CN104794710A - Image processing method and device - Google Patents
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Abstract
The invention provides an image processing method and device. The method includes: acquiring a target image including algal cells; binarizing the target image to obtain a target gray image; extracting connected areas in the target gray image, and calculating area of each connected area; dividing preset algal area by the area of each connected area to obtain a quotient of each connected area; using a sum of the quotients of the connected areas, as the amount of the algal cells included in the target image. The algal cells intersecting together are not subjected to image splitting, thus, the number of the algal cells can be accurately counted, and accuracy of counting the algal cells is improved.
Description
Technical field
The application relates to image processing field, particularly relates to a kind of image processing method and device.
Background technology
In the algae analysis and research of biological technical field, technician initiatively can cultivate alga cells.In order to the breeding situation of clear and definite alga cells, need statistics alga cells number.At present, adopt industrial camera to replace conventional microscope eyepiece, the sample solution of alga cells to be amplified, imaging, then utilize image procossing mode, calculate the alga cells quantity in imaging region.
In prior art, the method for image procossing and counting is: to the region including alga cells in imaging region, carries out Iamge Segmentation and generates multiple cut zone, then add up the quantity of cut zone, as the quantity of alga cells.But alga cells is generally longer, in incubation, there will be the situation that multiple alga cells intersects, cause accurately to split the alga cells intersected, and then cause cannot the quantity of accurate statistics alga cells.
So, need now a kind of mode to carry out the quantity of accurate statistics alga cells, to improve the accuracy rate of statistics alga cells quantity.
Summary of the invention
This application provides a kind of image processing method and device, so that accurate statistics alga cells quantity, to improve the accuracy rate of statistics alga cells quantity.
To achieve these goals, this application provides following technological means:
A kind of image processing method, comprising:
Obtain the target image including alga cells;
Binary conversion treatment is carried out to described target image, obtains target gray image;
Extract the connected region in described target gray image, and calculate the area of each connected region;
The area of described each connected region and default algae area are divided by, obtain the quotient of each connected region;
By the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
Preferably, described binary conversion treatment is carried out to described target image before, also comprise:
Utilize dynamic thresholding method that described target image is divided into multiple cut zone;
Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone.
Preferably, described binary conversion treatment is carried out to described target image, obtains target gray image and comprise:
Utilize the binary-state threshold of each cut zone self, to this cut zone binary conversion treatment, and obtain the gray level image of each cut zone;
The gray level image of all cut zone is formed described target gray image.
Preferably, described binary conversion treatment is carried out to described target image before, also comprise:
To background luminance uneven in described target image, carry out gamma correction.
Preferably, described binary conversion treatment is carried out to described target image, obtains target gray image and comprise:
Utilize default global threshold to carry out binary conversion treatment to described target image, obtain the described target gray image after binary conversion treatment.
Preferably, also comprise:
Calculate the Morphologic Parameters of each connected region.
A kind of image processing apparatus, comprising:
Acquiring unit, for obtaining the target image including alga cells;
Processing unit, for carrying out binary conversion treatment to described target image, obtains target gray image;
Extraction unit, for extracting the connected region in described target gray image, and calculates the area of each connected region;
Computing unit, for the area of described each connected region and default algae area being divided by, obtains the quotient of each connected region; By the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
Preferably, also comprise:
Definite threshold unit, is divided into multiple cut zone for utilizing dynamic thresholding method by described target image; Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone.
Then described processing unit, specifically for utilizing the binary-state threshold of each cut zone self, to this cut zone binary conversion treatment, and obtains the gray level image of each cut zone; The gray level image of all cut zone is formed described target gray image.
Preferably, also comprise:
Brightness correction unit, for background luminance uneven in described target image, carries out gamma correction;
Then described processing unit, specifically for utilizing default global threshold to carry out binary conversion treatment to described target image, obtains the described target gray image after binary conversion treatment.
Preferably, also comprise:
Calculating parameter unit, for calculating the Morphologic Parameters of each connected region.
