CN113450336A - Processing method and device of porous fluorescent microarray image, computer equipment and computer readable storage medium - Google Patents
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Abstract
The invention relates to the technical field of digital image processing, and discloses a processing method, a device, computer equipment and a computer readable storage medium of a porous fluorescent microarray image, namely, after filtering and denoising, image binarization processing and morphological processing are sequentially carried out on the porous fluorescent microarray image, a binarization image which is easy to accurately extract a contour can be obtained, the binarization image is divided into a plurality of square interval sub-images, and for each square interval sub-image, by synchronously determining the maximum value of contour coordinates, the center position of the contour, the center coordinates of a round hole and traversing calculation of fluorescence intensity values, not only can the operation speed of an algorithm be improved, but also the accuracy of a finally obtained fluorescence intensity detection result can be ensured, and further the fluorescence intensity data of each single-hole fluorescence area object can be quickly and accurately obtained, the purposes of real-time detection and quantitative analysis can be realized, and the method is convenient for practical application and popularization.
Description
Technical Field
The invention belongs to the technical field of digital image processing, and particularly relates to a method and a device for processing a porous fluorescent microarray image, computer equipment and a computer readable storage medium.
Background
Vogelstein et al first proposed the concept of "digital PCR" at the end of the 20 th century, a breakthrough in absolute quantification of nucleic acid copy numbers. The main principle of digital PCR is to place a single DNA (deoxyribose nucleic Acid) molecule in an independent reaction chamber, perform PCR amplification on the DNA molecule, detect a specific target sequence using a chemical reagent and a dye-labeled probe, and perform statistical calculation by the ratio and number of reaction units representing two signal types to realize absolute quantification of a sample, so digital PCR is also called single-molecule PCR. The detection process of digital PCR mainly comprises two parts, namely PCR amplification and fluorescent signal analysis. In the PCR amplification stage, samples are dispersed into tens of thousands of units (reaction chambers), so that only a single DNA molecule exists in each unit, and the amplification procedure and the amplification system are the same as those of common PCR; in the stage of fluorescent signal analysis, different from a method for performing real-time fluorescence determination on each cycle by a real-time fluorescence quantification technology (qPCR), the digital PCR technology is to collect a fluorescent signal of each reaction unit after amplification is finished, and then directly count or obtain the original concentration or content of a sample by means of Poisson statistics.
Microarray technology has been widely applied to the synchronous detection of biological information, and the complete microarray biochip analysis process includes several parts, such as sample collection, chip preparation, scanning imaging, image processing and data analysis. The specific principle is that after mixing a Taqman probe (an oligonucleotide probe, the 5 'end of which carries a fluorescent group and the 3' end of which carries a quenching group) labeled with fluorescein with template DNA (which can be a single-stranded molecule, a double-stranded molecule, a linear molecule or a circular molecule, and regarding the template DNA, the main factor influencing PCR is the number and purity of the template), the thermal cycle of high-temperature denaturation, low-temperature renaturation and proper-temperature extension and the like are sequentially completed, the Taqman probe which is complementarily matched with the template DNA is cut off according to the polymerase chain reaction rule, so that the fluorescein is dissociated in a reaction system and emits fluorescence under specific light excitation, the amplified target gene segment grows exponentially along with the increase of the cycle number, the fluorescence signal intensity which changes along with the amplification corresponding to the amplified target gene segment is detected in real time, and a plurality of standard products with known template concentrations are used as a reference, the copy number of the target gene of the sample to be detected can be obtained.
At present, image acquisition is mainly performed by a CCD (Charge Coupled Device) camera and an image acquisition card, and a specially designed image processing program is adopted to process the acquired image so as to realize detection of fluorescence signal intensity. However, since the acquired image is generally a multi-well fluorescent microarray image, the detection of the fluorescent signal intensity of each single-well fluorescent region object in the image needs to be performed, and the existing image processing program has the problems of low detection speed and low accuracy, so a new processing scheme suitable for the multi-well fluorescent microarray image is to be provided.
Disclosure of Invention
The invention aims to solve the problems of low detection speed and low accuracy of the existing image processing method for detecting the fluorescence signal intensity of a porous fluorescence microarray image, and provides a novel processing method, a device, computer equipment and a computer readable storage medium which are suitable for the porous fluorescence microarray image, so that the algorithm running speed can be improved, the accuracy of the finally obtained fluorescence intensity detection result can be ensured, the fluorescence intensity data of each single-hole fluorescence area object can be rapidly and accurately obtained, the purposes of real-time detection and quantitative analysis can be realized, and the practical application and popularization are facilitated.
