WO2021143700A1 - 生物芯片的数据处理方法、装置、终端及可读介质 - Google Patents

生物芯片的数据处理方法、装置、终端及可读介质 Download PDF

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WO2021143700A1
WO2021143700A1 PCT/CN2021/071365 CN2021071365W WO2021143700A1 WO 2021143700 A1 WO2021143700 A1 WO 2021143700A1 CN 2021071365 W CN2021071365 W CN 2021071365W WO 2021143700 A1 WO2021143700 A1 WO 2021143700A1
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image
sample point
sample
center position
biochip
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PCT/CN2021/071365
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English (en)
French (fr)
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吴琼
赵子健
史永明
黄继景
唐大伟
刘宗民
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京东方科技集团股份有限公司
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Priority to US17/424,172 priority Critical patent/US11875549B2/en
Publication of WO2021143700A1 publication Critical patent/WO2021143700A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • 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
    • 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
    • G06T5/30—Erosion or dilatation, e.g. thinning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/155—Segmentation; Edge detection involving morphological operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
    • 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
    • G06T2207/20036—Morphological image processing
    • 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
    • G06T2207/30072—Microarray; Biochip, DNA array; Well plate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Definitions

  • This article relates to, but is not limited to, the field of image processing technology, in particular to a data processing method, device, terminal and readable medium of a biochip.
  • Biochip technology fixes a certain amount of biochemical reaction space on a certain area of the substrate. When in use, load the test sample on the biochip, provide reaction conditions to make the reaction occur, and then use the light and electrical signals that can be detected by the existing technology as the basis for judging the intensity of the reaction or whether it occurs or not, and indirectly obtain the test sample Bioinformatics. Biochips overcome the traditional drawbacks of requiring repeated experiments, save manpower, sample size, and improve detection accuracy. It is an important means of modern life science research.
  • the existing biochip sample point detection methods mainly include parameter-dependent methods, label-assisted methods, automatic detection methods based on neural networks, and automatic detection methods based on edge detection and grid division.
  • the method of parameter dependence depends on manually set or input biochip parameters, such as row and column coordinates, chamber position template, etc., when using the biochip parameters, you must know the biochip parameters and input accurately to get the test results. This is in the type of biochips. Diversity or missing parameters will increase labor costs.
  • the label-assisted method requires the design of auxiliary labels on the biochip, which is suitable for analyzing the biochips from a single manufacturer.
  • the current biochips come from different manufacturers, and the design methods of the auxiliary labels of the biochips of different manufacturers are not unique, resulting in labeling.
  • the auxiliary method has poor compatibility and poor flexibility.
  • the algorithm implementation of the existing automatic detection method based on neural network and the automatic detection method based on edge detection and grid division is more complicated and computationally complex.
  • the embodiments of the present disclosure provide a biochip data processing method, device, terminal, and readable medium, which can be compatible with multiple biochip designs and reduce computational complexity.
  • an embodiment of the present disclosure provides a data processing method for a biochip, including: acquiring a biochip image to be detected; performing binarization processing on the biochip image to obtain a binary image; Perform morphological dilation operation to obtain the first image, perform morphological dilation operation on the binary image in the column direction, and obtain the second image; The connected area of the second image is detected to determine the number of rows and columns of the sample point array and the center position information of each sample point.
  • an embodiment of the present disclosure provides a biochip data processing device, including: an image acquisition module configured to acquire a biochip image to be detected; a binarization processing module configured to binarize the biochip image Process to obtain a binary image; the morphology operation module is configured to perform a morphological expansion operation on the binary image in the row direction to obtain a first image, and perform a morphological expansion operation on the binary image in the column direction to obtain a second image ;
  • the first detection module is configured to determine the number of rows and columns of the sample point array and the center of each sample point by performing connected domain detection on the first image in the row direction and connected domain detection on the second image in the column direction location information.
  • an embodiment of the present disclosure provides a data processing terminal, including a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the data of the biochip as described above Processing method steps.
  • an embodiment of the present disclosure provides a computer-readable medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned biochip data processing method are realized.
  • the biochip image is binarized, and the obtained binary image is subjected to morphological expansion operation and connected domain detection, so as to adaptively obtain the number of rows and columns of the sample point array of the biochip.
  • the center position information of the sample point thereby supporting the realization of the sample point detection of the biochip.
  • the embodiments of the present disclosure are compatible with multiple biochip designs, and the implementation process is simple, and the computational complexity is low.
  • FIG. 1 is a flowchart of a data processing method of a biochip provided by at least one embodiment of the present disclosure
  • FIG. 2 is an exemplary flowchart of a data processing method for a biochip provided by at least one embodiment of the present disclosure
  • FIG. 3 is an example diagram of a biochip image to be detected according to at least one embodiment of the present disclosure
  • FIG. 5 is another exemplary flowchart of a data processing method of a biochip provided by at least one embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of a data processing device for a biochip provided by at least one embodiment of the present disclosure
  • FIG. 7 is an exemplary diagram of a data processing device for a biochip provided by at least one embodiment of the present disclosure.
  • FIG. 8 is another example diagram of a data processing device for a biochip provided by at least one embodiment of the present disclosure.
  • Fig. 9 is an exemplary diagram of a data processing terminal provided by at least one embodiment of the present disclosure.
  • the present disclosure includes and contemplates combinations with features and elements known to those of ordinary skill in the art.
  • the embodiments, features, and elements disclosed in the present disclosure can also be combined with any conventional features or elements to form a unique invention solution defined by the claims.
  • Any feature or element of any embodiment can also be combined with features or elements from other invention solutions to form another unique invention solution defined by the claims. Therefore, it should be understood that any feature shown or discussed in this disclosure can be implemented individually or in any appropriate combination. Therefore, the embodiments are not subject to other restrictions except for the restrictions made according to the appended claims and their equivalents.
  • at least one modification and change may be made within the protection scope of the appended claims.
  • the specification may have presented the method or process as a specific sequence of steps. However, to the extent that the method or process does not depend on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As those of ordinary skill in the art will understand, other sequence of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be construed as a limitation on the claims. In addition, the claims for the method or process should not be limited to performing their steps in the written order, and those skilled in the art can easily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present disclosure.
  • the embodiments of the present disclosure provide a biochip data processing method, device, terminal, and readable medium, which can be compatible with multiple biochip designs and reduce computational complexity.
  • the data processing method provided by the embodiments of the present disclosure obtains the biological information of the test sample by analyzing the biochip image obtained after the biochip is loaded with the test sample.
  • the data processing method provided in this embodiment can be applied to array-type biological fluorescent chips.
  • FIG. 1 is a flowchart of a data processing method of a biochip provided by at least one embodiment of the present disclosure. As shown in FIG. 1, the data processing method of the biochip provided in this embodiment includes:
  • Step S11 Obtain a biochip image to be detected
  • Step S12 Binarize the biochip image to obtain a binary image
  • Step S13 Perform a morphological expansion operation on the binary image in the row direction to obtain a first image, and perform a morphological expansion operation on the binary image in the column direction to obtain a second image;
  • Step S14 Determine the number of rows and columns of the sample point array and the center position information of each sample point by performing connected domain detection on the first image in the row direction and connected domain detection on the second image in the column direction.
  • the row direction may be the horizontal direction of the sample point array
  • the column direction may be the vertical direction of the sample point array.
  • This embodiment performs adaptive detection for a collimated sample point array, where the collimated sample point array can mean that the row direction of the sample point array is parallel to the horizontal direction or the angle between the two is within the error range, and the sample point array The column direction is parallel to the vertical direction or the angle between the two is within the error range.
  • the collimation adjustment of the biochip image can be performed first, and then adaptive detection is performed according to the data processing method of this embodiment.
  • the sample points in the biochip image may be circular or rectangular. However, this disclosure is not limited to this.
  • performing a morphological expansion operation on the binary image in the row direction to obtain the first image may include: performing morphological expansion on the binary image in the row direction according to the first expansion operator Operate to obtain a first image, where a connected domain in the row direction in the first image represents a row of sample points.
  • performing a morphological expansion operation on the binary image in the column direction to obtain the second image may include: performing a morphological expansion operation on the binary image in the column direction according to the second expansion operator to obtain the second image , Where a connected domain in the column direction in the second image represents a column of sample points.
  • the value of the first expansion operator in the row direction is the width of the biochip image
  • the value of the second expansion operator in the column direction is the height of the biochip image
  • the value of the first expansion operator in the column direction may be 1
  • the value of the second expansion operator in the row direction may be 1.
