CN114967093B - Automatic focusing method and system based on microscopic hyperspectral imaging platform - Google Patents
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Abstract
The invention discloses an automatic focusing method and system based on microscopic hyperspectral images, wherein the method comprises the following steps: acquiring a downsampled image; based on the downsampled image, obtaining the field of view definition, judging the field of view definition, based on a judging result, obtaining an image sequence, based on the image sequence, obtaining a forward difference value sequence and a reverse difference value sequence, and based on the forward difference value sequence and the reverse difference value sequence, obtaining the clearest image position. Through the technical scheme, accuracy and instantaneity of acquiring hyperspectral images can be improved, and an optimal focusing position of a positioning object can be acquired.
Description
Technical Field
The invention relates to the technical field of microscopic imaging, in particular to an automatic focusing method and system based on a microscopic hyperspectral imaging platform.
Background
The hyperspectral image has abundant spatial information and spectrum information, and provides a new idea for classifying and identifying objects. However, the spectral range of conventional microscopes is very limited, so that the application of microscopic hyperspectral imaging techniques in the medical field is necessary. It is conventional practice to acquire hyperspectral image data field by field due to uneven thickness of the imaged object and using a digital scanning microscope. The location of the focal plane of the different areas of the object being imaged is unknown, but the focal plane in the adjacent areas of the object is within a certain range. Therefore, improvements in obtaining clear images of objects and in obtaining time for clear images are needed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an automatic focusing method and an automatic focusing system based on a microscopic hyperspectral imaging platform, which are used for improving the accuracy and the instantaneity of acquiring hyperspectral images and acquiring the optimal focusing position of a positioning object.
In order to achieve the technical purpose, the invention provides an automatic focusing method based on a microscopic hyperspectral image, which comprises the following steps:
acquiring a downsampled image; based on the downsampled image, obtaining the field of view definition, judging the field of view definition, based on a judging result, obtaining an image sequence, based on the image sequence, obtaining a forward difference value sequence and a reverse difference value sequence, and based on the forward difference value sequence and the reverse difference value sequence, obtaining the clearest image position.
Optionally, the process of acquiring the downsampled image includes:
setting acquisition conditions, wherein the acquisition conditions comprise a band range, a band number, exposure time, objective lens multiples and image labels;
and (3) based on the acquisition conditions, downsampling the monoscopic image to obtain a downsampled image.
Optionally, the process of obtaining the field of view definition includes:
and calculating the downsampled image through the Laplacian operator to obtain the field definition.
Optionally, the process of acquiring the image sequence includes:
and judging the field of view definition, if the field of view definition is greater than an expected value, moving the electric stage of the mobile microscope up and down, and acquiring an image sequence in a first range, otherwise, moving the lens up and down, and acquiring a downsampled image sequence in a second range, calculating a Laplace evaluation value based on the downsampled image sequence, moving the electric stage of the mobile microscope to the position of the maximum value of the Laplace evaluation value, and moving the electric stage of the mobile microscope up and down, and acquiring the image sequence in the first range.
Optionally, the process of obtaining the clearest position includes:
and filtering the forward difference value sequence and the reverse difference value sequence through the Laplacian operator, and taking the median value of the filtering result and the first image position in the image sequence as the clearest image position.
On the other hand, in order to better achieve the above technical objects, the present invention provides an auto-focusing system based on microscopic hyperspectral images, comprising:
a control computer, an optical microscope, a movable microscope electric stage, a beam splitter, an acousto-optic tunable filter, a gray level camera and a color camera;
the gray-scale camera and the color camera are used for acquiring downsampled images in the optical microscope, wherein the downsampled images comprise color images and gray-scale images;
the control computer is used for controlling the movement of the electric object stage of the mobile microscope, acquiring the field of view definition based on the downsampled image, judging the field of view definition, acquiring an image sequence based on a judging result, acquiring a forward difference value sequence and a reverse difference value sequence based on the image sequence, and acquiring the clearest image position based on the forward difference value sequence and the reverse difference value sequence;
the acousto-optic tunable filter is used for displaying downsampled images and image sequences in an optical microscope in real time.