In a first aspect, the present invention provides a method for processing a multi-well fluorescent microarray image, comprising:
acquiring a grayed porous fluorescent microarray image;
carrying out filtering and denoising treatment on the porous fluorescent microarray image to obtain a new porous fluorescent microarray image;
according to the comparison result of the gray value of the pixel point and a preset gray threshold value, carrying out image binarization processing on the new porous fluorescent microarray image to obtain a binarized image;
carrying out image morphological processing on the binary image to obtain a new binary image;
dividing the new binary image into a plurality of square interval sub-images;
for each square interval sub-image in the plurality of square interval sub-images, synchronously executing the following steps respectively:
traversing all pixel points in the sub-image in the square interval, and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge profile according to the binarization values of the pixel points;
determining the contour center coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value;
judging whether the contour center coordinate is a circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to a comparison result of the distance from the contour center coordinate to a preset circular hole center coordinate and a preset distance threshold;
if so, determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing averaging calculation on the gray value obtained by traversing to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
Based on the content of the invention, a new processing scheme suitable for a porous fluorescent microarray image is provided, namely, after filtering and denoising processing, image binarization processing and morphological processing are sequentially carried out on the porous fluorescent microarray image, a binarized image which is easy to accurately extract a contour can be obtained, the binarized image is divided into a plurality of square interval sub-images, and aiming at each square interval sub-image, by synchronously determining the maximum value of contour coordinates, determining the center position of the contour, judging the center coordinates of a circular hole and traversing calculation of fluorescence intensity values, the operation speed of an algorithm can be improved, the accuracy of a finally obtained fluorescence intensity detection result can be ensured, further, the fluorescence intensity data of each single-hole fluorescence area object can be rapidly and accurately obtained, and the purposes of real-time detection and quantitative analysis can be realized, is convenient for practical application and popularization.
In one possible design, the filtering and denoising processing is performed on the porous fluorescence microarray image to obtain a new porous fluorescence microarray image, and the method includes:
and performing median filtering processing on the porous fluorescent microarray image by using a two-dimensional sliding window containing 5 x 5 pixel points to obtain the new porous fluorescent microarray image.
In one possible design, performing image morphology processing on the binarized image to obtain a new binarized image, including:
and sequentially carrying out noise target removal processing, expansion first and then corrosion processing and/or hole filling processing on the binary image to obtain the new binary image.
In one possible design, the number of images of the plurality of square interval sub-images is greater than or equal to the number of well sites in the image of the multi-well fluorescent microarray.
In one possible design, traversing all pixel points in the sub-image in the square interval, and determining the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value of the edge contour according to the binarization value of the pixel points, wherein the method comprises the following steps:
initializing the minimum abscissa value to a value greater than or equal to the maximum abscissa value in the sub-image within the square interval, and initializing the minimum ordinate value to a value greater than or equal to the maximum ordinate value in the sub-image within the square interval, and initializing the maximum abscissa value to a value less than or equal to the minimum abscissa value in the sub-image within the square interval, and initializing the maximum ordinate value to a value less than or equal to the minimum ordinate value in the sub-image within the square interval;
sequentially traversing each pixel point in the sub-image in the square interval, and when the binarization value of the pixel point is a non-zero value, the following steps are carried out:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
In one possible design, after obtaining the calculation result for reflecting the fluorescence intensity of the single-hole fluorescence area object, the method further includes:
and marking the fluorescence intensity numerical value of the single-hole fluorescence area object in the multi-hole fluorescence microarray image according to the central coordinate of the circular hole to obtain a fluorescence value marked image.
