CN109489816B - Microscopic hyperspectral imaging platform and large-area data cube acquisition method - Google Patents
Microscopic hyperspectral imaging platform and large-area data cube acquisition method Download PDFInfo
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Abstract
The invention discloses a microscopic hyperspectral imaging platform and a method for acquiring a large-area data cube, wherein an optical microscope, a triaxial electric object stage, a beam splitter, an acousto-optic tunable filter, a gray scale camera and an industrial control computer are adopted to form the imaging platform, and the imaging platform is applied to sequentially complete the steps of generating a task acquisition sequence, acquiring microscopic hyperspectral images, splicing the microscopic hyperspectral images and generating the large-area data cube.
Description
Technical Field
The invention relates to the technical field of microscopic image splicing, in particular to a microscopic hyperspectral imaging platform and a method for collecting a large-area data cube.
Background
With the development of hyperspectral imaging technology, the medical field is detected from the ground, and hyperspectral images gradually play more and more important roles in many fields. The hyperspectral image has fine spatial resolution and spectral resolution, more information can be transmitted on a single image, and a foundation stone is provided for high-precision identification and classification of objects. However, in the medical field, doctors still use optical or electron microscopes to acquire single-point information on a glass slide, which has the disadvantages of small field of view and narrow spectral range, so that the demand of applying the microscopic hyperspectral imaging stitching technology to the medical field is more and more strong.
In addition, in order to solve the problem that a single image has a small field of view, a plurality of image splicing techniques are developed in the image field in the prior art, and the image splicing techniques are mainly divided into image matching and splicing methods based on feature points and regions, which require that images have rich color information and texture information. The problem exists that in the field of microscopic hyperspectral images, especially in the case of high-magnification objective lenses, the images in the field of view are mostly partial tissues and do not have complete texture information; under the condition of high magnification, the edge of the tissue is also enlarged to cause the edge to be unobvious, so that some feature matching methods based on edge gradient can cause wrong matching; in addition, in the aspect of microscopic hyperspectral imaging, how to select a wavelength as a reference image for feature matching is also a problem, and the transmittances of different substances at different wavelengths are different. Therefore, how to effectively use the spectral dimension information of the microscopic hyperspectral image to effectively splice the images of all the subareas to form a large-area microscopic hyperspectral image for doctors to diagnose is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a microscopic hyperspectral imaging platform and a large-area data cube acquisition method aiming at the defects of the prior art, the invention adopts an optical microscope, a triaxial electric objective table, a beam splitter, an acousto-optic tunable filter, a gray level camera and an industrial control computer to form the imaging platform, and uses the imaging platform to sequentially complete the steps of generating a task acquisition sequence, acquiring a microscopic hyperspectral image, splicing the microscopic hyperspectral image and generating a large-area data cube.
The specific technical scheme for realizing the purpose of the invention is as follows:
a microscopic hyperspectral imaging platform is characterized by comprising an optical microscope, a triaxial electric objective table, a light splitter, an acousto-optic tunable filter, a gray level camera and an industrial control computer;
a C bayonet adapter ring is arranged on the acousto-optic tunable filter;
a PCI control card is arranged on the triaxial electric objective table;
the industrial control computer is provided with an input interface of an image acquisition area and a blank image area, and is also provided with a central processing unit, a memory, a computer peripheral interface and a PCI control card;
the optical microscope is respectively connected with the three-axis electric objective table and the light splitter;
the light splitter is connected with the acousto-optic tunable filter, the acousto-optic tunable filter is connected with the gray level camera and the industrial control computer through the C bayonet adapter ring, and the industrial control computer is connected with the three-axis electric objective table and the gray level camera.
A method for performing large area data cube acquisition using an imaging platform, the method comprising the steps of:
step 1: generating a task acquisition sequence
Manually selecting the ranges of the image acquisition area and the blank image area by an input interface of an industrial control computer, and setting a track and acquisition points of the sequentially moving three-axis electric object stage in rows and columns by the industrial control computer according to the manually selected ranges; and respectively recording acquisition points in an image acquisition area, namely an organized area on the glass slide and a blank image area, namely an unorganized area on the glass slide in sequence to generate an acquisition task sequence.
Step 2: acquisition of microscopic hyperspectral images
According to the collection task sequence, an industrial control computer controls a three-axis electric objective table to move to a corresponding collection point relative to an optical microscope according to a set movement track, and information obtained by the optical microscope passes through a light splitter, an acousto-optic tunable filter and a gray level camera to generate a plurality of organized and two unorganized microscopic hyperspectral images, which are hereinafter called organized images and unorganized images.