Optionally, setting acquisition conditions of a gray level camera and a color camera, wherein the acquisition conditions comprise a band range, a band number, exposure time, objective lens multiples and image labels; and (3) based on the acquisition conditions, downsampling the monoscopic image to obtain a downsampled image.
Optionally, the control computer includes a calculation module, where the calculation module is configured to calculate the downsampled image by using the laplace operator to obtain the field of view definition.
Optionally, the control computer includes a judging module, the judging module is configured to judge the field of view definition, if the field of view definition is greater than an expected value, move the moving microscope electric stage up and down, and acquire an image sequence in a first range, otherwise move the lens up and down, and acquire a downsampled image sequence in a second range, calculate a laplace evaluation value based on the downsampled image sequence, move the moving microscope electric stage to a maximum value of the laplace evaluation value, and move the moving microscope electric stage up and down, and acquire the image sequence in the first range.
Optionally, the control computer includes a processing module, where the processing module is configured to filter the forward difference value sequence and the reverse difference value sequence through a laplace operator, and take a median value between a filtering result and a first image position in the image sequence as a clearest image position.
The invention has the following technical effects:
according to the microscopic hyperspectral image-based automatic focusing method, the definition operand is effectively reduced through image downsampling, and the efficiency is further improved through difference value operation. The cost investment of manual focusing is reduced, a clear view field can be efficiently and accurately obtained, and the shooting efficiency of microscopic digital images is efficiently and accurately improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the present invention provides an auto-focusing method based on microscopic hyperspectral image, comprising:
moving the electric object stage of the movable microscope to the field of view of interest, acquiring a downsampled image of the monoscopic field and storing the downsampled image into a memory;
calculating the definition of the current view field through the Laplacian operator, wherein the definition is larger than 6, directly executing the step 4, otherwise, continuously executing the step 3;
taking 24um as a step length to move up and down, acquiring a total of 5 downsampled picture sequences within a range of +/-48 um, calculating a Laplace evaluation value and moving to a maximum position;
taking 6um as a step length to move up and down, obtaining 9 image sequences in a range of +/-24 um, calculating two groups of forward and reverse difference value sequences of the image sequences, and searching the minimum value in the sequences;
and filtering out a group which does not meet the condition by using the Laplacian operator to obtain the clearest position.
Setting the data such as the wave band range, the wave band number, the exposure time, the objective lens multiple of the shot image, the name of the image and the like required by hyperspectral image acquisition in the process of calculating the definition of the current view field through the Laplacian operator, and controlling the three-axis electric object stage of the microscope to move to the region of interest manually or by using an industrial control machine; the monoscopic 2048 x 2048 pixel image is downsampled at a magnification of 0.08 and stored in memory. Calculating the Laplace evaluation value of the downsampled image, and if the evaluation value is greater than 6, directly performing the fine focusing in the step 4;
in the process of calculating the Laplace evaluation value and moving to the maximum position, taking 24um as a step length, and respectively storing down-sampled images in a memory by moving the Z axis of the objective table; respectively calculating corresponding Laplace definition evaluation values, and finally moving to object stage coordinates corresponding to the value with the highest evaluation value;
in the process of obtaining the clearest position, moving the Z axis of the objective table to store down-sampled images into a memory respectively by taking 6um as a step length; obtaining 9 picture sequences, and sequentially obtaining a group of columns A from a first picture according to coordinates, wherein the index of the first picture is 0, and the last picture is reduced to obtain a group of columns A; sequentially reducing the last sheet to the first sheet according to the coordinates to obtain another group of series B; finding the smallest number in the number column, and filtering out smaller points by using the Laplace evaluation value; the median value of the final point (if present in the array a) and the first image is the position of the sharpest image.
There are many factors to consider for achieving autofocus by controlling the motorized stage 270 of the microscope. The method comprises the steps of selecting coarse focusing step sizes, selecting coarse focusing image sequences, selecting fine focusing step sizes, selecting fine focusing image sequences and selecting a range containing clear images. The distance between the focal planes of adjacent regions will vary due to the thickness of the dicing, but is typically maintained within + -50 um. The step size of coarse focusing can be determined by fine focusing, the step size of fine focusing is selected to be 6um, at least 9 images are needed by adopting an image difference value, the step size of coarse focusing is set to be 24um, and the number of image sequences of coarse focusing can be set to be 5 because the step size of coarse focusing is limited by the range.