In the embodiment of the present application, multiple algae intersected forms a connected region, and each algae all has area, and the area of multiple alga cells intersected, is substantially equal to the area of connected region.So determined the area of an alga cells in advance by great many of experiments, be set to default algae area.For each connected region in target image, by the area of connected region divided by default algae area, the algae number comprised in each connected region just can be obtained.Algae number in all connected regions is added and, the algae number comprised in target image can be obtained.Do not relate in the application and Iamge Segmentation is carried out to multiple alga cells intersected, so the application can accurate statistics alga cells quantity, to improve the accuracy rate of statistics alga cells quantity.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The process flow diagram of Fig. 1 a kind of image processing method disclosed in the embodiment of the present application;
Fig. 2 is the process flow diagram of the embodiment of the present application another image processing method disclosed;
Fig. 3 is the process flow diagram of the embodiment of the present application another image processing method disclosed;
Fig. 4 is the process flow diagram of the embodiment of the present application another image processing method disclosed;
Fig. 5 is the process flow diagram of the embodiment of the present application another image processing method disclosed;
Fig. 6 is the process flow diagram of the embodiment of the present application another image processing method disclosed;
The diagram of connected region in Fig. 7 a kind of image processing method disclosed in the embodiment of the present application;
Fig. 8 is a kind of image processing apparatus structural representation disclosed in the embodiment of the present application;
Fig. 9 is the embodiment of the present application another image processing apparatus structural representation disclosed;
Figure 10 is the embodiment of the present application another image processing apparatus structural representation disclosed.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present application, be clearly and completely described the technical scheme in the embodiment of the present application, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.
Introducing before the application specifically implements, need to perform some and prepare work in advance: generally, in alga cells solution, the concentration of alga cells is higher, in order to careful alga cells under the microscope, needs alga cells solution dilution certain multiple.Then, the alga cells solution of certain volume after dilution certain multiple is positioned on microscopical object lens, adopts that industrial camera amplifies alga cells solution, imaging, obtain the image including alga cells.
The alga cells image taken by industrial camera, as target image, utilizes computer program to process target image, introduces the concrete implementation of computer program below.
As shown in Figure 1, this application provides a kind of image processing method, comprise step S101 ~ S106:
Step S101: obtain the target image including alga cells;
Obtain the target image including alga cells of industrial camera shooting, owing to there being some noise in target image, to the smoothing process of target image, interference can be caused to eliminate.
Before performing step S102 to target image, need the threshold value determining target image to be performed to binary conversion treatment, the embodiment of the present application provides the mode that two kinds are determined binary-state threshold, introduces two kinds of modes below one by one:
First kind of way: utilize dynamic thresholding method that described target image is divided into multiple cut zone; Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone.
Due to industrial camera shooting problem, cause the brightness in target image centre position partially bright, marginal portion brightness is partially dark, so adopt dynamic thresholding method in the application, target image is divided into multiple cut zone.So that each cut zone has the binary-state threshold of self, to eliminate the problem of the brightness irregularities of industrial camera shooting.
First, introduce and utilize dynamic thresholding method that described target image is divided into the detailed process of multiple cut zone, as shown in Figure 2, comprise step S201 ~ S205:
Step S201: determine a certain size a search window in the target image;
Adopt the mode of variable step in the application to determine a cut zone, its object is to, multiple alga cells intersected is segmented in a cut zone.In order to achieve this end, first determine the search window of an initial size, and in the target image with the region that the search window of initial size is determined, as prime area.
Step S202: the first average gray value calculating all pixels in described search window, the second average gray value of all pixels in the local neighborhood calculating described search window;
Calculate the average gray value of all pixels in search window as the first average gray value.Increase the step-length of described search window, using region corresponding for the step-length of increase as local neighborhood, calculate the average gray of all pixels in local neighborhood, as the second average gray.
Step S203: judge that the difference of described first average gray value and described second average gray value is whether in preset range; If so, then enter step S204, otherwise enter step S205.
Alga cells is the foreground image in target image, and all the other are background image, and the gray-scale value of foreground image is different from the gray-scale value of background image.
When all there is alga cells in search window and local neighborhood, then in both, the gray-scale value of pixel is more or less the same, and namely the difference of the first average gray value and the second average gray value should in preset range.
When there is alga cells in search window, when there is not alga cells in local neighborhood, then the gray-scale value difference in both is comparatively large, and namely the difference of the first average gray value and the second average gray value is not in preset range.
Step S204: described local neighborhood is added in described search window, reenters step S201.
When the difference of described first average gray value and described second average gray value is in preset range, illustrate in local domain to there is alga cells equally, so add in search window by described local neighborhood, expand the scope of search window.
Then, reenter step S201, continue to judge whether comprise alga cells in local neighborhood, with as much as possible, multiple alga cells intersected is divided in a cut zone.
Step S205: using up-to-date search window as a cut zone.
