[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

CN115598822A - Intelligent multidimensional microscopic image acquisition and processing method - Google Patents

Intelligent multidimensional microscopic image acquisition and processing method Download PDF

Info

Publication number
CN115598822A
CN115598822A CN202211609146.7A CN202211609146A CN115598822A CN 115598822 A CN115598822 A CN 115598822A CN 202211609146 A CN202211609146 A CN 202211609146A CN 115598822 A CN115598822 A CN 115598822A
Authority
CN
China
Prior art keywords
sample
image
horizontal section
area
horizontal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202211609146.7A
Other languages
Chinese (zh)
Other versions
CN115598822B (en
Inventor
曾凡新
李诗林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dazhou Aijia Feishite Technology Co ltd
Original Assignee
Dazhou Aijia Feishite Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Dazhou Aijia Feishite Technology Co ltd filed Critical Dazhou Aijia Feishite Technology Co ltd
Priority to CN202211609146.7A priority Critical patent/CN115598822B/en
Publication of CN115598822A publication Critical patent/CN115598822A/en
Application granted granted Critical
Publication of CN115598822B publication Critical patent/CN115598822B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/36Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/34Microscope slides, e.g. mounting specimens on microscope slides
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/36Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/361Optical details, e.g. image relay to the camera or image sensor
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/36Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/362Mechanical details, e.g. mountings for the camera or image sensor, housings
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/36Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/365Control or image processing arrangements for digital or video microscopes
    • GPHYSICS
    • G02OPTICS
    • G02BOPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B21/00Microscopes
    • G02B21/36Microscopes arranged for photographic purposes or projection purposes or digital imaging or video purposes including associated control and data processing arrangements
    • G02B21/365Control or image processing arrangements for digital or video microscopes
    • G02B21/367Control or image processing arrangements for digital or video microscopes providing an output produced by processing a plurality of individual source images, e.g. image tiling, montage, composite images, depth sectioning, image comparison

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Optics & Photonics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Microscoopes, Condenser (AREA)

Abstract

The invention provides an intelligent multi-dimensional microscopic image acquisition and processing method, which comprises the steps of driving a sample to move in the Z direction through an objective table, so that light source light rays are focused at different positions in the thickness direction of the sample, facilitating the primary shooting and screening of the thickness direction of the interior of the sample by a microscope, dividing the interior of the sample into a plurality of horizontal sections along the thickness direction of the sample, carrying out horizontal two-dimensional scanning and shooting on each horizontal section to obtain a global image of each horizontal section, splicing the global images of all the horizontal sections to obtain a spatial three-dimensional microscopic image of the sample, and carrying out image acquisition and splicing on different spatial layers in the sample to realize the microscopic imaging of different dimensions in the sample, improve the image acquisition comprehensiveness and the multi-dimensional property of the microscopic imaging and provide sufficient image data for the subsequent diagnosis of a target object.

Description

Intelligent multidimensional microscopic image acquisition and processing method
Technical Field
The invention relates to the technical field of medical image processing, in particular to an intelligent multi-dimensional microscopic image acquisition and processing method.
Background
In the medical diagnosis process, a biological sample from a patient is usually observed under a microscope to obtain the tissue structure information in the biological sample, so as to determine whether a lesion exists in the biological sample, thereby performing corresponding disease identification on the patient. The observation of the existing microscope on the biological sample is only limited to the microscopic imaging of the surface area of the biological sample, but the microscopic imaging of the structure condition of the internal area of the biological sample cannot be performed, so that the focus distribution condition of the biological sample cannot be comprehensively and sufficiently characterized by the image obtained by the microscopic imaging. The current microscope imaging can only carry out image acquisition on the peripheral surface of a biological sample, and cannot go deep into different levels in the biological sample to carry out multi-dimensional image acquisition, so that the comprehensiveness and the multi-dimensional property of image acquisition of the microscope imaging are reduced, and sufficient data cannot be provided for the investigation of a focus area.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent multi-dimensional microscopic image acquisition and processing method, which comprises the steps of placing a sample on a three-dimensional movable microscope stage, indicating the stage to drive the sample to move in the Z direction and indicating a light source of a microscope to irradiate light rays to the sample, so as to obtain sample structure characteristic information of a horizontal section image of each position in the thickness direction of the sample, and further determining a horizontal section shooting reference point corresponding to each position; taking the shooting reference point as a reference, indicating the objective table to drive the sample to horizontally move in two dimensions, and shooting to obtain a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample; the method comprises the steps of splicing all horizontal section sub-area images corresponding to horizontal sections at the same position to form a horizontal section global image based on image characteristic information of each horizontal section sub-area image, splicing all horizontal section global images to obtain a spatial three-dimensional microscopic image related to a sample, driving the sample to move in the Z direction through an objective table to focus light source light at different positions in the thickness direction of the sample, facilitating a microscope to preliminarily shoot and screen the interior of the sample in the thickness direction, dividing the interior of the sample into a plurality of horizontal sections in the thickness direction of the sample, horizontally scanning and shooting each horizontal section in a two-dimensional manner to obtain a global image of each horizontal section, splicing all horizontal section global images to obtain a spatial three-dimensional microscopic image of the sample, collecting and splicing the images of different spatial layers in the sample to realize microscopic imaging of different dimensions in the sample, improving the image collecting comprehensiveness and multidimensional property of microscopic imaging, and providing sufficient image data for subsequent target object diagnosis.