And step 3: splicing microscopic hyperspectral images
Inputting a plurality of organized images and two unorganized images into an industrial control computer in sequence, reading two adjacent organized images by the industrial control computer according to the sequence of rows and columns, storing the two adjacent organized images into a memory of the industrial control computer, and comparing, correcting white balance and denoising the organized images and the unorganized images one by the industrial control computer; then, a central processing unit of the industrial control computer sequentially calculates the accelerated steady characteristic value, calculates an image conversion matrix according to the characteristic value, finally carries out image splicing on a plurality of organized images one by one, and constructs a spliced microscopic hyperspectral image.
And 4, step 4: generating a large-area data cube
The industrial control computer constructs a spliced microscopic hyperspectral image and stores metadata of the hyperspectral image, wherein the metadata comprises the resolution of the image, the combination form of image data, the name of the image, the exposure time of the image, the objective lens multiple during image shooting, the initial waveband image of the image, the termination waveband of the image, the number of the wavebands of the image and the physical three-dimensional coordinates of the image, and the spliced microscopic hyperspectral image is combined with the metadata to generate a large-area data cube.
The invention adopts an optical microscope, a triaxial electric objective table, a beam splitter, an acousto-optic tunable filter, a gray level camera and an industrial control computer to form an imaging platform, and uses the imaging platform to sequentially complete the steps of generating a task acquisition sequence, acquiring a microscopic hyperspectral image, splicing the microscopic hyperspectral image and generating a large-area data cube.
Drawings
FIG. 1 is a schematic structural diagram of an imaging platform according to the present invention;
fig. 2 is a flowchart of the operation of the imaging platform of the present invention.
Detailed Description
The imaging platform comprises an optical microscope 1, a three-axis electric objective table 2, a light splitter 3, an acousto-optic tunable filter 4, a gray level camera 5 and an industrial control computer 7;
a C bayonet adapter ring is arranged on the acousto-optic tunable filter 4;
a PCI control card is arranged on the triaxial electric objective table 2;
the industrial control computer 7 is provided with an input interface of an image acquisition area and a blank image area, and is also provided with a central processing unit, a memory, a computer peripheral interface and a PCI control card;
the optical microscope 1 is respectively connected with the three-axis electric objective table 2 and the light splitter 3;
the light splitter 3 is connected with the acousto-optic tunable filter 4, the acousto-optic tunable filter 4 is connected with the gray level camera 5 and the industrial control computer 7 through a C bayonet adapter ring, and the industrial control computer 7 is connected with the three-axis electric objective table 2 and the gray level camera 5.
The method for implementing the large-area data cube acquisition by using the imaging platform comprises the following steps:
step 1: generating a task acquisition sequence
Manually selecting the ranges of the image acquisition area and the blank image area by an input interface of the industrial control computer 7, and setting the track and the acquisition point of the sequentially moving three-axis electric object stage 2 in rows and columns by the industrial control computer 7 according to the manually selected ranges; and respectively recording acquisition points in an image acquisition area, namely an organized area on the glass slide and a blank image area, namely an unorganized area on the glass slide in sequence to generate an acquisition task sequence.
Step 2: acquisition of microscopic hyperspectral images
According to the collection task sequence, the industrial control computer 7 controls the three-axis electric objective table 2 to move to a corresponding collection point relative to the optical microscope 1 according to a set movement track, and information obtained by the optical microscope 1 is used for generating a plurality of organized and two unorganized microscopic hyperspectral images, hereinafter referred to as organized images and unorganized images, through the optical splitter 3, the acousto-optic tunable filter 4 and the gray scale camera 5.
And step 3: splicing microscopic hyperspectral images
Inputting a plurality of organized images and two unorganized images into an industrial control computer 7 in sequence, reading two adjacent organized images by the industrial control computer 7 according to the sequence of rows and columns, storing the two adjacent organized images into a memory of the industrial control computer 7, and comparing, correcting white balance and denoising the organized images and the unorganized images one by the industrial control computer 7; then, the central processing unit of the industrial control computer 7 sequentially calculates the accelerated steady characteristic value, calculates an image conversion matrix according to the characteristic value, finally carries out image splicing on a plurality of organized images one by one, and constructs a spliced microscopic hyperspectral image.
And 4, step 4: generating a large-area data cube
The industrial control computer 7 constructs the spliced microscopic hyperspectral image and stores metadata of the hyperspectral image, wherein the metadata comprises the resolution of the image, the combination form of the image data, the name of the image, the exposure time of the image, the objective lens multiple during image shooting, the initial waveband image of the image, the termination waveband of the image, the number of the wavebands of the image and the physical three-dimensional coordinate of the image, and the spliced microscopic hyperspectral image is combined with the metadata to generate a large-area data cube.