When the difference of described first average gray value and the second average gray value exceeds described preset range, then illustrate and now multiple alga cells intersected is divided in a cut zone.Now, using up-to-date search window as a cut zone.
After a cut zone is divided to one group of multiple alga cells intersected, continues to perform segmentation step to target image rest of pixels point by the mode of Fig. 2, until the segmentation of described target image is complete, and obtain multiple cut zone.
The above-mentioned concrete steps for determining cut zone in target area, after determining multiple cut zone, need to determine each cut zone binary-state threshold further.Introduce the concrete implementation determining binary-state threshold in a cut zone below, the implementation in all the other cut zone is consistent therewith, repeats no longer one by one.
As shown in Figure 3, be the process utilizing method between maximum kind to determine a cut zone binary-state threshold, specifically comprise step S301 ~ S304:
Step S301: determine the binary-state threshold that this cut zone is initial, utilizes described initial binary-state threshold that described cut zone is divided into foreground image and background image;
Step S302: determine described foreground image and described background image proportion in described cut zone.
Step S303: utilize formula u=w0*u0+w1*u1 to calculate the overall average gray scale of described segmentation image.Wherein, u is overall average gray scale, and w0 is the pixel proportion of foreground image, and u0 is the average gray of foreground pixel point; W1 is background image pixels point proportion, the average gray of u1 background pixel point.
Step S304: utilize formula g=w0* (u0-u) * (u0-u)+w1* (u1-u) * (u1-u) to calculate the inter-class variance of foreground image and background image.
Step S305: amendment initial binary threshold value, to make the inter-class variance of foreground image and background image maximum.
The inter-class variance of foreground image and background image is maximum, and representing this binary-state threshold can farthest by foreground image and background image segmentation.Namely binary-state threshold corresponding when inter-class variance is maximum, for splitting the optimal threshold of this cut zone.The binary-state threshold of each cut zone is determined by the mode described in Fig. 3.
First kind of way is carried out the process of definite threshold by the mode of dynamic threshold segmentation, introduces the second way below.
The second way: the binary-state threshold utilizing each cut zone self, to this cut zone binary conversion treatment, and obtains the gray level image of each cut zone; The gray level image of all cut zone is formed described target gray image.
Due to the brightness irregularities in target image, so the mode of global threshold cannot be used to come Image Segmentation Using.In order to the mode of global threshold can be used to split target image, need the uneven brightness of all pixels in correction target image.
Before correction, the relation built in advance between pixel and intensity correction values is needed.As shown in Figure 4, the process of gamma correction is carried out in lower mask body introduction to target image.
Step S401: in the corresponding relation of pixel and intensity correction values, search the intensity correction values of each pixel in described target image;
For each pixel in target image, in the corresponding relation of the pixel built in advance and intensity correction values, according to identifier lookup and the pixel corresponding brightness corrected value of pixel.
Step S402: by the brightness value of each sampled pixel point, and, the sample correction brightness value corresponding with each sampled pixel point and be worth, again as the brightness value of each sampled pixel point.
After the intensity correction values obtaining each pixel, for pixel each in target image: by the brightness value of pixel, and the intensity correction values corresponding with this pixel is sued for peace, using with value again as the brightness value of this pixel.
After each pixel is all carried out gamma correction, just can eliminate the uneven impact on alga cells in target gray image of bias light.Then the default global threshold preset can be searched from database, as the threshold value split target image.
Then return Fig. 1, enter step S102: binary conversion treatment is carried out to described target image, obtain target gray image.
For the ease of computer disposal, target image is carried out binary conversion treatment, generate target gray image, target image to be changed into the language that computing machine can identify.
For kind of the processing mode of two in step S101, step S102 also has two kinds of processing modes, is described one by one below to two kinds of processing modes of step S102:
First kind of way: the binary-state threshold utilizing each cut zone self, to this cut zone binary conversion treatment, and obtains the gray level image of each cut zone; The gray level image of all cut zone is formed described target gray image.
The manner is corresponding with the first kind of way of step S101, owing to setting a binary-state threshold for each cut zone in step S101, so also according to the binary-state threshold of each cut zone self in the manner, carry out binary conversion treatment, and obtain the gray level image of each connected region.Its object is to eliminate uneven brightness in target image.After the gray level image obtaining each cut zone, by the gray level image of each cut zone composition target gray image.
The second way: utilize default global threshold to carry out binary conversion treatment to described target image, obtain the described target gray image after binary conversion treatment.