The invention provides an intelligent multi-dimensional microscopic image acquisition and processing method, which comprises the following steps:
step S1, a light source of an instruction microscope irradiates illumination light to a sample, and an objective table of the instruction microscope drives the sample to move in a Z direction, so that focusing illumination areas are formed at different positions in the thickness direction of the sample; acquiring a horizontal section image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal section image;
s2, identifying each horizontal section image to obtain sample structure characteristic information on the horizontal section corresponding to each position in the thickness direction of the sample; determining a shooting reference point of a horizontal section corresponding to each position according to the sample structure characteristic information;
s3, when the light source forms a focusing illumination area at each position in the thickness direction of the sample, the objective table is indicated to drive the sample to horizontally move in two dimensions according to the shooting reference point, and a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample are shot; identifying each horizontal section subregion image to obtain image characteristic information of each horizontal section subregion image;
step S4, according to the image characteristic information, splicing all horizontal section sub-region images corresponding to the horizontal section at the same position to obtain a horizontal section global image; and then splicing the horizontal section global images corresponding to all the horizontal sections to obtain a spatial three-dimensional microscopic image of the sample.
In an embodiment disclosed in the present application, in step S1, the light source of the instruction microscope irradiates illumination light to the sample, and the stage of the instruction microscope drives the sample to move in the Z direction, so as to form a focused illumination area at different positions in the thickness direction of the sample, specifically including:
determining the reference region area of a focused illumination region formed on the sample by a focused illumination spot generated by a light source according to the minimum cross-sectional area of the sample;
according to the area of the reference region, adjusting the condensation degree of a focusing optical element in the light source on a light beam emitted by the light source so as to enable the spot area of a focusing illumination spot generated by the light source to be equal to the area of the reference region;
indicating an objective table of the microscope to drive the sample to move at preset step intervals in the Z direction so that a focusing illumination spot generated by the light source enters the sample, and respectively forming focusing illumination areas at different positions in the thickness direction of the sample; wherein the Z direction is a direction perpendicular to the plane of the object stage.
In an embodiment disclosed in the present application, in step S1, determining a reference area of a focused illumination area formed on the sample by the focused illumination spot generated by the light source according to the minimum cross-sectional area of the sample specifically includes:
the method comprises the steps of obtaining the minimum cross section area of a sample along the thickness direction of the sample, and determining that the area of a reference area of a focusing illumination area formed on the sample by a focusing illumination spot generated by a light source is equal to half of the minimum cross section area;
in the step S1, adjusting a condensing degree of a light beam emitted by the light source by a focusing optical element inside the light source according to the area of the reference region, so that a spot area of a focused illumination spot generated by the light source is equal to the area of the reference region, specifically including:
and adjusting the beam reduction ratio of a focusing optical element in the light source to the beam area of the light beam emitted by the light source according to the area of the reference region, so that the spot area of the focused illumination spot generated by the light source is equal to the area of the reference region.
In an embodiment disclosed in the present application, in step S1, acquiring a horizontal cross-sectional image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal cross-sectional image specifically includes:
when an objective table of the microscope drives a sample to move in the Z direction for a preset step interval, a zoom objective of the microscope is instructed to carry out focusing shooting on a focusing illumination area formed by a focusing illumination spot generated by a light source in the sample, so that a horizontal section image corresponding to the position of the current focusing illumination area in the thickness direction of the sample is obtained;
and performing identification processing on the position of each horizontal sectional image in the thickness direction of the sample so that each horizontal sectional image corresponds to the position of each sample in the thickness direction in a one-to-one mode.