In a second aspect, the invention provides a processing device for a porous fluorescence microarray image, which comprises an image acquisition module, a filtering processing module, a binarization processing module, a morphology processing module, an image segmentation module and a fluorescence intensity calculation module which are sequentially in communication connection, wherein the fluorescence intensity calculation module comprises a first traversal submodule, a central coordinate determination submodule, a comparison and judgment submodule and a second traversal submodule which are sequentially in communication connection;
the image acquisition module is used for acquiring a grayed porous fluorescent microarray image;
the filtering processing module is used for carrying out filtering and denoising processing on the porous fluorescent microarray image to obtain a new porous fluorescent microarray image;
the binarization processing module is used for carrying out image binarization processing on the new porous fluorescent microarray image according to the comparison result of the gray value of the pixel point and a preset gray threshold value to obtain a binarized image;
the morphology processing module is used for performing morphology processing on the binary image to obtain a new binary image;
the image segmentation module is used for segmenting the new binarization image into a plurality of square interval sub-images;
the fluorescence intensity calculation module is used for respectively and sequentially starting the first traversal submodule, the center coordinate determination submodule, the comparison judgment submodule and the second traversal submodule aiming at each square interval sub-image in the plurality of square interval sub-images;
the first traversal submodule is used for traversing all pixel points in the sub-image in the square interval and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge contour according to the binarization values of the pixel points;
the central coordinate determination submodule is used for determining the contour central coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value;
the comparison and judgment submodule is used for judging whether the contour center coordinate is the circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to the comparison result of the distance from the contour center coordinate to the preset circular hole center coordinate and a preset distance threshold value;
and the second traversal submodule is used for determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole when the central coordinate of the outline is judged to be the central coordinate of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing averaging calculation on the traversed gray value to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
In one possible design, the first traversal submodule comprises a coordinate value initialization grandchild module and a coordinate value updating grandchild module which are connected in a communication mode;
the coordinate value initializing module is configured to initialize the minimum abscissa value to a value greater than or equal to a maximum abscissa value in the sub-image between the squares, initialize the minimum ordinate value to a value greater than or equal to a maximum ordinate value in the sub-image between the squares, initialize the maximum abscissa value to a value less than or equal to a minimum abscissa value in the sub-image between the squares, and initialize the maximum ordinate value to a value less than or equal to a minimum ordinate value in the sub-image between the squares;
and the coordinate value updating sun module is used for sequentially traversing each pixel point in the sub-image in the square interval, and when the binary value of the pixel point is a non-zero value, the coordinate value updating sun module comprises:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
In a third aspect, the present invention provides a computer device, comprising a memory, a processor and a transceiver, which are sequentially connected in communication, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and executing the processing method of the image of the multi-well fluorescent microarray as described in the first aspect or any one of the possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions which, when executed on a computer, perform a method of processing an image of the multi-well fluorescent microarray as described in the first aspect or any one of the possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform a method of processing an image of the multi-well fluorescent microarray as described in the first aspect or any one of the possible designs of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a processing method of a multi-well fluorescent microarray image provided by the present invention.
FIG. 2 is a process diagram of an example of the image processing of a multi-well fluorescent microarray provided by the present invention.
FIG. 3 is an exemplary graph of the labeling results of fluorescence intensity provided by the present invention.
FIG. 4 is a schematic structural diagram of a processing apparatus for multi-well fluorescent microarray images provided by the present invention.
Fig. 5 is a schematic structural diagram of a computer device provided by the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely representative of exemplary embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It will be understood that, although the terms first, second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, a first object may be referred to as a second object, and similarly, a second object may be referred to as a first object, without departing from the scope of example embodiments of the present invention.
It should be understood that, for the term "and/or" as may appear herein, it is merely an associative relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone or A and B exist at the same time; for the term "/and" as may appear herein, which describes another associative object relationship, it means that two relationships may exist, e.g., a/and B, may mean: a exists singly or A and B exist simultaneously; in addition, for the character "/" that may appear herein, it generally means that the former and latter associated objects are in an "or" relationship.
As shown in fig. 1, the processing method of the multi-well fluorescent microarray image provided in the first aspect of the present embodiment may be, but is not limited to, executed by a computer device communicatively connected to a CCD camera and an image acquisition card, so that after receiving the acquired multi-well fluorescent microarray image, the fluorescence intensity data of each single-well fluorescent area object can be rapidly and accurately acquired, and the purpose of real-time detection and quantitative analysis is achieved. The processing method of the multi-well fluorescent microarray image may include, but is not limited to, the following steps S1 to S6.
S1, obtaining a grayed porous fluorescent microarray image.
In step S1, the porous fluorescent microarray image is an image acquired by a CCD camera and an image acquisition card in microarray technology, and may be a real-time image (i.e., an image transmitted from the CCD camera and the image acquisition card in real time) or a non-real-time image (i.e., an image downloaded from a web server or read from a local database and acquired in history). As shown in FIG. 2, for example, an original multi-well fluorescent microarray image with 81 wells (actually 48 wells corresponding to a single-well fluorescent region object, respectively, and the rest being reserved wells) can be downloaded from a data network. In order to facilitate subsequent image binarization processing and fluorescence intensity calculation, if the obtained original porous fluorescence microarray image is a color image, graying processing needs to be performed on the color image to obtain a grayed porous fluorescence microarray image; if the acquired original porous fluorescent microarray image is a grayed image, the graying process can be skipped.