  • the width of the biochip image is the length of the biochip image in the horizontal direction
  • the height of the biochip image is the length of the biochip image in the vertical direction.
  • step S14 may include: by detecting the connected domain in the row direction in the first image, determining the number of rows of the sample point array and the center position information of each row of sample points in the column direction; by detecting the second The connected domain in the column direction of the image determines the number of columns of the sample point array and the center position information of each column of sample points in the row direction; according to the center position information of each row of sample points in the column direction and the row direction of each column of sample points The center position information of each sample point is obtained.
  • the center position information may include coordinate values in an image pixel coordinate system determined according to the biochip image.
  • the center position information of each row of sample points in the column direction can be stored in a one-dimensional vector, and the length of the one-dimensional vector is the number of rows of the sample point array; the center position information of each column of sample points in the row direction can be Stored in a one-dimensional vector, the length of the one-dimensional vector is the number of columns of the sample point array.
  • this disclosure is not limited to this.
  • the number of rows of the sample point array and the center position information of each row of sample points in the column direction can be stored in a two-dimensional vector.
  • the two-dimensional vector can record the row number and corresponding center position information.
  • determining the number of rows of the sample point array and the center position information of each row of sample points in the column direction by detecting the connected domains in the row direction in the first image may include: based on the first image, Use the findContours function in the open source computer vision library (OpenCV, Open Source Computer Vision Library) to obtain the number of rows of the sample point array and the center position information of each row of sample points in the column direction.
  • OpenCV Open Source Computer Vision Library
  • determining the number of columns of the sample point array and the center position information of each column of sample points in the row direction may include: using the findContours function in OpenCV to obtain samples based on the second image The number of columns of the dot array and the center position information of each column of sample points in the row direction.
  • the algorithm implementation can be simplified.
  • this disclosure is not limited to this.
  • other existing edge detection algorithms or custom edge detection algorithms can be used to detect the connected domain.
  • the data processing method provided in this embodiment may further include: performing binarization processing on the biochip image to obtain a target image, or performing binarization processing and morphological operations on the biochip image, Obtain the target image, where the threshold used in the binarization process to obtain the target image is greater than the threshold used in the binarization process to obtain the binary image; based on the number of rows and columns of the sample point array and each sample point The center position information of the target image is detected, and the position of the positive sample point in the sample point array is determined.
  • the morphological operation may include an expansion operation, or an expansion operation and an erosion operation.
  • the influence of noise can be eliminated through morphological operation, thereby improving the robustness to image noise and improving the reliability of calculation.
  • performing sample point detection on the target image based on the number of rows and columns of the sample point array and the center position information of each sample point to determine the position of the positive sample point in the sample point array may include :
  • an initialized sample point detection matrix is obtained (for example, the sample point detection matrix is initialized to a zero matrix);
  • the position of the positive sample point in the sample point array is determined.
  • the center position of each sample point and the pixel values corresponding to its four neighborhoods may be traversed, or the center position of each sample point and the pixel values corresponding to its eight neighborhoods may be traversed.
  • this disclosure is not limited to this.
  • the negative and positive detection of the sample point is performed, so that the positive sample in the sample point array Point to locate.
  • the data processing method of this embodiment may further include: performing sample point detection on the target image based on the number of rows and columns of the sample point array and the center position information of each sample point to determine the positive sample The number of points; or, based on the target image, use the findContours function in OpenCV to get the number of positive sample points in the sample point array.
  • the total number of the third value in the sample point detection matrix can be counted, that is, the number of positive sample points; alternatively, the number of positive sample points can be calculated.
  • the number is initialized to 0.
  • the number of positive sample points that is, add up to 1. After the traversal is completed, the number of positive sample points in the sample point array can be obtained.
  • FIG. 2 is an exemplary flowchart of a data processing method of a biochip provided by at least one embodiment of the present disclosure. This exemplary embodiment is used to count and locate the positive sample points in the sample point array of the array type fluorescent biochip.
  • the time complexity of the data processing method of this exemplary embodiment may be linear O(n), and the space complexity may be O(n).
  • this disclosure is not limited to this.
  • the data processing method provided by this exemplary embodiment includes the following processes:
  • Step S21 Obtain a biochip image to be detected, where the biochip image to be detected can be recorded as im_ori.
  • the reaction conditions are provided to cause the reaction to occur, and the image of the biochip to be tested can be obtained by imaging technology, as shown in FIG. 3, for example.
  • the biochip image needs to be adjusted so that the sample point array is aligned before subsequent processing is performed.
  • the present disclosure does not limit the processing method of image collimation.
  • the image pixel coordinate system is defined with the upper left corner of the biochip image as the origin, where the row direction may be a horizontal direction, parallel to the abscissa axis (such as the X axis shown in FIG. 3), and the column direction It can be a vertical direction, parallel to the ordinate axis (such as the Y axis shown in FIG. 3).
  • the origin of the image pixel coordinate system may be the lower left corner or the upper right corner of the biochip image.
  • Step S22 Binarize the biochip image im_ori by using the Otsu algorithm to obtain a binary image, for example, denoted as im_bin.
  • the threshold T1 of the binarization process can be obtained through the Otsu algorithm.
  • this disclosure is not limited to this. In other implementation manners, other binarization algorithms may be used, or a default threshold may be used for binarization processing.
  • the white area (gray value 255) of the binary image obtained by the binarization process of step S22 represents the chip sample point
  • the black area (gray The degree value is 0) represents the chip background.
  • the white area of the binary image may represent the chip background
  • the black area may represent the chip sample points.
  • Step S23 Perform a morphological expansion operation on the binary image im_bin in the horizontal direction (that is, the row direction) to obtain a first image, for example, denoted as im_bin_h.
  • the first expansion operator is used to perform the morphological expansion operation, where the value of the first expansion operator in the horizontal direction can be the width of the biochip image, and the value of the first expansion operator in the vertical direction can be For example, the first expansion operator can be the width of the biochip image*1.
  • the first expansion operator can have other values, as long as it is ensured that each row of sample points forms a connected domain through the expansion operation.
  • step S23 multiple connected domains can be formed in the binary image, and each row of sample points can form a connected domain.
  • Step S24 Determine the number of rows of the sample point array and the center position information (for example, the center coordinate value) of each row of sample points in the column direction by performing connected domain detection on the first image im_bin_h, to obtain the first vector coo_h.
  • the center position information for example, the center coordinate value
  • the findContours function in OpenCV can be used to obtain the number of rows of the chamber array (ie, the sample point array) and the center coordinate value of each row in the vertical direction (ie, the ordinate value of the center of each row),
  • the center coordinate values are stored in the first vector coo_h in order from top to bottom.
  • the first vector coo_h may be a one-dimensional vector.
  • the first vector coo_h stores the ordinate value of the center of each row of sample points, and the length of the vector represents the number of rows of the sample point array.
  • a two-dimensional vector may be used to record the row number and the center coordinate value of each row in the vertical direction.
  • the input of the findContours function can be the first image im_bin_h
  • the output can be a collection of contour points of each connected domain.
  • the ordinate value corresponding to the center of each connected domain can be calculated, and the ordinate can be calculated.
  • the values are sequentially stored in the first vector coo_h.
  • step S24 the number of rows of the sample point array and the center position information of each row can be adaptively obtained.
  • Step S25 Perform a morphological expansion operation on the binary image im_bin in the vertical direction (ie, the column direction) to obtain a second image, for example, denoted as im_bin_v.
  • the second expansion operator is used to perform the morphological expansion operation, where the value of the second expansion operator in the vertical direction can be the height of the biochip image, and the value of the second expansion operator in the horizontal direction can be It is 1, for example, the second expansion operator can be 1*the height of the biochip image.
  • the second expansion operator can have other values, as long as it is ensured that each column of sample points forms a connected domain through the expansion operation.
  • step S25 multiple connected domains can be formed in the binary image, and each column of sample points can form a connected domain.
  • Step S26 Determine the number of columns of the sample point array and the center position information (for example, the center coordinate value) of the sample points in each column by performing connected domain detection on the second image im_bin_v, to obtain a second vector coo_v.
  • the second vector coo_v may be a one-dimensional vector
  • the second vector coo_v stores the abscissa value of the center of each column of sample points
  • the length of the vector represents the total number of columns of the sample point array.
  • a two-dimensional vector may be used to record the column number and the center coordinate value of each column in the horizontal direction.
  • the input of the findContours function can be the second image im_bin_v
  • the output can be a collection of contour points of each connected domain.