Examples
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, the microscopic hyperspectral imaging platform works as follows:
the hyperspectral imaging platform provided by the invention is composed of an optical microscope 1, a triaxial electric objective table 2, a light splitter 3, an acousto-optic tunable filter 4, a gray level camera 5 and an industrial control computer 7.
The triaxial electric stage 2 provides the optical microscope 1 with the acquisition points of the image acquisition area or the blank image area through the movement thereof in the three-dimensional space, and is controlled by the industrial control computer 7 and records the track of the sequential movement of the triaxial electric stage 2 in rows and columns and the position of each acquisition point.
The optical splitter 3 is used for projecting the light of the optical microscope 1 into the acousto-optic tunable filter 4 and the gray level camera 5, the industrial control computer 7 dynamically adjusts the filtering range of the acousto-optic tunable filter 4, and the light with a certain wavelength is transmitted to the gray level camera 5 for shooting; the industrial control computer 7 is used for controlling hardware and providing operation support and storage support for image acquisition, matrix transformation and splicing.
Referring to fig. 1 and 2, a specific method for implementing large-area data cube acquisition by using a microscopic hyperspectral imaging platform is as follows:
1. generating an acquisition task sequence
1.1 manually selecting image acquisition area and blank image area
The ranges of the image capture area and the blank image area are manually selected by the input interface of the industrial control computer 7.
1.2, setting the moving track and the acquisition point of the three-axis electric objective table 2
The industrial control computer 7 edits and sets the moving track of the three-axis electric object stage 2 and the position of each acquisition point according to the manually selected image acquisition area and the blank image area.
1.3 recording the acquisition points
Recording coordinate information of acquisition points of an image acquisition area acquired by an industrial control computer 7, calculating a moving track, a moving step length and coordinates of all the acquisition points of the triaxial electric object stage 2, and controlling the moving step length and the step number of the triaxial electric object stage 2 by the industrial control computer 7 so as to ensure that a certain overlapping area exists between the transverse direction and the longitudinal direction of two adjacent shot images, and the shot areas completely cover the image acquisition area to generate an acquisition task sequence; the industrial control computer 7 calculates two acquisition points required in the blank image area, ensures that the two acquisition points are uniformly distributed in the blank image area, and adds the two acquisition points to the end of the acquisition task sequence.
2. Acquisition of microscopic hyperspectral images
2.1 carrying out microscopic hyperspectral image acquisition
The industrial control computer 7 acquires information of a current acquisition point from an acquisition task sequence, the industrial control computer 7 controls the three-axis electric object stage 2 to move according to coordinate information in the acquisition point, after the three-axis electric object stage 2 finishes moving, the industrial control computer 7 sets the gray scale camera 5 and the acousto-optic tunable filter 4 according to shooting parameters of microscopic hyperspectral images in the acquisition point, and after the setting is finished, data acquisition of the microscopic hyperspectral images is carried out band by band.
After the data acquisition of the microscopic hyperspectral image is finished, the industrial control computer records metadata information of the microscopic hyperspectral image and provides additional information for the subsequent image processing; the metadata information comprises the resolution ratio of the microscopic hyperspectral image, the combination form of the microscopic hyperspectral image data, the name of the microscopic hyperspectral image, the exposure duration of the microscopic hyperspectral image, the multiple of an objective lens when the microscopic hyperspectral image is shot, the initial waveband image of the microscopic hyperspectral image, the termination waveband of the microscopic hyperspectral image, the number of the wavebands of the microscopic hyperspectral image and the physical three-dimensional coordinates of the microscopic hyperspectral image.
2.2 generating multiple organized microscopic hyperspectral images
The industrial control computer 7 performs data acquisition on all acquisition points in the image acquisition area in the manner described in section 2.1 to generate a plurality of organized microscopic hyperspectral images.
2.3 generating two microstructural hyperspectral images
The industrial control computer 7 performs data acquisition on two acquisition points in the blank image area in the manner described in section 2.1 to generate two unstructured microscopic hyperspectral images.
3. Splicing microscopic hyperspectral images
3.1 reading in microscopic Hyperspectral image data
And a plurality of organized images and two unorganized images are sequentially input into the industrial control computer 7, and the industrial control computer 7 reads two adjacent organized images according to the sequence of rows and columns and stores the two organized images into the memory of the industrial control computer 7. The industrial control computer 7 reads in the two unorganized images and stores them into the memory of the industrial control computer 7.