In an embodiment disclosed in the present application, in step S2, performing identification processing on each horizontal cross-sectional image to obtain sample structure characteristic information on a horizontal cross-section corresponding to each position in the thickness direction of the sample, specifically including:
carrying out shape recognition processing on each horizontal section image to obtain the sample section shape structure feature information on the horizontal section corresponding to each position in the thickness direction of the sample; the sample profile topographic structure characteristic information comprises sample profile undulating structure distribution position information and sample profile undulating structure size information.
In an embodiment disclosed in the present application, in the step S2, determining, according to the sample structure feature information, a shooting reference point of a horizontal section corresponding to each position specifically includes:
extracting and obtaining distribution position information corresponding to the first three convex structures with the maximum curvature radius or distribution position information corresponding to the first three concave structures with the maximum curvature radius on the horizontal section corresponding to each position in the thickness direction of the sample from the sample structure characteristic information;
determining the central point of a triangle formed by the highest convex points of the first three convex structures with the maximum curvature radius according to the distribution position information corresponding to the first three convex structures with the maximum curvature radius; or determining the central point of a triangle formed by the lowest concave points of the first three concave structures with the maximum curvature radius according to the distribution position information corresponding to the first three concave structures with the maximum curvature radius;
and determining the central point as a shooting reference corresponding to each position in the thickness direction of the sample.
In an embodiment disclosed in the present application, in the step S3, when the light source forms a focused illumination area at each position in the thickness direction of the sample, the stage is instructed to drive the sample to perform horizontal two-dimensional movement according to the reference point, so as to obtain a plurality of horizontal cross-section sub-area images of different sub-areas of the horizontal cross section at each position in the thickness direction of the sample, specifically including:
when the light source forms a focusing illumination area on a horizontal section corresponding to each position in the thickness direction of the sample, instructing a zoom objective lens of the microscope to perform zooming and zooming operations by taking a shooting reference point corresponding to each position as a reference so as to enable the current shooting field center of the zoom objective lens to coincide with the shooting reference point, and instructing the zoom objective lens to perform first shooting in a fixed shooting field range;
when the zoom objective lens finishes the first shooting, the objective table is indicated to respectively move at preset step intervals along the X direction and the Y direction of a horizontal plane so as to drive the sample to horizontally move in two dimensions;
and when the objective table finishes moving for a preset step interval along the X direction and the Y direction of a horizontal plane, the zoom objective lens is instructed to shoot the current focusing illumination area of the horizontal section corresponding to each position in the thickness direction of the sample, so that a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample are obtained.
In an embodiment disclosed in the present application, in step S3, performing identification processing on each horizontal cross-section sub-area image to obtain image feature information of each horizontal cross-section sub-area image, specifically including:
and carrying out image boundary identification processing on each horizontal section subarea image to obtain image texture feature information of the image boundary area of each horizontal section subarea image.
In an embodiment disclosed in the present application, in the step S4, according to the image feature information, performing a stitching process on all horizontal section sub-region images corresponding to a horizontal section at the same position to obtain a horizontal section global image, specifically including:
and determining all horizontal cross section sub-region images adjacent to the periphery of each horizontal cross section sub-region image corresponding to the horizontal cross section at the same position according to the image texture feature information, and splicing each horizontal cross section sub-region image and all horizontal cross section sub-region images adjacent to the periphery of the horizontal cross section sub-region image to obtain a horizontal cross section global image.
In an embodiment disclosed in the present application, in the step S4, the stitching processing is performed on the global images of the horizontal cross sections corresponding to all the horizontal cross sections to obtain a spatial three-dimensional microscopic image of the sample, and specifically includes:
splicing the horizontal section global images corresponding to all the horizontal sections according to the sequence from low to high of the corresponding positions of all the horizontal sections in the Z direction, so as to obtain a three-dimensional spliced sample image;
and carrying out pixel sharpening processing and image magnification uniformization processing on the three-dimensional spliced sample image so as to obtain a spatial three-dimensional microscopic image of the sample.