  • the abscissa value corresponding to the center of each connected domain can be calculated, and the abscissa The values are sequentially stored in the second vector coo_v.
  • step S26 the number of columns of the sample point array and the center position information of each column can be adaptively obtained.
  • Step S27 Initialize the sample point detection matrix sample_loc as a zero matrix, where the number of rows of the sample point detection matrix sample_loc is the vector length of the first vector coo_h obtained in step S24, and the number of columns is the vector length of the second vector coo_v obtained in step S26. .
  • the sample point detection matrix sample_loc obtained by initialization in this step can be used to record subsequent detection results of positive sample points.
  • Step S28 Binarize the biochip image im_ori and perform morphological operations to obtain a target image, for example, denoted as im_bin_positive.
  • the biochip image is binarized first, and then the obtained binary image is subjected to morphological operations to eliminate the influence of noise, improve the robustness to image noise, and enhance the reliability of the calculation.
  • this disclosure is not limited to this.
  • the morphological operation in this step may include expansion operation, or expansion operation and corrosion operation.
  • the expansion operator of the morphological expansion operation in this step can be 5*5.
  • this disclosure is not limited to this.
  • Step S29 For the target image im_bin_positive obtained in step 28, the pixel value corresponding to the center position of the sample point and the pixel value corresponding to the four neighborhoods of the center position are traversed, and the sample point detection matrix sample_loc is updated according to the traversal result.
  • the center position of the sample point can be determined according to the first vector coo_h obtained in step S24 and the second vector coo_v obtained in step S26.
  • the center position of the sample point in the first row and first column can be determined according to the first coordinate value in the first vector coo_h and the first coordinate value in the second vector coo_v, where the first coordinate value in the first vector coo_h
  • the coordinate value is the ordinate value of the center of the sample point
  • the first coordinate value in the second vector coo_v is the abscissa value of the center of the sample point.
  • the center position of the sample point in the i-th row and j-th column is the y-th coordinate value in the first vector coo_h
  • the j-th coordinate value in the second vector coo_v is the coordinate position determined by the abscissa value.
  • i and j are both positive integers, i is less than or equal to the total number of rows, and j is less than or equal to the total number of columns.
  • the present disclosure does not limit the number of neighborhoods of the center position of the traversed sample points.
  • the pixel values corresponding to the center position of the sample point of the target image and the pixel values corresponding to the eight neighborhoods of the center position can be traversed.
  • Step S30 Determine the position and number of positive sample points (that is, the number of non-zero elements in the sample point detection matrix sample_loc) according to the updated sample point detection matrix sample_loc in step S29.
  • the position with the element value of 1 in the sample point position matrix sample_loc indicates the positive sample point
  • the position with the element value of 0 indicates the negative sample point.
  • sample point detection matrix sample_loc can be obtained based on the biochip image shown in FIG. 3 through the processing of this embodiment:
  • the number of positive sample points in the biochip image shown in FIG. 3 is 137.
  • the positive sample points can be located according to the positions of the non-zero elements in the sample point detection matrix. For example, if the value of the element in the second row and first column of the sample point detection matrix is 1, then Determine that the sample point in the second row and first column of the sample point array is a positive sample point.
  • the element value corresponding to the position of the detected positive sample point may be updated to 0, and the negative The element value corresponding to the position of the sample point is kept as 1. Then, the number of positive sample points can be obtained by counting the number of element values in the sample point detection matrix sample_loc.
  • the result of the sample point detection matrix sample_loc can be superimposed on the biochip image to be detected to obtain the visualized result as shown in FIG. 4, so as to intuitively understand the positive The location and number of sample points.
  • the data processing method provided in this embodiment is based on image binarization processing, morphological operations, and connected domain detection. With the help of functions in OpenCV, the automatic detection of the sample point array in the biochip image is realized, and the adaptive sample point array information ( The number of rows and columns of the sample point array, and the center position information of each sample point), and the position and number of positive sample points can be detected based on the sample point array information.
  • the data processing method provided in this embodiment can reduce computational complexity, increase computational speed, and be compatible with multiple chip designs. Moreover, it can effectively eliminate the influence of noise around sample points, and has better robustness to image noise, thereby increasing the algorithm reliability.
  • FIG. 5 is another exemplary flowchart of a data processing method of a biochip provided by at least one embodiment of the present disclosure.
  • the data processing method provided in this embodiment includes steps S31 to S41.
  • the difference between the data processing method provided in this embodiment and the data processing method shown in FIG. 2 is that the method for determining the number of positive sample points is different.
  • step S41 based on the target image obtained in step S38, the findContours function in OpenCV is used to obtain the number of positive sample points in the sample point array.
  • the connected domains of the target image are detected by the findContours function, and the total number of detected connected domains is the number of positive sample points.
  • step S40 the positive sample points are located according to the updated sample point detection matrix sample_loc.
  • the positioning method of the positive sample point refer to the description of the embodiment shown in FIG. 2.
  • FIG. 6 is a schematic diagram of a data processing device for a biochip provided by at least one embodiment of the present disclosure.
  • the data processing device provided by this embodiment includes: an image acquisition module 11, a binarization processing module 12, a morphology operation module 13, and a first detection module 14.
  • the image acquisition module 11 is configured to acquire the biochip image to be detected;
  • the binarization processing module 12 is configured to perform binarization processing on the biochip image to obtain a binary image;
  • the morphology operation module 13 is configured to be in the row direction Perform a morphological expansion operation on the binary image to obtain a first image, and perform a morphological expansion operation on the binary image in the column direction to obtain a second image;
  • the first detection module 14 is configured to perform the first image Perform connected domain detection and perform connected domain detection on the second image in the column direction to determine the number of rows and columns of the sample point array and the center position information of each sample point.
  • the morphology operation module 13 is configured to perform a morphological expansion operation on the binary image in the row direction to obtain the first image: perform the morphological expansion operation on the binary image in the row direction according to the first expansion operator The morphological expansion operation obtains the first image, where a connected domain in the row direction in the first image represents a row of sample points.
  • the morphological operation module 13 is configured to perform a morphological expansion operation on the binary image in the column direction to obtain a second image: perform a morphological expansion operation on the binary image in the column direction according to the second expansion operator to obtain The second image, where a connected domain in the column direction in the second image represents a column of sample points.
  • the value of the first expansion operator in the row direction is the width of the biochip image
  • the value of the second expansion operator in the column direction is the height of the biochip image
  • the first detection module 14 is configured to determine the number of rows of the sample point array by performing connected field detection on the first image in the row direction and connected field detection on the second image in the column direction in the following manner: , The number of columns and the center position information of each sample point: by detecting the connected domain in the row direction in the first image, the number of rows of the sample point array and the center position information of each row of sample points in the column direction are determined; Second, the connected domain in the column direction in the image, determine the number of columns of the sample point array and the center position information of each column of sample points in the row direction; according to the center position information of each row of sample points in the column direction and the position of each column of sample points The center position information in the row direction obtains the center position information of each sample point.
  • the first detection module 14 is configured to determine the number of rows of the sample point array and the center position of each row of sample points in the column direction by detecting the connected domains in the row direction in the first image in the following manner Information: Based on the first image, use the findContours function in OpenCV to obtain the number of rows of the sample point array and the center position information of each row of sample points in the column direction.
  • the first detection module 14 is configured to determine the number of columns of the sample point array and the center position information of each column of sample points in the row direction by detecting the connected domains in the second image in the column direction in the following manner: based on the second image, using The findContours function in OpenCV obtains the number of columns of the sample point array and the center position information of each column of sample points in the row direction.
  • the binarization processing module 12 is further configured to perform binarization processing on the biochip image to obtain the target image.
  • the binarization processing module 12 and the morphological operation module 13 are further configured to sequentially perform binarization and morphological operations on the biochip image to obtain the target image.
  • the threshold value used in the binarization process to obtain the target image is greater than the threshold value used in the binarization process to obtain the binary image.
  • the data processing device may further include: a second detection module 15 configured to perform a measurement on the target based on the number of rows and columns of the sample point array and the center position information of each sample point. The image is tested for sample points to determine the location information of the positive sample points.
  • a second detection module 15 configured to perform a measurement on the target based on the number of rows and columns of the sample point array and the center position information of each sample point. The image is tested for sample points to determine the location information of the positive sample points.