S2, filtering and denoising the porous fluorescent microarray image to obtain a new porous fluorescent microarray image.
In step S2, the filtering and denoising process is a conventional image preprocessing method to remove noise. Preferably, a two-dimensional sliding window with 5 × 5 pixels may be used to perform median filtering on the porous fluorescent microarray image, so as to obtain the new porous fluorescent microarray image. The median filtering process is to adopt a median filtering method to carry out filtering and denoising, wherein the median filtering method is a nonlinear smoothing technology, the gray value of each pixel point is set as the median of the gray values of all the pixel points in a certain neighborhood window of the point, and the surrounding pixel values are close to the real values, so that the isolated noise point is eliminated. The median filtering method needs to use a two-dimensional sliding template (i.e. the two-dimensional sliding window is usually a 3 × 3 or 5 × 5 region, and may also be different shapes, such as a line, a circle, a cross, or a circular ring, etc.) with a certain structure, so as to sort the pixels in the template according to the size of the pixel values, and generate a two-dimensional data sequence with monotonously rising (or falling). The median filtering method is characterized in that partial noise is filtered while the edges are protected, so that peak-spike interference can be effectively eliminated, and the accuracy of subsequent detection is improved. In addition, the new image of the multi-well fluorescent microarray obtained by filtering and denoising can be shown in fig. 2.
And S3, carrying out image binarization processing on the new porous fluorescent microarray image according to the comparison result of the gray value of the pixel point and a preset gray threshold value to obtain a binarized image.
In the step S3, the image binarization processing is a conventional image preprocessing manner, that is, the gray value of the pixel on the image is set to be 0 (black) or 255 (white), specifically, the gray value of the pixel whose gray value is smaller than the preset gray threshold is updated to be 0, and the gray value of the pixel whose gray value is greater than or equal to the preset gray threshold is updated to be a non-zero value (for example, 255), that is, the whole image exhibits an obvious black-and-white effect. The preset gray level threshold may be 127. Further, the binarized image obtained by the image binarizing process may be as shown in fig. 2.
And S4, carrying out image morphological processing on the binary image to obtain a new binary image.
In step S4, the new binarized image may be obtained by, but not limited to, sequentially performing image noise target removal processing, expansion-first-then-etching processing, and/or hole filling processing on the binarized image. The noise target removal process, i.e. the small target removal process, is a conventional image morphology processing manner, and may be implemented by, but not limited to, calling a bweareaopen () function in MATLAB software, so as to delete the small-area contour object in advance. The dilation-prior-erosion processing and the hole filling processing are also conventional image morphology processing manners, and can be realized by but not limited to calling an imclose () function and an imfill () function in MATLAB software respectively. Further, the new binarized image obtained by morphological processing of the image may be as shown in fig. 2.
And S5, segmenting the new binarization image into a plurality of square interval sub-images.
In the step S5, the square interval sub-image is a square grid image, and the number of the square interval sub-images is preferably greater than or equal to the number of the hole sites in the porous fluorescent microarray image, so that each hole site corresponds to at least one square interval sub-image, which is beneficial to performing subsequent synchronous traversal calculation, increasing the operation speed of the algorithm, and rapidly obtaining the fluorescent intensity detection result. For example, for a multiwell fluorescent microarray image with 81 well sites, 81 square interval sub-images can be segmented.
S6, respectively but not limited to, synchronously executing the following steps S61-S64 for each square interval sub-image in the plurality of square interval sub-images.
S61, traversing all pixel points in the sub-image in the square interval, and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge contour according to the binarization values of the pixel points.
In step S61, the edge contour may be, but is not limited to, an edge contour of a single-well fluorescence area object in the multi-well fluorescence microarray image, and specifically, the determination method includes, but is not limited to, the following steps S611 to S612.
S611, initializing the minimum abscissa value to be a numerical value which is larger than or equal to the maximum abscissa value in the sub-image in the square interval, initializing the minimum ordinate value to be a numerical value which is larger than or equal to the maximum ordinate value in the sub-image in the square interval, initializing the maximum abscissa value to be a numerical value which is smaller than or equal to the minimum abscissa value in the sub-image in the square interval, and initializing the maximum ordinate value to be a numerical value which is smaller than or equal to the minimum ordinate value in the sub-image in the square interval.
In the step S611, the minimum abscissa value, the minimum ordinate value, the maximum abscissa value, and the maximum ordinate value and all subsequent coordinate values are coordinate values in the image of the porous fluorescent microarray.