The invention has the advantages that the creative results from the double reduction of the image downsampling and the image difference value on the automatic focusing operation quantity of the microscopic hyperspectral image, and the accurate real-time automatic focusing can be realized on the basis.
The specific algorithm flow of the automatic focusing method based on the microscopic hyperspectral image provided by the invention can be seen in fig. 1, and experimental result data of the embodiment are as follows:
based on step 100, the acousto-optic tunable filter 210 of the hyperspectral image is set first, the band range is set to the expected band range, the real-time display band of the current industrial controller is set to 640nm, and a series of initialization settings are performed for the exposure time, the light intensity objective lens multiple of the halogen lamp light source 200, and the sample name. Selecting a field of interest by controlling an electric stage 270 of a microscope through a manual or industrial control machine, or demarcating a shooting area and moving to a first field of view of the demarcating shooting area to acquire a downsampled image of the field of view, namely, downsampling a gray image of a current monoscopic field 2048 x 2048 pixels and a 640nm wave band by 0.08 multiplying power;
then, step 110 is executed, the sharpness evaluation value (average value) of the downsampled image obtained in step 100 is calculated through the laplace operator, if the sharpness evaluation value is greater than 6, the fine focusing process of step 120 is directly executed, otherwise, step 115 is continuously executed;
TABLE 1
| Position(mm) | Sharpness |
| -48 | 2.436 |
| -24 | 3.369 |
| 0 | 9.522 |
| 24 | 2.573 |
| 48 | 2.409 |
Taking 24um as a step length, respectively storing the downsampled images in a memory by the Z axis of the movable objective table 270; and respectively calculating corresponding Laplace definition evaluation values, and finally moving to the objective table 270 coordinates corresponding to the highest evaluation value.
Since the maximum value in table 1 is 9.522, no movement is required at the current position;
in step 120, the electric stage 270 of the microscope is moved up and down with a step size of 6um, and similar operations are performed as in step 115, and the image obtained by the gray scale camera 240 is refreshed in real time, and downsampled at a rate of 0.08, and the downsampled image is stored in the memory. An image sequence formed by 9 microscopic hyperspectral single-band gray level images can be obtained within the range of +/-24 um. In step 125 and step 130, we calculate two sets of difference series of the forward difference array with the length of 8 obtained by subtracting the ith image (1<i. Ltoreq.9) from the first image in the image sequence and the reverse difference array with the length of 8 obtained by subtracting the ith image (9>i. Ltoreq.1) from the last image in the image sequence, respectively, and find the minimum value in the series. The specific implementation data of this step are shown in table 2:
TABLE 2
| Forward difference | 0-1 | 0-2 | 0-3 | 0-4 | 0-5 | 0-6 | 0-7 | 0-8 |
| Mean value of | 4.768 | 10.255 | 12.991 | 7.856 | 3.187 | 2.265 | 4.351 | 6.076 |
| Reverse difference | 8-7 | 8-6 | 8-5 | 8-4 | 8-3 | 8-2 | 8-1 | 8-0 |
| Mean value of | 2.787 | 5.243 | 7.922 | 11.761 | 15.939 | 13.672 | 8.383 | 6.076 |
The Z axis of the moving stage 270 stores the downsampled images in memory, respectively, with a step size of 6 um. Obtaining 9 picture sequences, and sequentially reducing the first picture to the last picture according to coordinates to obtain a group of image mean value series A; sequentially reducing the last image to the first image according to the coordinates to obtain another group of image mean value series B;
according to step 140, the two sets of arrays are traversed, down-sampled gray images in the image sequence corresponding to the minimum value are found, the sharpness evaluation values of the two images are respectively calculated by using the laplace operator, one image with a smaller evaluation value is filtered, and finally the position of the microscope electric stage 270 corresponding to the image is the clearest position. The specific implementation of this example is:
finding the smallest number in the number column, and filtering out smaller points by using the Laplace evaluation value; the median value of the final point (if present in the array a) and the first image is the position of the sharpest image. Excluding the position corresponding to 2.787 with low sharpness evaluation value, the clearest image position is the position corresponding to 0-6 in the table, namely the midpoint between the 6 th image and the first image.