Compared with the prior art, the intelligent multi-dimensional microscopic image acquisition and processing method has the advantages that the sample is placed on the three-dimensional movable microscope objective table, the objective table is indicated to drive the sample to move in the Z direction, and the light source of the microscope is indicated to irradiate light rays to the sample, so that the sample structure characteristic information of the horizontal section image of each position in the thickness direction of the sample is obtained, and the horizontal section shooting reference point corresponding to each position is determined; taking the shooting reference point as a reference, indicating the objective table to drive the sample to horizontally move in two dimensions, and shooting to obtain a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample; based on image characteristic information of each horizontal section subregion image, all the horizontal section subregion images corresponding to the horizontal section at the same position are spliced to form a horizontal section global image, all the horizontal section global images are spliced to obtain a spatial three-dimensional microscopic image related to a sample, a stage drives the sample to move in the Z direction to focus light source light rays at different positions in the thickness direction of the sample, a microscope is convenient to preliminarily shoot and screen the inside of the sample in the thickness direction, the inside of the sample is divided into a plurality of horizontal sections along the thickness direction of the sample, each horizontal section is horizontally scanned and shot to obtain a global image of each horizontal section, all the global images of all the horizontal sections are spliced to obtain a spatial three-dimensional microscopic image of the sample, the microscopic imaging of different dimensions in the sample is realized by carrying out image acquisition and splicing on different spatial layers in the sample, the image acquisition comprehensiveness and the multidimensional property of the microscopic imaging are improved, and sufficient image data are provided for subsequent target object diagnosis.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow diagram of an intelligent multi-dimensional microscopic image acquisition and processing method provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart diagram of an intelligent multi-dimensional microscopic image collecting and processing method according to an embodiment of the present invention. The intelligent multi-dimensional microscopic image acquisition and processing method comprises the following steps:
step S1, a light source of an instruction microscope irradiates illumination light to a sample, and an objective table of the instruction microscope drives the sample to move in a Z direction, so that focusing illumination areas are formed at different positions in the thickness direction of the sample; acquiring a horizontal section image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal section image;
s2, identifying each horizontal section image to obtain sample structure characteristic information on the horizontal section corresponding to each position in the thickness direction of the sample; determining a shooting reference point of a horizontal section corresponding to each position according to the structural feature information of the sample;
s3, when the light source forms a focusing illumination area at each position in the thickness direction of the sample, the objective table is indicated to drive the sample to horizontally move in two dimensions according to the shooting reference point, and a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample are shot; identifying each horizontal section subregion image to obtain image characteristic information of each horizontal section subregion image;
step S4, according to the image characteristic information, splicing all horizontal section sub-region images corresponding to the horizontal section at the same position to obtain a horizontal section global image; and then splicing the horizontal section global images corresponding to all the horizontal sections to obtain a spatial three-dimensional microscopic image of the sample.
The beneficial effects of the above technical scheme are: this intelligent multidimension microscopic image gathers and processing method drives the sample through the objective table earlier and carries out the Z direction and remove, so that light source light is at the different position focus of sample self thickness direction, be convenient for the microscope to carry out thickness direction's preliminary shooting to sample inside and examine, divide into a plurality of horizontal sections with this along sample thickness direction with sample inside, carry out horizontal two-dimensional scanning to every horizontal section and shoot, obtain the universe image of every horizontal section, and splice the universe image of all horizontal sections, obtain the space three-dimensional microscopic image of sample, it is through carrying out image acquisition and concatenation to the different space aspect of sample inside, realize the microscopic imaging to the different dimensions of sample inside, improve the image acquisition comprehensiveness and the multidimension nature of microscopic imaging, provide sufficient image data for follow-up to the target object diagnosis.
Preferably, in step S1, the light source of the instruction microscope irradiates illumination light to the sample, and the stage of the instruction microscope drives the sample to move in the Z direction, so as to form focused illumination regions at different positions in the thickness direction of the sample, specifically including:
determining the reference area of a focused illumination area formed on the sample by a focused illumination spot generated by a light source according to the minimum cross-sectional area of the sample;
adjusting the condensation degree of a focusing optical element in the light source on the light beam emitted by the light source according to the area of the reference region, so that the area of a light spot of a focusing illumination light spot generated by the light source is equal to the area of the reference region;
indicating an objective table of the microscope to drive the sample to move at preset step intervals in the Z direction so that a focusing illumination spot generated by the light source enters the sample, and respectively forming focusing illumination areas at different positions in the thickness direction of the sample; wherein the Z direction is a direction perpendicular to the plane of the stage.
The beneficial effects of the above technical scheme are: in actual operation, a biological sample obtained from a target object can be pretreated to ensure that the biological sample has certain light transmittance, so that light from a microscope light source can irradiate into the biological sample. And determining the reference area of a focused illumination area formed on the sample by the focused illumination spot generated by the light source by taking the minimum cross-sectional area of the biological sample in the thickness direction of the biological sample as a reference, so that the focused illumination spot generated by the light source can form a sufficiently large illumination area on the sample. The objective table through the indicating microscope drives the sample to move in the Z direction at a preset step interval, when the sample moves at a preset step interval, a focusing illumination light spot generated by the light source can form a corresponding focusing illumination area on a horizontal section corresponding to a certain position in the thickness direction of the sample, so that the microscope can carry out preliminary shooting on the formed focusing illumination area, and the preliminary analysis of structural characteristics is conveniently carried out on each horizontal section.