  • the second detection module 15 is configured to perform sample point detection on the target image based on the number of rows and columns of the sample point array and the center position information of each sample point in the following manner to determine the positive sample point Position in the sample point array: Based on the number of rows and columns of the sample point array, the initialized sample point detection matrix is obtained; based on the center position information of each sample point in the sample point array, traverse the target image for each sample point The pixel value corresponding to the center position and the pixel value corresponding to the neighborhood of the center position of the sample point; when the pixel value corresponding to the center position of any sample point in the target image or the neighborhood of the center position of the sample point is detected If the corresponding pixel value is the first value, the element value at the corresponding position of the sample point in the sample point detection matrix is updated to the third value; when the pixel value corresponding to the center position of any sample point in the target image or the If the pixel value corresponding to the neighborhood of the center position of the sample point is the second value,
  • the data processing device of this embodiment may further include: a third detection module 16 configured to be based on the number of rows and columns of the sample point array and the center of each sample point The location information is used to detect the sample points of the target image to determine the number of positive sample points; or, based on the target image, use the findContours function in OpenCV to obtain the number of positive sample points in the sample point array.
  • a third detection module 16 configured to be based on the number of rows and columns of the sample point array and the center of each sample point The location information is used to detect the sample points of the target image to determine the number of positive sample points; or, based on the target image, use the findContours function in OpenCV to obtain the number of positive sample points in the sample point array.
  • the embodiments of the present disclosure also provide a data processing terminal, including a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the above-mentioned biochip data processing method.
  • Fig. 9 is an exemplary diagram of a data processing terminal provided by at least one embodiment of the present disclosure.
  • the data processing terminal includes: a processor 21, a memory 22, a bus system 23, and a display 24, where the processor 21, the memory 22, and the display 24 are connected through the bus system 23, and the memory 22
  • the processor 21 is configured to store instructions, and the processor 21 is configured to execute instructions stored in the memory 22 to control the display content of the display 24.
  • the processor 21 may be a central processing unit (CPU, Central Processing Unit), and the processor 21 may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), Ready-made programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 22 may include a read-only memory and a random access memory, and provide instructions and data to the processor 21. A part of the memory 22 may also include a non-volatile random access memory. For example, the memory 22 may also store device type information.
  • the bus system 23 may include a power bus, a control bus, a status signal bus, etc. in addition to a data bus. However, for clear description, at least one bus is marked as the bus system 23 in FIG. 9.
  • the processing performed by the above-mentioned data processing apparatus may be completed by an integrated logic circuit of hardware in the processor 21 or instructions in the form of software. That is, the steps of the method disclosed in the embodiments of the present disclosure may be embodied as being executed and completed by a hardware processor, or executed and completed by a combination of hardware and software modules in the processor.
  • the software module can be located in storage media such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc.
  • the storage medium is located in the memory 22, and the processor 21 reads the information in the memory 22, and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.
  • embodiments of the present disclosure also provide a computer-readable medium storing a computer program, and when the computer program is executed by a processor, the steps of the data processing method described above are implemented.