According to the automatic focusing method based on the microscopic hyperspectral image, the downsampled image is used for operation integrally, so that the operation amount is greatly reduced. And the difference value operation is adopted to replace the traditional definition operation, so that the calculation complexity is further reduced, and the real-time automatic focusing can be realized under the condition of ensuring the definition of the image. The monoscopic auto-focus can be completed within 0.9s at the fastest speed. The cost investment of manual focusing is reduced, and the shooting efficiency of the digital microscopic hyperspectral image is improved efficiently and accurately.
Example two
As shown in fig. 2, an embodiment of the present invention is based on a microscopic hyperspectral imaging system comprising: an industrial control computer, an optical microscope, a programmable three-axis motorized stage 270, a beam splitter, an acousto-optic tunable filter 210, a gray scale camera 240 and a color camera 250. Wherein the industrial control computer provides a user-friendly operation interface for user operation, executing corresponding codes. In the above operations, one of the operations performs iterative scanning on the acquired image based on the current position, and finally moves to the in-focus position based on the scanning result, acquires the image, and refreshes the real-time preview interface of the industrial control machine. In addition, the method also carries out self-adaptive optimization aiming at instantaneity, the optimization is mainly based on the calculated value of image definition evaluation, more fine-granularity focusing calculation is directly carried out after a certain threshold value is exceeded, and otherwise, coarse-granularity focusing operation is carried out first.
The embodiment of the invention specifically describes an automatic focusing method based on the system. The method adopts down-sampling of the whole monoscopic acquisition image, if the definition evaluation value of the down-sampling image at the current position is larger than a threshold value, the method directly enters fine-granularity focusing calculation, otherwise, a group of down-sampling image sequences are obtained by continuously moving the objective table 270, and the defocusing data obtained by calculation is collected. And finding the maximum value so as to reach the vicinity of the quasi-focusing position, acquiring a group of downsampled image sequences based on the current position with smaller step length, and comparing the difference values to obtain the real quasi-focusing position. The automatic focusing method is effective for both color images and gray-scale images of the microscopic hyperspectral acquisition system.
The embodiment of the invention mainly comprises the following steps of: setting the data such as the wave band range, the wave band number, the exposure time, the objective lens multiple of the shot image, the name of the image and the like required by hyperspectral image acquisition; taking the image data combination form of BSQ, namely taking the image according to the wave band, the industrial control computer can display the microscopic hyperspectral gray image in the current wave band under the current position of the microscope stage 270 in real time through the acousto-optic tunable filter 210; the image is transmitted into the automatic focusing algorithm 260, the current monoscopic image is downsampled, and a downsampled image definition evaluation value is calculated to judge whether the downsampled image definition evaluation value is larger than a threshold value or not; if the image sequence is larger than the preset value, fine focusing is directly carried out, otherwise, the large-step objective table 270 is moved to acquire the image sequence to find the maximum value and move; moving the stage 270 in small steps acquires the image sequence and performs image difference to obtain the true focus position and moves to that position, and finally completes the auto-focusing of the microscopic hyperspectral image.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (2)
1. An auto-focusing method based on microscopic hyperspectral image is characterized by comprising the following steps:
acquiring a downsampled image; acquiring field of view definition based on a downsampled image, judging the field of view definition, acquiring an image sequence based on a judging result, acquiring a forward difference value sequence and a reverse difference value sequence based on the image sequence, and acquiring the clearest image position based on the forward difference value sequence and the reverse difference value sequence;
the process of acquiring the downsampled image includes:
setting acquisition conditions, wherein the acquisition conditions comprise a band range, a band number, exposure time, objective lens multiples and image labels;
downsampling the monoscopic image based on the acquisition condition to obtain a downsampled image;
the process of obtaining the field of view definition comprises the following steps:
the downsampled image is calculated through the Laplace operator to obtain the definition of the field of view;
the process of acquiring a sequence of images includes:
judging the field of view definition, if the field of view definition is greater than an expected value, moving the moving microscope electric stage up and down, and acquiring an image sequence in a first range, otherwise, moving the moving microscope electric stage up and down, and acquiring a downsampled image sequence in a second range, calculating a Laplace evaluation value based on the downsampled image sequence, moving the moving microscope electric stage to the maximum value of the Laplace evaluation value, and moving the moving microscope electric stage up and down, and acquiring the image sequence in the first range;
the process of obtaining the clearest position includes:
in the image sequence, sequentially reducing the first image to the last image according to the coordinates to obtain a forward difference value sequence, sequentially reducing the last image to the first image according to the coordinates to obtain a reverse difference value sequence, searching the minimum value in the forward difference value sequence and the reverse difference value sequence, respectively carrying out definition evaluation value calculation on the downsampled image corresponding to the minimum value of the forward difference value sequence and the downsampled image corresponding to the minimum value of the reverse difference value sequence through a Laplacian operator, filtering downsampled images with low definition evaluation value, taking the median value of the reserved downsampled image and the first image as the position of the clearest image if the reserved downsampled image corresponds to the forward difference value sequence, and taking the median value of the reserved downsampled image and the last image as the position of the clearest image if the reserved downsampled image corresponds to the reverse difference value sequence.
2. An automatic focusing system based on microscopic hyperspectral images is characterized in that: comprising the steps of (a) a step of,
a control computer, an optical microscope, a movable microscope electric stage, a beam splitter, an acousto-optic tunable filter, a gray level camera and a color camera;
the gray-scale camera and the color camera are used for acquiring downsampled images in the optical microscope, wherein the downsampled images comprise color images and gray-scale images;
the control computer is used for controlling the movement of the electric object stage of the mobile microscope, acquiring the field of view definition based on the downsampled image, judging the field of view definition, acquiring an image sequence based on a judging result, acquiring a forward difference value sequence and a reverse difference value sequence based on the image sequence, and acquiring the clearest image position based on the forward difference value sequence and the reverse difference value sequence;
the acousto-optic tunable filter is used for displaying downsampled images and image sequences in the optical microscope in real time;
setting acquisition conditions of a gray level camera and a color camera, wherein the acquisition conditions comprise a wave band range, a wave band number, exposure time, objective lens multiple and an image label; downsampling the monoscopic image based on the acquisition condition to obtain a downsampled image;
the control computer comprises a calculation module, wherein the calculation module is used for calculating the downsampled image through the Laplacian to obtain the definition of the field of view;
the control computer comprises a judging module, wherein the judging module is used for judging the definition of a field of view, if the definition of the field of view is larger than an expected value, the moving microscope electric stage moves up and down, and an image sequence is acquired in a first range, otherwise, the moving microscope electric stage moves up and down, and a downsampled image sequence is acquired in a second range, a Laplace evaluation value is calculated based on the downsampled image sequence, the moving microscope electric stage moves to the maximum value of the Laplace evaluation value, and the moving microscope electric stage moves up and down, and an image sequence is acquired in the first range;
the control computer comprises a processing module, wherein the processing module is used for sequentially reducing the first image to the last image according to coordinates in an image sequence to obtain a forward difference value sequence, sequentially reducing the last image to the first image according to coordinates to obtain a reverse difference value sequence, searching the minimum value in the forward difference value sequence and the reverse difference value sequence, respectively carrying out definition evaluation value calculation on the downsampled image corresponding to the minimum value of the forward difference value sequence and the downsampled image corresponding to the minimum value of the reverse difference value sequence through a Laplacian operator, filtering the downsampled image with low definition evaluation value, taking the median value of the reserved downsampled image and the first image as the clearest image position if the reserved downsampled image corresponds to the forward difference value sequence, and taking the median value of the reserved downsampled image and the last image as the clearest image position if the reserved downsampled image corresponds to the reverse difference value sequence.
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