S612, sequentially traversing each pixel point in the square interval sub-image, and when the binarization value of the pixel point is a non-zero value (for example 255), the following steps are performed:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
After the step S612, if the finally obtained minimum abscissa value is greater than the maximum abscissa value and/or the minimum ordinate value is greater than the maximum ordinate value, it indicates that there is no edge profile, i.e. the square interval sub-image is a completely black image, and there is no corresponding single-hole fluorescence area object, and the subsequent steps S62 to S64 may not be executed.
S62, determining the contour center coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value.
In the step S62, the abscissa value of the contour center coordinate is an average of the minimum abscissa value and the maximum abscissa value, and the ordinate value of the contour center coordinate is an average of the minimum ordinate value and the maximum ordinate value.
S63, judging whether the contour center coordinate is the circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to the comparison result of the distance from the contour center coordinate to the preset circular hole center coordinate and a preset distance threshold value.
In step S63, the coordinates of the center of the predetermined circular hole are the coordinates of the circular hole on the predetermined template on the porous fluorescent microarray image. If the distance is smaller than or equal to the preset distance threshold, the offset of the edge contour and the contour of the circular hole position is small, and the contour center coordinate can be determined to be the actual circular hole center coordinate corresponding to the single-hole fluorescent area object; conversely and otherwise, the edge profile may be considered to be interference noise. Furthermore, the preset distance threshold may be determined according to multiple actual measurements, for example, one quarter of the radius value of the circular hole location.
And S64, if so, determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing average calculation on the gray value obtained by traversing to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
After step S64, in order to display the fluorescence intensity data in the multi-well fluorescence microarray image, after obtaining the calculation result for reflecting the fluorescence intensity of the single-well fluorescence area object, the method further includes, but is not limited to: and marking the fluorescence intensity numerical value of the single-hole fluorescence area object in the multi-hole fluorescence microarray image according to the central coordinate of the circular hole to obtain a fluorescence value marked image. Specifically, the fluorescence intensity value is marked around the center coordinates of the circular hole, such as an area directly above, an area directly below, an area directly to the left, or an area directly to the right. The resulting fluorescence value signature image of the final signature may be as shown in fig. 3.
Therefore, based on the processing method described in the foregoing steps S1 to S6, a new processing scheme suitable for a porous fluorescent microarray image is provided, that is, after filtering and denoising, image binarization and morphological processing are sequentially performed on the porous fluorescent microarray image, a binarized image which is easy to accurately extract a contour can be obtained, the binarized image is divided into a plurality of square interval sub-images, and for each square interval sub-image, by synchronously performing contour coordinate maximum determination, contour center position determination, circular hole center coordinate determination and fluorescence intensity value traversal calculation, not only the algorithm operation speed can be increased, but also the accuracy of the finally obtained fluorescence intensity detection result can be ensured, and further the fluorescence intensity data of each single-hole fluorescence area object can be rapidly and accurately obtained, and the purposes of real-time detection and quantitative analysis can be achieved, is convenient for practical application and popularization.
As shown in fig. 4, a second aspect of the present embodiment provides a virtual device for implementing the processing method of the porous fluorescent microarray image according to the first aspect, including an image obtaining module, a filtering processing module, a binarization processing module, a morphology processing module, an image segmentation module, and a fluorescence intensity calculating module, which are sequentially and communicatively connected, where the fluorescence intensity calculating module includes a first traversal submodule, a center coordinate determining submodule, a comparison and judgment submodule, and a second traversal submodule, which are sequentially and communicatively connected;
the image acquisition module is used for acquiring a grayed porous fluorescent microarray image;
the filtering processing module is used for carrying out filtering and denoising processing on the porous fluorescent microarray image to obtain a new porous fluorescent microarray image;
the binarization processing module is used for carrying out image binarization processing on the new porous fluorescent microarray image according to the comparison result of the gray value of the pixel point and a preset gray threshold value to obtain a binarized image;
the morphology processing module is used for performing morphology processing on the binary image to obtain a new binary image;
the image segmentation module is used for segmenting the new binarization image into a plurality of square interval sub-images;
the fluorescence intensity calculation module is used for respectively and sequentially starting the first traversal submodule, the center coordinate determination submodule, the comparison judgment submodule and the second traversal submodule aiming at each square interval sub-image in the plurality of square interval sub-images;
the first traversal submodule is used for traversing all pixel points in the sub-image in the square interval and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge contour according to the binarization values of the pixel points;
the central coordinate determination submodule is used for determining the contour central coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value;
the comparison and judgment submodule is used for judging whether the contour center coordinate is the circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to the comparison result of the distance from the contour center coordinate to the preset circular hole center coordinate and a preset distance threshold value;
and the second traversal submodule is used for determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole when the central coordinate of the outline is judged to be the central coordinate of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing averaging calculation on the traversed gray value to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
In one possible design, the first traversal submodule comprises a coordinate value initialization grandchild module and a coordinate value updating grandchild module which are connected in a communication mode;
the coordinate value initializing module is configured to initialize the minimum abscissa value to a value greater than or equal to a maximum abscissa value in the sub-image between the squares, initialize the minimum ordinate value to a value greater than or equal to a maximum ordinate value in the sub-image between the squares, initialize the maximum abscissa value to a value less than or equal to a minimum abscissa value in the sub-image between the squares, and initialize the maximum ordinate value to a value less than or equal to a minimum ordinate value in the sub-image between the squares;
and the coordinate value updating sun module is used for sequentially traversing each pixel point in the sub-image in the square interval, and when the binary value of the pixel point is a non-zero value, the coordinate value updating sun module comprises:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
For the working process, working details and technical effects of the foregoing apparatus provided in the second aspect of this embodiment, reference may be made to the processing method described in the first aspect, which is not described herein again.