  • Such software may be distributed on a computer-readable medium, and the computer-readable medium may include a computer storage medium (or a non-transitory medium) and a communication medium (or a transitory medium).
  • the term computer storage medium includes volatile and non-volatile data implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Sexual, removable and non-removable media.
  • Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or Any other medium used to store desired information and that can be accessed by a computer.
  • communication media usually contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as carrier waves or other transmission mechanisms, and may include any information delivery media. .

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Abstract

一种生物芯片的数据处理方法，包括：获取待检测的生物芯片图像；对生物芯片图像进行二值化处理，得到二值图像；在行方向上对二值图像进行形态学膨胀操作，得到第一图像，在列方向上对二值图像进行形态学膨胀操作，得到第二图像；通过在行方向对第一图像进行连通域检测以及在列方向对第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。

Description

生物芯片的数据处理方法、装置、终端及可读介质
本申请要求于2020年1月14日提交中国专利局、申请号为202010037639.2、发明名称为“生物芯片的数据处理方法、装置、终端及可读介质”的中国专利申请的优先权，其内容应理解为通过引用的方式并入本申请中。
技术领域
本文涉及但不限于图像处理技术领域，尤指一种生物芯片的数据处理方法、装置、终端及可读介质。
背景技术
生物芯片技术将一定数量的生物化学反应空间固定于一定面积的基片。在使用时，加载测试样本到生物芯片上，提供反应条件令反应发生，然后利用已有技术可检测到的光、电信号等作为反应发生强度或发生与否的判断依据，间接获取测试样本的生物信息。生物芯片克服传统需要多次重复实验的弊端，节约人力、样本量，提高了检测精度，是现代生命科学研究的重要手段。
已有的生物芯片样本点检测方法主要有参数依赖方法、标记辅助方法、基于神经网络的自动检测方法、基于边缘检测和网格划分的自动检测方法。其中，参数依赖方法，依赖于人工设定或输入的生物芯片参数，比如行列坐标、腔室位置模板等，在使用时必须知晓生物芯片参数并准确输入方能得到检测结果，这在生物芯片种类多样或参数缺失的情况会造成人力成本的提高。标记辅助方法需要在生物芯片上设计辅助标记，适用于分析来自单一生产方的生物芯片，但是目前的生物芯片来自不同生产方，不同生产方的生物芯片的辅助标记的设计方式不唯一，导致标记辅助方法的兼容性不佳、灵活性较差。已有的基于神经网络的自动检测方法和基于边缘检测和网格划分的自动检测方法的算法实现较为复杂、计算复杂度较高。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本公开实施例提供了一种生物芯片的数据处理方法、装置、终端及可读介质，可以兼容多种生物芯片设计并降低计算复杂度。
一方面，本公开实施例提供了一种生物芯片的数据处理方法，包括：获取待检测的生物芯片图像；对生物芯片图像进行二值化处理，得到二值图像；在行方向上对二值图像进行形态学膨胀操作，得到第一图像，在列方向上对二值图像进行形态学膨胀操作，得到第二图像；通过在行方向对第一图像进行连通域检测以及在列方向对所述第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
另一方面，本公开实施例提供一种生物芯片的数据处理装置，包括：图像获取模块，配置为获取待检测的生物芯片图像；二值化处理模块，配置为对生物芯片图像进行二值化处理，得到二值图像；形态学操作模块，配置为在行方向上对二值图像进行形态学膨胀操作，得到第一图像，在列方向上对二值图像进行形态学膨胀操作，得到第二图像；第一检测模块，配置为通过在行方向对第一图像进行连通域检测以及在列方向对第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
另一方面，本公开实施例提供一种数据处理终端，包括：存储器和处理器，所述存储器存储有计算机程序，所述计算机程序被所述处理器执行时实现如上所述的生物芯片的数据处理方法的步骤。
另一方面，本公开实施例提供一种计算机可读介质，存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的生物芯片的数据处理方法的步骤。
本公开实施例通过对生物芯片图像进行二值化处理，并对得到的二值图像进行形态学膨胀操作和连通域检测，可以自适应得到生物芯片的样本点阵列的行数、列数以及每一样本点的中心位置信息，从而支持实现生物芯片的 样本点检测。本公开实施例可以兼容多种生物芯片设计，而且实现过程简单，计算复杂度较低。
在阅读并理解了附图和详细描述后，可以明白其他方面。
附图说明
附图用来提供对本公开技术方案的理解，并且构成说明书的一部分，与本公开的实施例一起用于解释本公开的技术方案，并不构成对本公开技术方案的限制。附图中至少一个部件的形状和大小不反映真实比例，目的只是示意说明本公开内容。
图1为本公开至少一实施例提供的生物芯片的数据处理方法的流程图；
图2为本公开至少一实施例提供的生物芯片的数据处理方法的一种示例流程图；
图3为本公开至少一实施例的待检测的生物芯片图像的一个示例图；
图4为本公开至少一实施例的输出结果的示例图；
图5为本公开至少一实施例提供的生物芯片的数据处理方法的另一示例流程图；
图6为本公开至少一实施例提供的生物芯片的数据处理装置的示意图；
图7为本公开至少一实施例提供的生物芯片的数据处理装置的一种示例图；
图8为本公开至少一实施例提供的生物芯片的数据处理装置的另一示例图；
图9为本公开至少一实施例提供的数据处理终端的示例图。
具体实施方式
本公开描述了多个实施例，但是该描述是示例性的，而不是限制性的，并且对于本领域的普通技术人员来说显而易见的是，在本公开所描述的实施例包含的范围内可以有更多的实施例和实现方案。尽管在附图中示出了许多 可能的特征组合，并在实施方式中进行了讨论，但是所公开的特征的许多其它组合方式也是可能的。除非特意加以限制的情况以外，任何实施例的任何特征或元件可以与任何其它实施例中的任何其他特征或元件结合使用，或可以替代任何其它实施例中的任何其他特征或元件。
本公开包括并设想了与本领域普通技术人员已知的特征和元件的组合。本公开已经公开的实施例、特征和元件也可以与任何常规特征或元件组合，以形成由权利要求限定的独特的发明方案。任何实施例的任何特征或元件也可以与来自其它发明方案的特征或元件组合，以形成另一个由权利要求限定的独特的发明方案。因此，应当理解，在本公开中示出或讨论的任何特征可以单独地或以任何适当的组合来实现。因此，除了根据所附权利要求及其等同替换所做的限制以外，实施例不受其它限制。此外，可以在所附权利要求的保护范围内进行至少一种修改和改变。
此外，在描述具有代表性的实施例时，说明书可能已经将方法或过程呈现为特定的步骤序列。然而，在该方法或过程不依赖于本文所述步骤的特定顺序的程度上，该方法或过程不应限于所述的特定顺序的步骤。如本领域普通技术人员将理解的，其它的步骤顺序也是可能的。因此，说明书中阐述的步骤的特定顺序不应被解释为对权利要求的限制。此外，针对该方法或过程的权利要求不应限于按照所写顺序执行它们的步骤，本领域技术人员可以容易地理解，这些顺序可以变化，并且仍然保持在本公开实施例的精神和范围内。
本公开实施例提供一种生物芯片的数据处理方法、装置、终端及可读介质，可以兼容多种生物芯片设计，并降低计算复杂度。本公开实施例提供的数据处理方法通过对生物芯片加载测试样本后得到的生物芯片图像进行分析，来获取测试样本的生物信息。本实施例提供的数据处理方法可以适用于阵列型生物荧光芯片。
图1为本公开至少一实施例提供的生物芯片的数据处理方法的流程图。如图1所示，本实施例提供的生物芯片的数据处理方法，包括：
步骤S11、获取待检测的生物芯片图像；
步骤S12、对生物芯片图像进行二值化处理，得到二值图像；
步骤S13、在行方向上对二值图像进行形态学膨胀操作，得到第一图像，在列方向上对二值图像进行形态学膨胀操作，得到第二图像；
步骤S14、通过在行方向对第一图像进行连通域检测以及在列方向对第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
在本实施例中，行方向可以为样本点阵列的水平方向，列方向可以为样本点阵列的竖直方向。本实施例针对准直的样本点阵列进行自适应检测，其中，准直的样本点阵列可以指样本点阵列的行方向与水平方向平行或两者之间的角度在误差范围内、样本点阵列的列方向与竖直方向平行或两者之间的角度在误差范围内。当生物芯片图像内的样本点阵列存在倾斜角度时，可以先对生物芯片图像进行准直调整后，再按照本实施例的数据处理方法进行自适应检测。
在一些示例性实施例中，生物芯片图像中的样本点可以为圆形或矩形。然而，本公开对此并不限定。
在一些示例性实施方式中，步骤S13中，在行方向上对二值图像进行形态学膨胀操作，得到第一图像，可以包括：在行方向上根据第一膨胀算子对二值图像进行形态学膨胀操作，得到第一图像，其中，第一图像中行方向上的一个连通域表示一行样本点。
步骤S13中，在列方向上对二值图像进行形态学膨胀操作，得到第二图像，可以包括：在列方向上根据第二膨胀算子对二值图像进行形态学膨胀操作，得到第二图像，其中，第二图像中列方向上的一个连通域表示一列的样本点。
在一些示例性实施方式中，第一膨胀算子在行方向上的取值为生物芯片图像的宽度，第二膨胀算子在列方向上的取值为生物芯片图像的高度。此外，第一膨胀算子在列方向上的取值可以为1，第二膨胀算子在行方向上的取值可以为1。然而，本公开对此并不限定。生物芯片图像的宽度为生物芯片图像沿水平方向的长度，生物芯片图像的高度为生物芯片图像沿竖直方向的长度。