3.2 microscopic Hyperspectral image preprocessing
The industrial control computer computing system 7 divides the organized image and the unorganized image by wave band, so as to eliminate the influence of light source, dust and the like; the industrial control computer computing system 7 respectively carries out the work of spectrum correction and stripe noise elimination on the two organized images in the above way, so that the cleanness and the clarity of the microscopic hyperspectral images are ensured, and the splicing stability of the microscopic hyperspectral images is improved.
3.3 micro-hyperspectral image stitching
The industrial control computer computing system 7 sequentially carries out accelerated robust feature value computation on the two adjacent organized images in each group, and computes an image conversion matrix according to the feature values. And the industrial control computer computing system 7 splices the plurality of organized images one by one according to the conversion matrix to construct a spliced microscopic hyperspectral image.
4. Generating a large-area data cube
After the microscopic hyperspectral images are spliced, the computing system of the industrial control computer 7 generates metadata information of the large-area microscopic hyperspectral image according to the metadata information of each sub-microscopic hyperspectral image, synthesizes the image data information and generates the large-area data cube.
Claims (1)
1. A microscopic hyperspectral imaging platform is characterized by comprising an optical microscope (1), a triaxial electric objective table (2), a light splitter (3), an acousto-optic tunable filter (4), a gray level camera (5) and an industrial control computer (7);
a C bayonet adapter ring is arranged on the acousto-optic tunable filter (4);
a PCI control card is arranged on the triaxial electric objective table (2);
the industrial control computer (7) is provided with an input interface of an image acquisition area and a blank image area, and is also provided with a central processing unit, a memory, a computer peripheral interface and a PCI control card;
the optical microscope (1) is respectively connected with the three-axis electric objective table (2) and the light splitter (3);
the optical splitter (3) is connected with the acousto-optic tunable filter (4), the acousto-optic tunable filter (4) is connected with the gray level camera (5) and the industrial control computer (7) through a C bayonet adapter ring, and the industrial control computer (7) is connected with the three-axis electric objective table (2) and the gray level camera (5);
the method for implementing the large-area data cube acquisition by using the microscopic hyperspectral imaging platform comprises the following steps:
step 1: generating a task acquisition sequence
Manually selecting the ranges of the image acquisition area and the blank image area by an input interface of the industrial control computer (7), and setting a track and an acquisition point of the sequentially moving three-axis electric object stage (2) in rows and columns by the industrial control computer (7) according to the manually selected range; sequentially recording acquisition points in an image acquisition area, namely an organized area on the glass slide and a blank image area, namely an unorganized area on the glass slide, so as to generate an acquisition task sequence;
step 2: acquisition of microscopic hyperspectral images
According to an acquisition task sequence, an industrial control computer (7) controls a three-axis electric objective table (2) to move to a corresponding acquisition point relative to an optical microscope (1) according to a set movement track, and information acquired by the optical microscope (1) generates a plurality of organized and two pairs of unorganized microscopic hyperspectral images, hereinafter referred to as organized images and unorganized images, through a light splitter (3), an acousto-optic tunable filter (4) and a gray camera (5);
and step 3: splicing microscopic hyperspectral images
Inputting a plurality of organized images and two unorganized images into an industrial control computer (7) in sequence, reading two adjacent organized images by the industrial control computer (7) according to the sequence of rows and columns, storing the two adjacent organized images into a memory of the industrial control computer (7), and comparing, correcting white balance and denoising the organized images and the unorganized images one by the industrial control computer (7); then, a central processing unit of an industrial control computer (7) sequentially calculates an accelerated steady characteristic value, calculates an image conversion matrix according to the characteristic value, and finally carries out image splicing on a plurality of organized images one by one to construct a spliced microscopic hyperspectral image;
and 4, step 4: generating a large-area data cube
The industrial control computer (7) constructs a spliced microscopic hyperspectral image and stores metadata of the hyperspectral image, wherein the metadata comprises the resolution of the image, the combination form of the image data, the name of the image, the exposure time of the image, the objective lens multiple during image shooting, the initial waveband image of the image, the termination waveband of the image, the number of the wavebands of the image and the physical three-dimensional coordinate of the image, and the spliced microscopic hyperspectral image is combined with the metadata to generate a large-area data cube.
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| US5828451A (en) * | 1997-09-30 | 1998-10-27 | Northrop Grumman Corporation | Spectral imaging system and method employing an acousto-optic tunable filter for wavelength selection with increased field of view brightness |
| CN1296699C (en) * | 2003-12-19 | 2007-01-24 | 武汉大学 | Microscopic multispectral marrow and its peripheral blood cell auto-analyzing instrument and method |
| CN101539456B (en) * | 2009-04-24 | 2010-09-08 | 华东师范大学 | General hyperspectral imaging component |
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