As shown in fig. 5, a third aspect of the present embodiment provides a computer device for performing the processing method of the multi-well fluorescent microarray image according to the first aspect, comprising a memory, a processor and a transceiver, which are sequentially and communicatively connected, wherein the memory is used for storing a computer program, the transceiver is used for transmitting and receiving messages, and the processor is used for reading the computer program and performing the processing method of the multi-well fluorescent microarray image according to the first aspect. For example, the Memory may include, but is not limited to, a Random-Access Memory (RAM), a Read-Only Memory (ROM), a Flash Memory (Flash Memory), a First-in First-out (FIFO), and/or a First-in Last-out (FILO), and the like; the transceiver may be, but is not limited to, a WiFi (wireless fidelity) wireless transceiver, a bluetooth wireless transceiver, a GPRS (General Packet Radio Service) wireless transceiver, and/or a ZigBee (ZigBee protocol, low power consumption local area network protocol based on ieee802.15.4 standard) wireless transceiver, etc.; the processor may not be limited to the use of a microprocessor of the model number STM32F105 family. In addition, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.
For the working process, working details, and technical effects of the foregoing computer device provided in the third aspect of this embodiment, reference may be made to the processing method in the first aspect, which is not described herein again.
This embodiment provides, in a fourth aspect, a computer-readable storage medium storing instructions including the method for processing a multi-well fluorescent microarray image according to the first aspect, i.e., the computer-readable storage medium storing instructions which, when executed on a computer, perform the method for processing a multi-well fluorescent microarray image according to the first aspect. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
For a working process, working details, and technical effects of the foregoing computer-readable storage medium provided in the fourth aspect of this embodiment, reference may be made to the processing method in the first aspect, which is not described herein again.
A fifth aspect of the present embodiment provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of processing a multi-well fluorescent microarray image as described in the first aspect. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices.
Finally, it should be noted that the present invention is not limited to the above alternative embodiments, and that various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. A method for processing a multi-well fluorescent microarray image, comprising:
acquiring a grayed porous fluorescent microarray image;
carrying out filtering and denoising treatment on the porous fluorescent microarray image to obtain a new porous fluorescent microarray image;
according to the comparison result of the gray value of the pixel point and a preset gray threshold value, carrying out image binarization processing on the new porous fluorescent microarray image to obtain a binarized image;
carrying out image morphological processing on the binary image to obtain a new binary image;
dividing the new binary image into a plurality of square interval sub-images;
for each square interval sub-image in the plurality of square interval sub-images, synchronously executing the following steps respectively:
traversing all pixel points in the sub-image in the square interval, and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge profile according to the binarization values of the pixel points;
determining the contour center coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value;
judging whether the contour center coordinate is a circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to a comparison result of the distance from the contour center coordinate to a preset circular hole center coordinate and a preset distance threshold;
if so, determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing averaging calculation on the gray value obtained by traversing to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
2. The process of claim 1, wherein the step of filtering and denoising the fluorescence microarray image to obtain a new fluorescence microarray image comprises:
and performing median filtering processing on the porous fluorescent microarray image by using a two-dimensional sliding window containing 5 x 5 pixel points to obtain the new porous fluorescent microarray image.