在一示例性实施方式中，步骤S14可以包括：通过检测第一图像中行方向上的连通域，确定样本点阵列的行数以及每一行样本点在列方向上的中心位置信息；通过检测第二图像中列方向上的连通域，确定样本点阵列的列数以及每一列样本点在行方向上的中心位置信息；根据每一行样本点在列方向上的中心位置信息以及每一列样本点在行方向上的中心位置信息，得到每一样本点的中心位置信息。其中，中心位置信息可以包括在根据生物芯片图像确定的图像像素坐标系中的坐标值。其中，每一行样本点在列方向上的中心位置信息可以存储在一个一维向量中，该一维向量的长度即为样本点阵列的行数；每一列样本点在行方向上的中心位置信息可以存储在一个一维向量中，该一维向量的长度即为样本点阵列的列数。然而，本公开对此并不限定。在其他实现方式中，样本点阵列的行数以及每一行样本点在列方向上的中心位置信息可以通过一个二维向量进行存储，比如，二维向量可以记录行编号以及对应的中心位置信息。
在一些示例性实施方式中，通过检测第一图像中在行方向上的连通域，确定样本点阵列的行数以及每一行样本点在列方向上的中心位置信息，可以包括：基于第一图像，利用开源计算机视觉库(OpenCV，Open Source Computer Vision Library)中的findContours函数得到样本点阵列的行数以及每一行样本点在列方向上的中心位置信息。
通过检测第二图像中在列方向上的连通域，确定样本点阵列的列数以及每一列样本点在行方向上的中心位置信息，可以包括：基于第二图像，利用OpenCV中的findContours函数得到样本点阵列的列数以及每一列样本点在行方向上的中心位置信息。
在本示例性实施方式中，通过借用OpenCV中的findContours函数，可以简化算法实现。然而，本公开对此并不限定。在其他实现方式中，可以采用其他已有的边缘检测算法或自定义的边缘检测算法来检测连通域。
在一些示例性实施方式中，本实施例提供的数据处理方法还可以包括：对生物芯片图像进行二值化处理，得到目标图像，或者，对生物芯片图像进行二值化处理和形态学操作，得到目标图像，其中，得到目标图像进行的二值化处理所采用的阈值大于得到二值图像进行的二值化处理所采用的阈值； 基于样本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行样本点检测，确定阳性样本点在样本点阵列中的位置。
在本示例性实施方式中，形态学操作可以包括膨胀操作、或者膨胀操作和腐蚀操作。通过形态学操作可以消除噪点影响，从而提高对图像噪声的鲁棒性，提高计算可靠性。
在一些示例性实施方式中，基于样本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行样本点检测，确定阳性样本点在样本点阵列中的位置，可以包括：
基于样本点阵列的行数和列数，得到初始化的样本点检测矩阵(比如，样本点检测矩阵初始化为零矩阵)；
基于样本点阵列中每一样本点的中心位置信息，遍历目标图像内每个样本点的中心位置对应的像素值和该样本点的中心位置的邻域对应的像素值；当检测到目标图像内任一样本点的中心位置对应的像素值或该样本点的中心位置的邻域对应的像素值为第一数值(比如，255)，则更新样本点检测矩阵内该样本点对应位置的元素值为第三数值(比如，1)；当检测到目标图像内任一样本点的中心位置对应的像素值或该样本点的中心位置的邻域对应的像素值为第二数值(比如，0)，则将样本点检测矩阵内该样本点对应位置的元素值保持为初始值；
根据遍历目标图像得到的样本点检测矩阵内第三数值的位置，确定阳性样本点在样本点阵列中的位置。
在本示例性实施方式中，可以遍历每个样本点的中心位置及其四邻域对应的像素值，或者，可以遍历每个样本点的中心位置及其八邻域对应的像素值。然而，本公开对此并不限定。
在本示例性实施方式中，通过在目标图像中对每个样本点的中心位置及其邻域对应的像素值进行遍历，来进行样本点的阴阳性检测，从而对样本点阵列中的阳性样本点进行定位。
在一些示例性实施方式中，本实施例的数据处理方法还可以包括：基于样本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行 样本点检测，确定阳性样本点的个数；或者，基于目标图像，利用OpenCV中的findContours函数得到样本点阵列中阳性样本点的个数。
示例性地，在基于样本点检测矩阵对阳性样本点进行定位之后，可以统计样本点检测矩阵内第三数值的总数目，即为阳性样本点的个数；或者，可以将阳性样本点的个数初始化为0，在基于每一样本点的中心位置信息对目标图像进行遍历的过程中，当检测到目标图像内样本点的中心位置或中心位置的邻域对应的像素值为第一数值，则更新阳性样本点的个数，即累加1，在遍历完成后可以得到样本点阵列中阳性样本点的个数。
下面通过示例性的实施过程对本实施例提供的数据处理方法进行详细说明。
图2为本公开至少一实施例提供的生物芯片的数据处理方法的一个示例流程图。本示例性实施例用于对阵列型荧光生物芯片的样本点阵列中的阳性样本点进行计数和定位。
本示例性实施例的数据处理方法的时间复杂度可以为线性型O(n)，空间复杂度可以为O(n)。然而，本公开对此并不限定。
如图2所示，本示例性实施例提供的数据处理方法，包括以下过程：
步骤S21、获取待检测的生物芯片图像，其中，待检测的生物芯片图像可以记为im_ori。
在本示例性实施例中，在加载测试样本到生物芯片后，提供反应条件令反应发生，通过摄像技术可以得到待检测的生物芯片图像，例如图3所示。
在一些示例中，若待检测的生物芯片图像内的样本点阵列发生倾斜，则需要对生物芯片图像进行调整，使得样本点阵列准直后再进行后续处理。本公开对于图像准直的处理方式并不限定。
在本示例性实施例中，以生物芯片图像的左上角为原点定义图像像素坐标系，其中，行方向可以为水平方向，平行于横坐标轴(比如图3所示的X轴)，列方向可以为竖直方向，平行于纵坐标轴(比如图3所示的Y轴)。然而，本公开对此并不限定。在其他实现方式中，图像像素坐标系的原点可以为生物芯片图像的左下角或右上角等。
步骤S22、采用大津(Otsu)算法对生物芯片图像im_ori进行二值化处理，得到二值图像，例如记为im_bin。其中，通过Otsu算法可以得到二值化处理的阈值T1。然而，本公开对此并不限定。在其他实现方式中，可以采用其他二值化算法，或者，可以采用默认的阈值进行二值化处理。
在本示例性实施例中，针对图3所示的生物芯片图像，通过步骤S22的二值化处理得到的二值图像的白色区域(灰度值为255)代表芯片样本点，黑色区域(灰度值为0)代表芯片背景。然而，本公开对此并不限定。在其他实现方式中，二值图像的白色区域可以代表芯片背景，黑色区域代表芯片样本点。
步骤S23、在水平方向(即行方向)上对二值图像im_bin进行形态学膨胀操作，得到第一图像，例如记为im_bin_h。本步骤采用第一膨胀算子进行形态学膨胀操作，其中，第一膨胀算子在水平方向上的取值可以为生物芯片图像的宽度，第一膨胀算子在竖直方向上的取值可以为1，比如，第一膨胀算子可以为生物芯片图像的宽度*1。然而，本公开对此并不限定。第一膨胀算子可以为其他取值，只要保证通过膨胀操作使得每一行样本点形成一个连通域即可。
通过步骤S23可以在二值图像中形成多个连通域，且每一行样本点可以形成一个连通域。
步骤S24、通过对第一图像im_bin_h进行连通域检测，确定样本点阵列的行数以及每一行样本点在列方向上的中心位置信息(比如，中心坐标值)，得到第一向量coo_h。
在本步骤中，可以利用OpenCV中的findContours函数得到腔室阵列(即样本点阵列)的行数以及对应每一行在竖直方向上的中心坐标值(即，每一行中心的纵坐标值)，将中心坐标值按从上到下的顺序存入第一向量coo_h。其中，第一向量coo_h可以为一个一维向量，第一向量coo_h内存储的是每行样本点中心的纵坐标值，向量长度表示样本点阵列的行数。然而，本公开对此并不限定。在其他实现方式中，可以采用二维向量记录行编号以及每一行在竖直方向上的中心坐标值。
其中，findContours函数的输入可以为第一图像im_bin_h，输出可以是 每一个连通域的轮廓点的集合，根据findContours函数的输出信息可以计算每个连通域的中心对应的纵坐标值，并将纵坐标值依次存入第一向量coo_h。
通过步骤S24可以自适应得到样本点阵列的行数以及每一行的中心位置信息。
步骤S25、在竖直方向(即列方向)上对二值图像im_bin进行形态学膨胀操作，得到第二图像，例如记为im_bin_v。本步骤采用第二膨胀算子进行形态学膨胀操作，其中，第二膨胀算子在竖直方向上的取值可以为生物芯片图像的高度，第二膨胀算子在水平方向上的取值可以为1，比如，第二膨胀算子可以为1*生物芯片图像的高度。然而，本公开对此并不限定。第二膨胀算子可以为其他取值，只要保证通过膨胀操作使得每一列样本点形成一个连通域即可。
通过步骤S25可以在二值图像中形成多个连通域，每一列样本点可以形成一个连通域。
步骤S26、通过对第二图像im_bin_v进行连通域检测，确定样本点阵列的列数以及每一列样本点的中心位置信息(比如，中心坐标值)，得到第二向量coo_v。
在本步骤中，可以利用OpenCV中的findContours函数得到腔室阵列的列数以及对应每一列在水平方向上的中心坐标值(即，每一列中心的横坐标值)，将中心坐标值按从左到右的顺序存入第二向量coo_v。其中，第二向量coo_v可以为一个一维向量，第二向量coo_v内存储的是每列样本点中心的横坐标值，向量长度表示样本点阵列的总列数。然而，本公开对此并不限定。在其他实现方式中，可以采用二维向量记录列编号以及每一列在水平方向上的中心坐标值。
其中，findContours函数的输入可以为第二图像im_bin_v，输出可以是每一个连通域的轮廓点的集合，根据findContours函数的输出信息可以计算每个连通域的中心对应的横坐标值，并将横坐标值依次存入第二向量coo_v。
通过步骤S26可以自适应得到样本点阵列的列数以及每一列的中心位置信息。
步骤S27、初始化样本点检测矩阵sample_loc为零矩阵，其中，样本点检测矩阵sample_loc的行数为步骤S24得到的第一向量coo_h的向量长度，列数为步骤S26得到的第二向量coo_v的向量长度。
本步骤初始化得到的样本点检测矩阵sample_loc可以用于记录后续的阳性样本点的检测结果。
步骤S28、对生物芯片图像im_ori进行二值化处理和形态学操作，得到目标图像，例如记为im_bin_positive。在本步骤中，先对生物芯片图像进行二值化处理，然后对得到的二值图像进行形态学操作，以消除噪点影响，提高对图像噪声的鲁棒性，并增强运算的可靠性。
本步骤中进行的二值化处理所采用的阈值T2大于步骤S22所采用的阈值T1，例如，T2＝T1+50。然而，本公开对此并不限定。
本步骤中的形态学操作可以包括膨胀操作，或者，膨胀操作和腐蚀操作。比如，本步骤的形态学膨胀操作的膨胀算子可以为5*5。然而，本公开对此并不限定。