Preferably, in step S1, determining a reference area of a focused illumination area formed on the sample by the focused illumination spot generated by the light source according to the minimum cross-sectional area of the sample, specifically including:
the method comprises the steps of obtaining the minimum cross section area of a sample along the thickness direction of the sample, and determining that the area of a reference area of a focusing illumination area formed on the sample by a focusing illumination spot generated by a light source is equal to half of the minimum cross section area;
in step S1, adjusting a condensing degree of a light beam emitted by a light source by a focusing optical element inside the light source according to the area of the reference region, so that a spot area of a focused illumination spot generated by the light source is equal to the area of the reference region specifically includes:
and adjusting the beam shrinking ratio of a focusing optical element in the light source to the light beam area of the light beam emitted by the light source according to the area of the reference area, so that the light spot area of a focusing illumination light spot generated by the light source is equal to the area of the reference area.
The beneficial effects of the above technical scheme are: the area of a reference region of a focused illumination region formed on the sample by a focused illumination spot generated by the light source is set to be equal to half of the minimum cross section area of the sample, so that the focused illumination spot can form a large enough illumination region and enough illumination brightness on each horizontal section of the sample, and a microscope can clearly acquire the structural details of the sample on the horizontal section. In addition, in practical application, different types of 4f optical systems such as a Galileo telescopic optical system and the like can be arranged in the light source to serve as focusing optical elements, so that beam reduction processing with different proportions can be carried out on the cross section area of the light beam emitted by the light source, the cross section area of the light beam is increased or reduced, and the area of a focusing illumination spot generated by the light source is ensured to be always equal to the area of the reference area.
Preferably, in step S1, acquiring a horizontal cross-sectional image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal cross-sectional image, specifically including:
when an objective table of the microscope drives a sample to move in the Z direction for a preset step interval, a zoom objective of the microscope is instructed to carry out focusing shooting on a focusing illumination area formed by a focusing illumination spot generated by the light source in the sample, so that a horizontal section image corresponding to the position of the current focusing illumination area in the thickness direction of the sample is obtained;
and performing identification processing on the position of each horizontal sectional image in the thickness direction of the sample so that each horizontal sectional image corresponds to the position of each sample in the thickness direction in a one-to-one mode.
The beneficial effects of the above technical scheme are: when the object stage of the microscope drives the sample to move in the Z direction by a preset step interval, the light source forms a corresponding focusing illumination area on a horizontal section corresponding to the corresponding position in the thickness direction of the sample, and at the moment, the zoom objective of the microscope can find and position the horizontal section where the current focusing illumination area is located, and focus and shoot the focusing illumination area in the positioned horizontal section to obtain a horizontal section image corresponding to the position of the current focusing illumination area in the thickness direction of the sample. Each horizontal section image corresponds to a specific position in the thickness direction of the sample, and identification processing about the position of the horizontal section image in the thickness direction of the sample is carried out on each horizontal section image, so that sequential splicing and combination can be conveniently carried out on all horizontal section global images.
Preferably, in step S2, the identifying process is performed on each horizontal cross-sectional image to obtain the structural feature information of the sample on the horizontal cross-section corresponding to each position in the thickness direction of the sample, and the identifying process specifically includes:
carrying out shape recognition processing on each horizontal section image to obtain the sample section shape structure characteristic information on the horizontal section corresponding to each position in the thickness direction of the sample; the sample profile topographic structure characteristic information comprises sample profile undulating structure distribution position information and sample profile undulating structure size information.
The beneficial effects of the above technical scheme are: the topographic structure feature recognition processing is carried out on each horizontal section image, the distribution and size features of the fluctuation structure of each horizontal section can be obtained, the shooting datum points which are obvious in structure can be conveniently and accurately positioned on each horizontal section in the follow-up process, and therefore the comprehensive scanning shooting of each horizontal section is achieved.
Preferably, in step S2, determining a reference point of the horizontal section corresponding to each position according to the sample structure feature information includes:
extracting and obtaining distribution position information corresponding to the first three convex structures with the maximum curvature radius or distribution position information corresponding to the first three concave structures with the maximum curvature radius on the horizontal section corresponding to each position in the thickness direction of the sample from the sample structure characteristic information;
determining the central point of a triangle formed by the highest convex points of the first three convex structures with the maximum curvature radius according to the distribution position information corresponding to the first three convex structures with the maximum curvature radius; or determining the central point of a triangle formed by the lowest concave points of the first three concave structures with the maximum curvature radius according to the distribution position information corresponding to the first three concave structures with the maximum curvature radius;
and determining the central point as a shooting reference point corresponding to each position in the thickness direction of the sample.