3. The processing method as claimed in claim 1, wherein performing image morphology processing on the binarized image to obtain a new binarized image comprises:
and sequentially carrying out noise target removal processing, expansion first and then corrosion processing and/or hole filling processing on the binary image to obtain the new binary image.
4. The process of claim 1, wherein the number of images in the plurality of square interval sub-images is greater than or equal to the number of wells in the multiwell fluorescent microarray image.
5. The processing method of claim 1, wherein traversing all pixel points in the sub-image within the square interval, determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge profile according to the binarized values of the pixel points, comprises:
initializing the minimum abscissa value to a value greater than or equal to the maximum abscissa value in the sub-image within the square interval, and initializing the minimum ordinate value to a value greater than or equal to the maximum ordinate value in the sub-image within the square interval, and initializing the maximum abscissa value to a value less than or equal to the minimum abscissa value in the sub-image within the square interval, and initializing the maximum ordinate value to a value less than or equal to the minimum ordinate value in the sub-image within the square interval;
sequentially traversing each pixel point in the sub-image in the square interval, and when the binarization value of the pixel point is a non-zero value, the following steps are carried out:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
6. The process of claim 1, wherein after obtaining the calculation reflecting the fluorescence intensity of the single-well fluorescence area object, the process further comprises:
and marking the fluorescence intensity numerical value of the single-hole fluorescence area object in the multi-hole fluorescence microarray image according to the central coordinate of the circular hole to obtain a fluorescence value marked image.
7. The processing device of the porous fluorescence microarray image is characterized by comprising an image acquisition module, a filtering processing module, a binarization processing module, a morphology processing module, an image segmentation module and a fluorescence intensity calculation module which are sequentially in communication connection, wherein the fluorescence intensity calculation module comprises a first traversal submodule, a central coordinate determination submodule, a comparison judgment submodule and a second traversal submodule which are sequentially in communication connection;
the image acquisition module is used for acquiring a grayed porous fluorescent microarray image;
the filtering processing module is used for carrying out filtering and denoising processing on the porous fluorescent microarray image to obtain a new porous fluorescent microarray image;
the binarization processing module is used for carrying out image binarization processing on the new porous fluorescent microarray image according to the comparison result of the gray value of the pixel point and a preset gray threshold value to obtain a binarized image;
the morphology processing module is used for performing morphology processing on the binary image to obtain a new binary image;
the image segmentation module is used for segmenting the new binarization image into a plurality of square interval sub-images;
the fluorescence intensity calculation module is used for respectively and sequentially starting the first traversal submodule, the center coordinate determination submodule, the comparison judgment submodule and the second traversal submodule aiming at each square interval sub-image in the plurality of square interval sub-images;
the first traversal submodule is used for traversing all pixel points in the sub-image in the square interval and determining a minimum abscissa value, a minimum ordinate value, a maximum abscissa value and a maximum ordinate value of the edge contour according to the binarization values of the pixel points;
the central coordinate determination submodule is used for determining the contour central coordinate of the edge contour according to the minimum abscissa value, the minimum ordinate value, the maximum abscissa value and the maximum ordinate value;
the comparison and judgment submodule is used for judging whether the contour center coordinate is the circular hole center coordinate corresponding to the single-hole fluorescent area object or not according to the comparison result of the distance from the contour center coordinate to the preset circular hole center coordinate and a preset distance threshold value;
and the second traversal submodule is used for determining a circular hole area according to the central coordinate of the circular hole and the known radius of the circular hole when the central coordinate of the outline is judged to be the central coordinate of the circular hole, traversing all pixel points in the circular hole area in the porous fluorescent microarray image, and performing averaging calculation on the traversed gray value to obtain a calculation result for reflecting the fluorescence intensity of the single-hole fluorescent area object.
8. The processing apparatus as set forth in claim 7, wherein the first traversal submodule includes a coordinate value initialization grandchild module and a coordinate value update grandchild module communicatively coupled;
the coordinate value initializing module is configured to initialize the minimum abscissa value to a value greater than or equal to a maximum abscissa value in the sub-image between the squares, initialize the minimum ordinate value to a value greater than or equal to a maximum ordinate value in the sub-image between the squares, initialize the maximum abscissa value to a value less than or equal to a minimum abscissa value in the sub-image between the squares, and initialize the maximum ordinate value to a value less than or equal to a minimum ordinate value in the sub-image between the squares;
and the coordinate value updating sun module is used for sequentially traversing each pixel point in the sub-image in the square interval, and when the binary value of the pixel point is a non-zero value, the coordinate value updating sun module comprises:
if the abscissa value of the pixel point is larger than the current value of the maximum abscissa value, updating the maximum abscissa value to the abscissa value of the pixel point;
if the ordinate value of the pixel point is larger than the current value of the maximum ordinate value, updating the maximum ordinate value to the ordinate value of the pixel point;
if the abscissa value of the pixel point is smaller than the current value of the minimum abscissa value, updating the minimum abscissa value to the abscissa value of the pixel point;
and if the ordinate value of the pixel point is smaller than the current value of the minimum ordinate value, updating the minimum ordinate value to the ordinate value of the pixel point.