步骤S29、对于步骤28得到的目标图像im_bin_positive，遍历样本点中心位置对应的像素值以及中心位置的四邻域对应的像素值，根据遍历结果，更新样本点检测矩阵sample_loc。
以目标图像中的白色区域(灰度值为255)代表芯片样本点，黑色区域(灰度值为0)代表芯片背景为例，在目标图像的像素值遍历过程中，当检测到样本点中心位置对应的像素值为255，或者，样本点中心位置的四邻域中至少一个邻域对应的像素值为255，则将样本点检测矩阵sample_loc中该样本点对应位置的元素值赋值为1，否则保持样本点检测矩阵sample_loc中该样本点对应位置的元素值不变，即仍为0。
以目标图像中的白色区域(灰度值为255)代表芯片背景，黑色区域(灰度值为0)代表芯片样本点为例，在目标图像的像素值遍历过程中，当检测到样本点中心位置对应的像素值为0，或者，样本点中心位置的四邻域中至少一个邻域对应的像素值为0，则将样本点检测矩阵sample_loc中该样本点对应位置的元素值赋值为1，否则保持样本点检测矩阵sample_loc中该样本 点对应位置的元素值不变，即仍为0。
在本步骤中，样本点中心位置可以根据步骤S24得到的第一向量coo_h和步骤S26得到第二向量coo_v确定。比如，在第一行第一列的样本点中心位置可以根据第一向量coo_h中第一个坐标值与第二向量coo_v中第一个坐标值来确定，其中，第一向量coo_h中第一个坐标值为样本点中心的纵坐标值，第二向量coo_v中第一个坐标值为样本点中心的横坐标值。同理，第i行第j列的样本点中心位置为第一向量coo_h中第i个坐标值为纵坐标值、第二向量coo_v中第j个坐标值为横坐标值所确定的坐标位置。其中，i、j均为正整数，且i小于或等于总行数，j小于或等于总列数。
本公开对于遍历的样本点中心位置的邻域数目并不限定。比如，在其他实现方式中，可以遍历目标图像的样本点中心位置对应的像素值以及中心位置的八邻域对应的像素值。
步骤S30、根据步骤S29更新后的样本点检测矩阵sample_loc，确定阳性样本点的位置和个数(即样本点检测矩阵sample_loc中的非零元素个数)。其中，样本点位置矩阵sample_loc中元素值为1的位置指示阳性样本点，元素值为0的位置指示阴性样本点。
在一些示例性实施方式中，基于图3所示的生物芯片图像通过本实施例的处理可以得到如下所示的样本点检测矩阵sample_loc：
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1；
1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0；
0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1；
1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0；
0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1；
1,0,0,1,1,0,0,0,0,0,1,1,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0；
1,0,0,0,0,0,1,1,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0；
0,1,1,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,0,0,0,1,1；
0,1,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,1,1,0,0,1,0,1,1,0,0,0；
0,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0,0,1,0；
1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1；
0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0；
0,0,0,0,0,0,0,1,0,1,0,1,1,1,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0；
1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0；
0,1,0,0,0,1,1,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,1,0,0,0,1,1,0；
0,0,1,1,0,0,1,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,1；
0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,1,0,1,1,0,0,0,0,0；
1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1；
1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]
本示例性实施例中，根据样本点检测矩阵sample_loc中的非零元素个数可知，图3所示的生物芯片图像中的阳性样本点个数为137。
在本示例性实施例中，可以根据样本点检测矩阵中非零元素的位置来对阳性样本点进行定位，比如，上述样本点检测矩阵中第二行第一列的元素值为1，则可以确定样本点阵列中第二行第一列的样本点为阳性样本点。
在一些示例性实施方式中，当初始化的样本点检测矩阵sample_loc中元素值均为1，则在本步骤S29中，可以将检测到的阳性样本点的位置对应的元素值更新为0，将阴性样本点的位置对应的元素值保持为1；然后，可以通过统计样本点检测矩阵sample_loc中元素值为0的个数来得到阳性样本点个数。
在一些示例性实施方式中，在得到样本点检测矩阵sample_loc之后，可以将样本点检测矩阵sample_loc的结果叠加到待检测的生物芯片图像，得到如图4所示的可视化结果，以便于直观了解阳性样本点的位置和数目。
本实施例提供的数据处理方法基于图像二值化处理、形态学操作以及连通域检测，借助OpenCV中的函数实现对生物芯片图像中样本点阵列进行自动检测，得到自适应的样本点阵列信息(样本点阵列的行数、列数以及每一样本点的中心位置信息)，并基于样本点阵列信息可以检测到阳性样本点的 位置以及个数。本实施例提供的数据处理方法可以降低计算复杂度，提高运算速度，兼容多种芯片设计，而且，可以有效排除样本点周围噪声的影响，对图像噪声的鲁棒性较佳，从而增加了算法可靠性。
图5为本公开至少一实施例提供的生物芯片的数据处理方法的另一示例流程图。如图5所示，本实施例提供数据处理方法包括步骤S31至步骤S41。本实施例提供的数据处理方法与图2所示的数据处理方法的区别在于：阳性样本点的个数的确定方式不同。
在本示例性实施例中，在步骤S41，基于步骤S38得到的目标图像，利用OpenCV中的findContours函数，得到样本点阵列中阳性样本点的个数。其中，通过findContours函数对目标图像进行连通域检测，检测到的连通域总数即为阳性样本点的个数。
在步骤S40中，根据更新后的样本点检测矩阵sample_loc，对阳性样本点定位。关于阳性样本点的定位方式可以参照图2所示实施例的说明。
本示例性实施例的其余步骤均可以参照图2所示实施例的说明，故于此不再赘述。
图6为本公开至少一实施例提供的生物芯片的数据处理装置的示意图。如图6所示，本实施例提供的数据处理装置，包括：图像获取模块11、二值化处理模块12、形态学操作模块13及第一检测模块14。图像获取模块11，配置为获取待检测的生物芯片图像；二值化处理模块12，配置为对生物芯片图像进行二值化处理，得到二值图像；形态学操作模块13，配置为在行方向上对二值图像进行形态学膨胀操作，得到第一图像，在列方向上对二值图像进行形态学膨胀操作，得到第二图像；第一检测模块14，配置为通过在行方向对第一图像进行连通域检测以及在列方向对第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
在一些示例性实施方式中，形态学操作模块13配置为通过以下方式在行方向上对二值图像进行形态学膨胀操作，得到第一图像：在行方向上根据第一膨胀算子对二值图像进行形态学膨胀操作，得到第一图像，其中，第一图像中行方向上的一个连通域表示一行样本点。形态学操作模块13配置为通过以下方式在列方向上对二值图像进行形态学膨胀操作，得到第二图像：在列 方向上根据第二膨胀算子对二值图像进行形态学膨胀操作，得到第二图像，其中，第二图像中列方向上的一个连通域表示一列的样本点。
在一些示例性实施方式中，第一膨胀算子在行方向上的取值为生物芯片图像的宽度，第二膨胀算子在列方向上的取值为生物芯片图像的高度。
在一些示例性实施方式中，第一检测模块14配置为通过以下方式通过在行方向对第一图像进行连通域检测以及在列方向对第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息：通过检测第一图像中在行方向上的连通域，确定样本点阵列的行数以及每一行样本点在列方向上的中心位置信息；通过检测第二图像中在列方向上的连通域，确定样本点阵列的列数以及每一列样本点在行方向上的中心位置信息；根据每一行样本点在列方向上的中心位置信息以及每一列样本点在行方向上的中心位置信息，得到每一样本点的中心位置信息。
在一些示例性实施方式中，第一检测模块14配置为通过以下方式通过检测第一图像中在行方向上的连通域，确定样本点阵列的行数以及每一行样本点在列方向上的中心位置信息：基于第一图像，利用OpenCV中的findContours函数得到样本点阵列的行数以及每一行样本点在列方向上的中心位置信息。第一检测模块14配置为通过以下方式通过检测第二图像中在列方向上的连通域，确定样本点阵列的列数以及每一列样本点在行方向上的中心位置信息：基于第二图像，利用OpenCV中的findContours函数得到样本点阵列的列数以及每一列样本点在行方向上的中心位置信息。
在一些示例性实施方式中，如图7所示，二值化处理模块12，还配置为对生物芯片图像进行二值化处理，得到目标图像。或者，二值化处理模块12和形态学操作模块13还配置为依次对生物芯片图像进行二值化处理和形态学操作，得到目标图像。其中，得到目标图像进行的二值化处理所采用的阈值大于得到二值图像进行的二值化处理所采用的阈值。
如图7所示，本示例性实施例提供的数据处理装置还可以包括：第二检测模块15，配置为基于样本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行样本点检测，确定阳性样本点的位置信息。