The beneficial effects of the above technical scheme are: by the mode, the shooting reference points corresponding to each position in the thickness direction of the sample (namely each horizontal section) are determined by taking the first three maximum curvature radiuses of the convex structures or the concave structures existing in the horizontal sections as the reference, so that the object stage is indicated to drive the sample to move front and back, left and right in the X direction and the Y direction of the horizontal plane by taking each shooting reference point as the reference, and therefore comprehensive scanning micro-shooting of each horizontal section is achieved.
Preferably, in step S3, when the light source forms a focused illumination area at each position in the thickness direction of the sample, the stage is instructed to drive the sample to horizontally move in two dimensions according to the reference point, so as to obtain a plurality of horizontal cross-section sub-area images of different sub-areas of the horizontal cross-section at each position in the thickness direction of the sample, which specifically includes:
when the light source forms a focusing illumination area on a horizontal section corresponding to each position in the thickness direction of the sample, instructing a zoom objective lens of the microscope to perform zooming and zooming operations by taking a shooting reference point corresponding to each position as a reference so as to enable the current shooting field center of the zoom objective lens to coincide with the shooting reference point, and instructing the zoom objective lens to perform first shooting in a fixed shooting field range;
after the zoom objective lens finishes the first shooting, the objective table is indicated to respectively move at preset step intervals along the X direction and the Y direction of a horizontal plane so as to drive the sample to horizontally move in two dimensions;
and when the objective table finishes moving for a preset step interval along the X direction and the Y direction of a horizontal plane, the zoom objective lens is instructed to shoot the current focusing illumination area of the horizontal section corresponding to each position in the thickness direction of the sample, so that a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample are obtained.
The beneficial effects of the above technical scheme are: by the mode, the zoom objective of the microscope can be guaranteed to carry out comprehensive seamless scanning shooting on each horizontal section, and a plurality of horizontal section subarea images of different subareas of each horizontal section are obtained, so that the integral visual representation of each horizontal section is realized.
Preferably, in step S3, the identifying process is performed on each horizontal cross-section sub-area image to obtain image feature information of each horizontal cross-section sub-area image, and the identifying process specifically includes:
and carrying out image boundary identification processing on each horizontal section subarea image to obtain image texture feature information of the image boundary area of each horizontal section subarea image.
The beneficial effects of the above technical scheme are: and performing image boundary identification processing on each horizontal section subarea image to obtain image texture feature information of an image boundary area of each horizontal section subarea image, so that other horizontal section subarea images connected around each horizontal section subarea image can be determined by taking the image texture feature information as a reference, and the splicing correctness of the horizontal section subarea images is ensured.
Preferably, in step S4, according to the image feature information, all the horizontal section sub-region images corresponding to the horizontal section at the same position are subjected to stitching processing to obtain a horizontal section global image, which specifically includes:
and determining all horizontal section sub-region images adjacent to the periphery of each horizontal section sub-region image corresponding to the horizontal section at the same position according to the image texture feature information, and splicing each horizontal section sub-region image and all horizontal section sub-region images adjacent to the periphery of the horizontal section sub-region image to obtain a horizontal section global image.
The beneficial effects of the above technical scheme are: by the method, all horizontal section sub-region images which belong to the same position in the thickness direction of the same sample can be correctly spliced, and the spliced horizontal section global image is ensured to be consistent with the corresponding horizontal section.
Preferably, in step S4, the stitching processing is performed on the horizontal cross section global images corresponding to all the horizontal cross sections to obtain a spatial three-dimensional microscopic image of the sample, and the stitching processing specifically includes:
splicing the horizontal section global images corresponding to all the horizontal sections according to the sequence of the positions of all the horizontal sections corresponding to the Z direction from low to high so as to obtain a three-dimensional spliced sample image;
and carrying out pixel sharpening processing and image magnification uniformization processing on the three-dimensional spliced sample image so as to obtain a spatial three-dimensional microscopic image of the sample.
