressing all sperm motility events
Sperm motility event row address
And activation time point of sperm motility event
Sequentially written into the memory in time sequence
In a data matrix of individual elements, wherein,
is a natural number not less than 1 ten thousand;
s104, outputting all sperm activity event column addresses which are displayed in the data matrix and are continuously recorded in a first target time period through a three-dimensional view
Sperm motility event row address
And activation time point of sperm motility event
Wherein, the three-dimensional coordinate axes in the three-dimensional view are respectively the addresses of the corresponding sperm motility events
Axis of abscissas
Corresponding sperm motility event row address
Axis of ordinates
And corresponding activation time point of sperm motility event
Time axis of
；
S105, outputting all sperm activity event column addresses which are displayed in the data matrix and are continuously recorded in a second target time period through a two-dimensional view
And sperm motility event row address
Wherein, the two-dimensional coordinate axes in the two-dimensional view are respectively the addresses of the corresponding sperm motility event columns
Axis of abscissas
And corresponding sperm motility event row address
Axis of ordinates
The number of the two-dimensional views is multiple, and the two-dimensional views correspond to multiple second target time periods which are equal in time length and are at different times in a one-to-one mode.
2. A DVS data processing method for facilitating sperm motility analysis as described in claim 1, further comprising the steps of, after said step S105:
s106, searching the sperm motility area in the two-dimensional view by applying a clustering algorithm, and identifying the sperm motility area in the corresponding two-dimensional view after the sperm motility area is searched.
3. A DVS data processing method for facilitating sperm motility analysis as described in claim 2, further comprising the step of, in said step S106:
and S600, if at least one sperm activity area is found, taking the sperm activity area with the largest total number of sperm activity events as the most active sperm area, and identifying the most active sperm area in the corresponding two-dimensional view.
4. A DVS data processing method for facilitating sperm motility analysis as described in claim 3, wherein: in each two-dimensional view, all of the spermatozoa motile events in the most motile regions of sperm are identified in a first color, all of the spermatozoa motile events in the other spermatozoa regions are identified in a second color, and all of the remaining spermatozoa events are identified in a third color.
5. A DVS data processing method for facilitating sperm motility analysis as described in claim 3, wherein: and outputting the total number of the sperm motility events displayed in each two-dimensional view and the total number of the sperm motility events in the most sperm active area of each two-dimensional view through a line graph.
6. A DVS data processing method for facilitating sperm motility analysis as described in claim 2 wherein said clustering algorithm is a Mean Shift algorithm comprising the steps of:
s601, setting one coordinate as a starting point in a two-dimensional view;
s602, determining a preset area by taking the starting point as a center, and counting the deviation mean value of all sperm activity event coordinates in the preset area relative to the starting point;
s603, moving the starting point to a coordinate position corresponding to the deviation mean value, and then returning to execute the step S602 until the distribution density function of the sperm motility event in the preset area reaches the maximum value;
and S604, if the total number of the sperm motility events in the last preset area exceeds a preset sperm motility threshold value, taking the last preset area as a sperm motility area.
7. A DVS data processing method for facilitating sperm motility analysis as described in claim 6, wherein: and (4) respectively executing the steps S601 to S604 aiming at each coordinate in the two-dimensional view, and then only taking the sperm activity area with more sperm activity events as the sperm activity area aiming at the two sperm activity areas with partially overlapped areas.
8. A method of DVS data processing to facilitate sperm motility analysis as described in claim 1, wherein at said step S102, each time slice in said sperm motility event log file is denoised by:
s201, reading a row event field in a time slice, and acquiring a sperm activity event column address x and a sperm activity event activation time point t;
s202, reading a column event field in a time slice to obtain a sperm activity event row address y;
s203, if the sperm activity event column address x of the current time slice is not equal to the sperm activity event column address xprev of the previous time slice and/or the sperm activity event row address y of the current time slice is not equal to the sperm activity event row address yprev of the previous time slice, searching whether the coordinates of the sperm activity events of other time slices fall in a noise point detection interval [ x-1-x +1, y-1-y +1] in a time period t-delta tau-t + delta tau, wherein delta tau is a preset time threshold value;
and S204, if the data do not exist, taking the current time slice as noise, and removing the data from the sperm motility event record file.
9. A DVS data processing method for facilitating sperm motility analysis as described in claim 1, wherein: the duration of the first target time period is between 3 and 10 seconds.
10. A DVS data processing method for facilitating sperm motility analysis as described in claim 1, wherein: the duration of the second target time period is between 0.015 and 0.025 seconds.
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