9. Computer device, comprising a memory, a processor and a transceiver, which are in communication connection in this order, wherein the memory is used for storing a computer program, the transceiver is used for sending and receiving messages, and the processor is used for reading the computer program and executing the processing method according to any one of claims 1 to 6.
10. A computer-readable storage medium having stored thereon instructions for performing the processing method of any one of claims 1 to 6 when the instructions are run on a computer.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117274267A (en) * | 2023-11-22 | 2023-12-22 | 合肥晶合集成电路股份有限公司 | Automatic detection method and device for mask layout, processor and electronic equipment |
| CN117671677A (en) * | 2024-02-02 | 2024-03-08 | 吉林省星博医疗器械有限公司 | Fluorescent microarray identification analysis method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050164187A1 (en) * | 2002-01-18 | 2005-07-28 | Dougherty Edward R. | Simulating microarrays using a parameterized model |
| US20100111396A1 (en) * | 2008-11-06 | 2010-05-06 | Los Alamos National Security | Object and spatial level quantitative image analysis |
| CN105705659A (en) * | 2013-08-28 | 2016-06-22 | 赛卢拉研究公司 | Massively parallel single cell analysis |
| CN107274349A (en) * | 2017-06-05 | 2017-10-20 | 新疆大学 | Biological chip fluorescent is as the determination method and device at angle of inclination |
| CN109215026A (en) * | 2018-09-29 | 2019-01-15 | 广东工业大学 | A kind of accurate LED defect inspection method of high speed based on machine vision |
| CN109563130A (en) * | 2016-04-07 | 2019-04-02 | 卡斯西部储备大学 | For treating the TDP-43 mitochondria positioning inhibitor of neurodegenerative disease |
-
2021
- 2021-07-01 CN CN202110745910.2A patent/CN113450336B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050164187A1 (en) * | 2002-01-18 | 2005-07-28 | Dougherty Edward R. | Simulating microarrays using a parameterized model |
| US20100111396A1 (en) * | 2008-11-06 | 2010-05-06 | Los Alamos National Security | Object and spatial level quantitative image analysis |
| CN105705659A (en) * | 2013-08-28 | 2016-06-22 | 赛卢拉研究公司 | Massively parallel single cell analysis |
| CN109563130A (en) * | 2016-04-07 | 2019-04-02 | 卡斯西部储备大学 | For treating the TDP-43 mitochondria positioning inhibitor of neurodegenerative disease |
| CN107274349A (en) * | 2017-06-05 | 2017-10-20 | 新疆大学 | Biological chip fluorescent is as the determination method and device at angle of inclination |
| CN109215026A (en) * | 2018-09-29 | 2019-01-15 | 广东工业大学 | A kind of accurate LED defect inspection method of high speed based on machine vision |
Non-Patent Citations (3)
| Title |
|---|
| M. PARTHASARATHY: "An Adaptive Segmentation Method Based on Gaussian Mixture Model (GMM) Clustering for DNA Microarray", 《2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS》 * |
| 李红卫: "基于数学形态学的微阵列芯片荧光图像处理与分析", 《传感技术学报》 * |
| 陆恩泽: "基于CCD的新型显色生物芯片识读仪的研发", 《中国优秀博硕士学位论文全文数据库（硕士）信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117274267A (en) * | 2023-11-22 | 2023-12-22 | 合肥晶合集成电路股份有限公司 | Automatic detection method and device for mask layout, processor and electronic equipment |
| CN117274267B (en) * | 2023-11-22 | 2024-04-05 | 合肥晶合集成电路股份有限公司 | Automatic detection method and device for mask layout, processor and electronic equipment |
| CN117671677A (en) * | 2024-02-02 | 2024-03-08 | 吉林省星博医疗器械有限公司 | Fluorescent microarray identification analysis method and system |
| CN117671677B (en) * | 2024-02-02 | 2024-04-30 | 吉林省星博医疗器械有限公司 | Fluorescent microarray identification analysis method and system |
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