在一些示例性实施方式中，第二检测模块15配置为通过以下方式基于样 本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行样本点检测，确定阳性样本点在样本点阵列中的位置：基于样本点阵列的行数和列数，得到初始化的样本点检测矩阵；基于样本点阵列中每一样本点的中心位置信息，遍历目标图像内每个样本点的中心位置对应的像素值和所述样本点的中心位置的邻域对应的像素值；当检测到目标图像内任一样本点的中心位置对应的像素值或所述样本点的中心位置的邻域对应的像素值为第一数值，则更新样本点检测矩阵内所述样本点对应位置的元素值为第三数值；当检测到目标图像内任一样本点的中心位置对应的像素值或所述样本点的中心位置的邻域对应的像素值为第二数值，则将样本点检测矩阵内所述样本点对应位置的元素值保持为初始值；根据遍历目标图像得到的样本点检测矩阵内第三数值的位置，确定阳性样本点在样本点阵列中的位置。
在一些示例性实施方式中，如图8所示，本实施例的数据处理装置还可以包括：第三检测模块16，配置为基于样本点阵列的行数、列数以及每一样本点的中心位置信息，对目标图像进行样本点检测，确定阳性样本点的个数；或者，基于目标图像，利用OpenCV中的findContours函数得到样本点阵列中阳性样本点的个数。
关于本实施例提供的生物芯片的数据处理装置的相关说明可以参照上述方法实施例的描述，故于此不再赘述。
本公开实施例还提供一种数据处理终端，包括：存储器和处理器，存储器存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的生物芯片的数据处理方法的步骤。
图9为本公开至少一实施例提供的数据处理终端的示例图。如图9所示，在一个示例中，数据处理终端包括：处理器21、存储器22、总线系统23和显示器24，其中，处理器21、存储器22和显示器24通过该总线系统23相连，存储器22配置为存储指令，处理器21配置为执行存储器22存储的指令，以控制显示器24的显示内容。
在一些示例性实施方式中，处理器21可以是中央处理单元(CPU，Central Processing Unit)，处理器21还可以是其他通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑 器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
在一些示例性实施方式中，存储器22可以包括只读存储器和随机存取存储器，并向处理器21提供指令和数据。存储器22的一部分还可以包括非易失性随机存取存储器。例如，存储器22还可以存储设备类型的信息。
在一些示例性实施方式中，总线系统23除包括数据总线之外，还可以包括电源总线、控制总线和状态信号总线等。但是为了清楚说明起见，在图9中将至少一种总线都标为总线系统23。
在一些示例性实施方式中，上述数据处理装置所执行的处理可以通过处理器21中的硬件的集成逻辑电路或者软件形式的指令完成。即本公开实施例所公开的方法的步骤可以体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等存储介质中。该存储介质位于存储器22，处理器21读取存储器22中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。
此外，本公开实施例还提供一种计算机可读介质，存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的数据处理方法的步骤。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些组件或所有组件可以被实施为由处理器，如数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或 其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
以上显示和描述了本公开的基本原理和主要特征和本公开的优点。本公开不受上述实施例的限制，上述实施例和说明书中描述的只是说明本公开的原理，在不脱离本公开精神和范围的前提下，本公开还会有多种变化和改进，这些变化和改进都落入要求保护的本公开范围内。

Claims (13)

  1. 一种生物芯片的数据处理方法，包括：
    获取待检测的生物芯片图像；
    对所述生物芯片图像进行二值化处理，得到二值图像；
    在行方向上对所述二值图像进行形态学膨胀操作，得到第一图像，在列方向上对所述二值图像进行形态学膨胀操作，得到第二图像；
    通过在所述行方向对所述第一图像进行连通域检测以及在所述列方向对所述第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
  2. 根据权利要求1所述的数据处理方法，其中，所述在行方向上对所述二值图像进行形态学膨胀操作，得到第一图像，包括：在所述行方向上根据第一膨胀算子对所述二值图像进行形态学膨胀操作，得到第一图像，其中，所述第一图像中行方向上的一个连通域表示一行样本点；
    所述在列方向上对所述二值图像进行形态学膨胀操作，得到第二图像，包括：在所述列方向上根据第二膨胀算子对所述二值图像进行形态学膨胀操作，得到第二图像，其中，所述第二图像中列方向上的一个连通域表示一列的样本点。
  3. 根据权利要求2所述的数据处理方法，其中，所述第一膨胀算子在行方向上的取值为所述生物芯片图像的宽度，所述第二膨胀算子在列方向上的取值为所述生物芯片图像的高度。
  4. 根据权利要求1所述的数据处理方法，其中，所述通过在所述行方向对所述第一图像进行连通域检测以及在所述列方向对所述第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息，包括：
    通过检测所述第一图像中在所述行方向上的连通域，确定所述样本点阵列的行数以及每一行样本点在列方向上的中心位置信息；
    通过检测所述第二图像中在所述列方向上的连通域，确定所述样本点阵 列的列数以及每一列样本点在行方向上的中心位置信息；
    根据每一行样本点在列方向上的中心位置信息以及每一列样本点在行方向上的中心位置信息，得到每一样本点的中心位置信息。
  5. 根据权利要求4所述的数据处理方法，其中，所述通过检测所述第一图像中在所述行方向上的连通域，确定所述样本点阵列的行数以及每一行样本点在列方向上的中心位置信息，包括：基于所述第一图像，利用开源计算机视觉库OpenCV中的findContours函数得到所述样本点阵列的行数以及每一行样本点在列方向上的中心位置信息；
    所述通过检测所述第二图像中在所述列方向上的连通域，确定所述样本点阵列的列数以及每一列样本点在行方向上的中心位置信息，包括：基于所述第二图像，利用OpenCV中的findContours函数得到所述样本点阵列的列数以及每一列样本点在行方向上的中心位置信息。
  6. 根据权利要求1至5中任一项所述的数据处理方法，还包括：
    对所述生物芯片图像进行二值化处理，得到目标图像，或者，对所述生物芯片图像进行二值化处理和形态学操作，得到目标图像；其中，得到所述目标图像进行的二值化处理所采用的阈值大于得到所述二值图像进行的二值化处理所采用的阈值；
    基于所述样本点阵列的行数、列数以及每一样本点的中心位置信息，对所述目标图像进行样本点检测，确定阳性样本点在样本点阵列中的位置。
  7. 根据权利要求6所述的数据处理方法，其中，所述基于所述样本点阵列的行数、列数以及每一样本点的中心位置信息，对所述目标图像进行样本点检测，确定阳性样本点在样本点阵列中的位置，包括：
    基于所述样本点阵列的行数和列数，得到初始化的样本点检测矩阵；
    基于所述样本点阵列中每一样本点的中心位置信息，遍历所述目标图像内每个样本点的中心位置对应的像素值和所述样本点的中心位置的邻域对应的像素值；当检测到所述目标图像内任一样本点的中心位置对应的像素值或所述样本点的中心位置的邻域对应的像素值为第一数值，则更新所述样本点检测矩阵内所述样本点对应位置的元素值为第三数值；当检测到所述目标图 像内任一样本点的中心位置对应的像素值或所述样本点的中心位置的邻域对应的像素值为第二数值，则将所述样本点检测矩阵内所述样本点对应位置的元素值保持为初始值；
    根据遍历所述目标图像得到的样本点检测矩阵内第三数值的位置，确定阳性样本点在样本点阵列中的位置。
  8. 根据权利要求6所述的数据处理方法，还包括：
    基于所述样本点阵列的行数、列数以及每一样本点的中心位置信息，对所述目标图像进行样本点检测，确定阳性样本点的个数；或者，
    基于所述目标图像，利用开源计算机视觉库OpenCV中的findContours函数得到所述样本点阵列中阳性样本点的个数。
  9. 一种生物芯片的数据处理装置，包括：
    图像获取模块，配置为获取待检测的生物芯片图像；
    二值化处理模块，配置为对所述生物芯片图像进行二值化处理，得到二值图像；
    形态学操作模块，配置为在行方向上对所述二值图像进行形态学膨胀操作，得到第一图像，在列方向上对所述二值图像进行形态学膨胀操作，得到第二图像；
    第一检测模块，配置为通过在所述行方向对所述第一图像进行连通域检测以及在所述列方向对所述第二图像进行连通域检测，确定样本点阵列的行数、列数以及每一样本点的中心位置信息。
  10. 根据权利要求9所述的数据处理装置，其中，所述二值化处理模块，还配置为对所述生物芯片图像进行二值化处理，得到目标图像；或者，所述二值化处理模块和形态学操作模块还配置为依次对所述生物芯片图像进行二值化处理和形态学操作，得到目标图像；其中，得到所述目标图像进行的二值化处理所采用的阈值大于得到所述二值图像进行的二值化处理所采用的阈值；
    所述数据处理装置还包括：第二检测模块，配置为基于所述样本点阵列的行数、列数以及每一样本点的中心位置信息，对所述目标图像进行样本点 检测，确定阳性样本点在样本点阵列中的位置。
  11. 根据权利要求10所述的数据处理装置，还包括：第三检测模块，配置为基于所述样本点阵列的行数、列数以及每一样本点的中心位置信息，对所述目标图像进行样本点检测，确定阳性样本点的个数；或者，基于所述目标图像，利用开源计算机视觉库OpenCV中的findContours函数得到所述样本点阵列中阳性样本点的个数。
  12. 一种数据处理终端，包括：存储器和处理器，所述存储器存储有计算机程序，所述计算机程序被所述处理器执行时实现如权利要求1至8中任一项所述的生物芯片的数据处理方法的步骤。
  13. 一种计算机可读介质，存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至8中任一项所述的生物芯片的数据处理方法的步骤。
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