The manner is corresponding with the second way in step S101, so obtain the default global threshold prestored in a database, and utilizes default global threshold to carry out binary conversion treatment to target image, and then obtains the target gray image after process.
Then return Fig. 1, enter step S103: extract the connected region in described target gray image, and calculate the area of each connected region.
After acquisition target gray image, just there are foreground image (alga cells) and background image, for foreground image, extract the pixel be connected together one by one and form connected region, the alga cells that namely connected region intersects.Then add up the number of pixel in each connected region, then be multiplied by the area of a pixel, and then obtain the area of each connected region.
Step S104: the area of described each connected region and default algae area are divided by, obtain the quotient of each connected region;
Determine the area of an alga cells in advance through great many of experiments, and be set to default algae area.By the area of each connected region, divided by the area of an alga cells, obtain the quotient of each connected region, quotient is the alga cells quantity comprised in each connected region.The alga cells quantity comprised in each connected region is calculated by this step.
Certainly, the quotient calculated may not be integer, so adopt the mode rounded, determines the alga cells quantity that connected region comprises.Such as, the quotient in a connected region is 4.4 and determines to comprise 5 alga cells in this connected region.
Step S105: by the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
The quotient of each connected region is the amount of algae of each connected region, by the quotient of each connected region and value, as the quantity of the alga cells comprised in target image.
In the embodiment of the present application, multiple algae intersected forms a connected region, and each algae all has area, and the area of multiple alga cells intersected, is substantially equal to the area of connected region.So determined the area of an alga cells in advance by great many of experiments, be set to default algae area.For each connected region in target image, by the area of connected region divided by default algae area, the algae number comprised in each connected region just can be obtained.Algae number in all connected regions is added and, the algae number comprised in target image can be obtained.Do not relate in the application and Iamge Segmentation is carried out to multiple alga cells intersected, so the application can accurate statistics alga cells quantity, to improve the accuracy rate of statistics alga cells quantity.
Introduce the applicable cases after counting alga cells quantity below:
The first applicable cases: calculate alga cells concentration, as shown in Figure 5, comprise step S501 ~ S502:
Step S501: obtain the dilution ratio of alga cells, the liquor capacity of alga cells;
Determine in advance to the dilution ratio of alga cells solution, and carry out the liquor capacity of the alga cells measured under being positioned over industrial camera.
Step S502: pass through formula
calculate the concentration of described alga cells.
The alga cells quantity utilizing the method for Fig. 1 to calculate, divided by liquor capacity, obtains quotient, obtains the algae concentration in this alga cells solution.Because this alga cells solution is through dilution, so quotient is multiplied by dilution ratio, obtain the concentration of primitive algae cell solution.
The concentration of alga cells solution has significant role in commercial Application.
The second applicable cases: calculate alga cells motility rate, as shown in Figure 6, comprise step S601 ~ S603:
Step S601: judge whether the mean flow rate of each connected region is greater than the second predetermined threshold value; If so, then enter step S602, otherwise do not process.
Due to alga cells self character, the brightness of active alga cells is brighter, and the brightness of nonactive alga cells is darker.So setting the second predetermined threshold value.
Judge whether the mean flow rate of connected region is greater than the second predetermined threshold value, if be greater than the second predetermined threshold value, then represent in this connected region to be active algae, otherwise represent in this connected region to be nonactive alga cells.
Step S602: quantity summation mean flow rate being greater than the connected region of described second predetermined threshold value, as the alga cells quantity alive comprised in described target image;
Then add up in target image, mean flow rate is greater than the quantity summation of the connected region of the second predetermined threshold value, using quantity summation as alga cells quantity of living.
Step S603: by the ratio of described alga cells quantity alive and described alga cells quantity, as the motility rate of alga cells in this time period.
By the alga cells quantity alive obtained in step S603, with, the ratio of the alga cells quantity that step S105 obtains, as the motility rate of alga cells.All the other ratios are the inactive ratio of alga cells.
The activity of alga cells has significant role in commercial Application.
3rd applicable cases: computation of morphology parameter.
Calculate the parameters such as the girth of each connected region, major axis, minor axis, using as subsequent treatment foundation.
As shown in Figure 7, black region is a connected region, and the profile length of black region is the girth of connected region, a major axis longer in two line segments of right-angled intersection, a shorter minor axis.
Corresponding with the image processing method embodiment that Fig. 1 provides, as shown in Figure 8, present invention also provides a kind of image processing apparatus, comprising:
Acquiring unit 81, for obtaining the target image including alga cells;
Processing unit 82, for carrying out binary conversion treatment to described target image, obtains target gray image;
Extraction unit 83, for extracting the connected region in described target gray image, and calculates the area of each connected region;
Computing unit 84, for the area of described each connected region and default algae area being divided by, obtains the quotient of each connected region; By the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
As shown in Figure 9, described image processing apparatus, also comprises:
Definite threshold unit 85, is divided into multiple cut zone for utilizing dynamic thresholding method by described target image; Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone.
Then described processing unit 82, specifically for utilizing the binary-state threshold of each cut zone self, to this cut zone binary conversion treatment, and obtains the gray level image of each cut zone; The gray level image of all cut zone is formed described target gray image.
As shown in Figure 10, described image processing apparatus, also comprises:
Brightness correction unit 86, for background luminance uneven in described target image, carries out gamma correction;
Then described processing unit 82, specifically for utilizing default global threshold to carry out binary conversion treatment to described target image, obtains the described target gray image after binary conversion treatment.
As shown in fig. 9 or 10, described image processing apparatus also comprises:
Calculating parameter unit 87, for calculating the Morphologic Parameters of each connected region.
If the function described in the present embodiment method using the form of SFU software functional unit realize and as independently production marketing or use time, can be stored in a computing equipment read/write memory medium.Based on such understanding, the part of the part that the embodiment of the present application contributes to prior art or this technical scheme can embody with the form of software product, this software product is stored in a storage medium, comprising some instructions in order to make a computing equipment (can be personal computer, server, mobile computing device or the network equipment etc.) perform all or part of step of method described in each embodiment of the application.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. various can be program code stored medium.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiment, between each embodiment same or similar part mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the application.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein when not departing from the spirit or scope of the application, can realize in other embodiments.Therefore, the application can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (10)
1. an image processing method, is characterized in that, comprising:
Obtain the target image including alga cells;
Binary conversion treatment is carried out to described target image, obtains target gray image;
Extract the connected region in described target gray image, and calculate the area of each connected region;
The area of described each connected region and default algae area are divided by, obtain the quotient of each connected region;
By the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
2. the method for claim 1, is characterized in that, described binary conversion treatment is carried out to described target image before, also comprise:
Utilize dynamic thresholding method that described target image is divided into multiple cut zone;
Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone.
3. method as claimed in claim 2, is characterized in that, describedly carries out binary conversion treatment to described target image, obtains target gray image and comprises:
Utilize the binary-state threshold of each cut zone self, to this cut zone binary conversion treatment, and obtain the gray level image of each cut zone;
The gray level image of all cut zone is formed described target gray image.
4. the method for claim 1, is characterized in that, described binary conversion treatment is carried out to described target image before, also comprise:
To background luminance uneven in described target image, carry out gamma correction.
5. method as claimed in claim 4, is characterized in that, describedly carries out binary conversion treatment to described target image, obtains target gray image and comprises:
Utilize default global threshold to carry out binary conversion treatment to described target image, obtain the described target gray image after binary conversion treatment.
6. the method as described in claim 1-5, is characterized in that, also comprises:
Calculate the Morphologic Parameters of each connected region.
7. an image processing apparatus, is characterized in that, comprising:
Acquiring unit, for obtaining the target image including alga cells;
Processing unit, for carrying out binary conversion treatment to described target image, obtains target gray image;
Extraction unit, for extracting the connected region in described target gray image, and calculates the area of each connected region;
Computing unit, for the area of described each connected region and default algae area being divided by, obtains the quotient of each connected region; By the quotient of each connected region and value, as in described target image comprise the quantity of alga cells.
8. device as claimed in claim 7, is characterized in that, also comprise:
Definite threshold unit, is divided into multiple cut zone for utilizing dynamic thresholding method by described target image; Maximum variance between clusters is utilized to calculate the binary-state threshold of each cut zone;
Then described processing unit, specifically for utilizing the binary-state threshold of each cut zone self, to this cut zone binary conversion treatment, and obtains the gray level image of each cut zone; The gray level image of all cut zone is formed described target gray image.
9. device as claimed in claim 7, is characterized in that, also comprise:
Brightness correction unit, for background luminance uneven in described target image, carries out gamma correction;
Then described processing unit, specifically for utilizing default global threshold to carry out binary conversion treatment to described target image, obtains the described target gray image after binary conversion treatment.
10. the device as described in any one of claim 7-9, is characterized in that, also comprises:
Calculating parameter unit, for calculating the Morphologic Parameters of each connected region.
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