The beneficial effects of the above technical scheme are: by means of the mode, the horizontal section global images corresponding to all the horizontal sections are spliced according to the sequence from low to high of the positions of all the horizontal sections corresponding to the Z direction, and therefore the sample can be subjected to image representation in the three-dimensional X direction, the Y direction and the Z direction. In addition, pixel sharpening processing and image magnification factor uniformizing processing are carried out on the three-dimensional spliced sample image, so that the image detail consistency of each horizontal section global image contained in the three-dimensional spliced sample image can be ensured, and the situation that image distortion is caused due to the fact that the magnification factors of different horizontal section global images in the three-dimensional spliced sample image are different is avoided.
According to the content of the embodiment, the intelligent multi-dimensional microscopic image acquisition and processing method drives the sample to move in the Z direction through the objective table, so that light source light is focused at different positions in the thickness direction of the sample, a microscope is convenient to preliminarily shoot and screen the inside of the sample in the thickness direction, the inside of the sample is divided into a plurality of horizontal sections along the thickness direction of the sample, horizontal two-dimensional scanning and shooting are carried out on each horizontal section, the global image of each horizontal section is obtained, the global images of all the horizontal sections are spliced, the space three-dimensional microscopic image of the sample is obtained, image acquisition and splicing are carried out on different space layers in the sample, microscopic imaging of different dimensions in the sample is realized, the image acquisition comprehensiveness and the multi-dimensional performance of the microscopic imaging are improved, and sufficient image data are provided for subsequent target object diagnosis.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. The intelligent multi-dimensional microscopic image acquisition and processing method is characterized by comprising the following steps:
step S1, a light source of an instruction microscope irradiates illumination light to a sample, and an objective table of the instruction microscope drives the sample to move in a Z direction, so that focusing illumination areas are formed at different positions in the thickness direction of the sample; acquiring a horizontal section image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal section image;
s2, identifying each horizontal section image to obtain sample structure characteristic information on the horizontal section corresponding to each position in the thickness direction of the sample; determining a shooting reference point of a horizontal section corresponding to each position according to the sample structure characteristic information;
s3, when the light source forms a focusing illumination area at each position in the thickness direction of the sample, the objective table is indicated to drive the sample to horizontally move in two dimensions according to the shooting reference point, and a plurality of horizontal section sub-area images of different sub-areas of the horizontal section at each position in the thickness direction of the sample are shot and obtained; identifying each horizontal section subregion image to obtain image characteristic information of each horizontal section subregion image;
s4, according to the image characteristic information, splicing all horizontal section sub-area images corresponding to the horizontal section at the same position to obtain a horizontal section global image; and then splicing the horizontal section global images corresponding to all the horizontal sections to obtain a spatial three-dimensional microscopic image of the sample.
2. The intelligent multi-dimensional microscopic image acquisition and processing method according to claim 1, characterized in that:
in step S1, instruct the light source of microscope to shine the illumination light to the sample, instruct microscope' S objective table to drive the sample to move in the Z direction simultaneously to this forms the focus illumination region in sample self thickness direction different positions, specifically include:
determining the reference region area of a focused illumination region formed on the sample by a focused illumination spot generated by a light source according to the minimum cross-sectional area of the sample;
according to the area of the reference region, adjusting the condensation degree of a focusing optical element in the light source on the light beam emitted by the light source so as to enable the spot area of a focusing illumination spot generated by the light source to be equal to the area of the reference region;
indicating an objective table of the microscope to drive the sample to move at preset step intervals in the Z direction so that a focusing illumination spot generated by the light source enters the sample, and respectively forming focusing illumination areas at different positions in the thickness direction of the sample; wherein the Z direction is a direction perpendicular to the plane of the object stage.
3. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 2, wherein:
in step S1, determining a reference area of a focused illumination area formed on the sample by the focused illumination spot generated by the light source according to the minimum cross-sectional area of the sample, specifically including:
acquiring the minimum cross-sectional area of a sample along the thickness direction of the sample, and determining that the area of a reference area of a focusing illumination area formed on the sample by a focusing illumination spot generated by a light source is equal to half of the minimum cross-sectional area;
in step S1, adjusting a condensing degree of a light beam emitted by a light source by a focusing optical element inside the light source according to the area of the reference region, so that a spot area of a focused illumination spot generated by the light source is equal to the area of the reference region, specifically including:
and adjusting the beam reduction ratio of a focusing optical element in the light source to the beam area of the light beam emitted by the light source according to the area of the reference region, so that the spot area of the focused illumination spot generated by the light source is equal to the area of the reference region.
4. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 3, wherein:
in step S1, acquiring a horizontal cross-sectional image corresponding to each position in the thickness direction of the sample, and performing identification processing on each horizontal cross-sectional image, specifically including:
when an objective table of the microscope drives a sample to move in the Z direction by a preset step interval, a zoom objective of the microscope is instructed to carry out focusing shooting on a focusing illumination area formed by a focusing illumination spot generated by the light source in the sample, so that a horizontal section image corresponding to the position of the current focusing illumination area in the thickness direction of the sample is obtained;
and performing identification processing on the position of each horizontal sectional image in the thickness direction of the sample so that each horizontal sectional image corresponds to the position of each sample in the thickness direction in a one-to-one mode.
5. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 4, wherein:
in step S2, performing identification processing on each horizontal cross-sectional image to obtain sample structural feature information on a horizontal cross-section corresponding to each position in the sample thickness direction, specifically including:
carrying out shape recognition processing on each horizontal section image to obtain the sample section shape structure feature information on the horizontal section corresponding to each position in the thickness direction of the sample; the sample profile topographic structure characteristic information comprises sample profile undulating structure distribution position information and sample profile undulating structure size information.
6. The intelligent multi-dimensional microscopic image acquisition and processing method according to claim 5, characterized in that:
in step S2, determining a reference point of the horizontal section corresponding to each position according to the sample structure feature information, specifically including:
extracting and obtaining distribution position information corresponding to the first three convex structures with the maximum curvature radius or distribution position information corresponding to the first three concave structures with the maximum curvature radius on the horizontal section corresponding to each position in the thickness direction of the sample from the sample structure characteristic information;
determining the central point of a triangle formed by the highest convex points of the first three convex structures with the maximum curvature radius according to the distribution position information corresponding to the first three convex structures with the maximum curvature radius; or determining the central point of a triangle formed by the lowest concave points of the first three concave structures with the maximum curvature radius according to the distribution position information corresponding to the first three concave structures with the maximum curvature radius;
and determining the central point as a shooting reference point corresponding to each position in the thickness direction of the sample.
7. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 6, wherein:
in the step S3, when the light source forms a focused illumination area at each position in the thickness direction of the sample, the stage is instructed to drive the sample to perform horizontal two-dimensional movement according to the reference point, so as to obtain a plurality of horizontal cross-section sub-area images of different sub-areas of the horizontal cross section at each position in the thickness direction of the sample by shooting, which specifically includes:
when the light source forms a focusing illumination area on a horizontal section corresponding to each position in the thickness direction of the sample, instructing a zoom objective lens of the microscope to perform zooming and zooming operations by taking a shooting reference point corresponding to each position as a reference so as to enable the current shooting field center of the zoom objective lens to coincide with the shooting reference point, and instructing the zoom objective lens to perform first shooting in a fixed shooting field range;
when the zoom objective lens finishes the first shooting, the objective table is indicated to respectively move at preset step intervals along the X direction and the Y direction of a horizontal plane so as to drive the sample to horizontally move in two dimensions;
and when the objective table finishes moving for a preset step interval along the X direction and the Y direction of a horizontal plane, the zoom objective lens is instructed to shoot the current focusing illumination area of the horizontal section corresponding to each position in the thickness direction of the sample, so that a plurality of horizontal section subarea images of different subareas of the horizontal section at each position in the thickness direction of the sample are obtained.
8. The intelligent multi-dimensional microscopic image acquisition and processing method according to claim 7, characterized in that:
in step S3, performing identification processing on each horizontal-section sub-area image to obtain image feature information of each horizontal-section sub-area image, which specifically includes:
and carrying out image boundary identification processing on each horizontal section subregion image to obtain image texture feature information of the image boundary area of each horizontal section subregion image.
9. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 8, wherein:
in step S4, according to the image feature information, performing a stitching process on all the horizontal section sub-region images corresponding to the horizontal section at the same position to obtain a horizontal section global image, which specifically includes:
and determining all horizontal section sub-region images adjacent to the periphery of each horizontal section sub-region image corresponding to the horizontal section at the same position according to the image texture feature information, and splicing each horizontal section sub-region image and all horizontal section sub-region images adjacent to the periphery of the horizontal section sub-region image to obtain a horizontal section global image.
10. The intelligent multi-dimensional microscopic image acquisition and processing method of claim 9, wherein:
in step S4, the stitching processing is performed on the horizontal cross section global images corresponding to all the horizontal cross sections, so as to obtain a spatial three-dimensional microscopic image of the sample, which specifically includes:
splicing the horizontal section global images corresponding to all the horizontal sections according to the sequence of the positions of all the horizontal sections corresponding to the Z direction from low to high so as to obtain a three-dimensional spliced sample image;
and carrying out pixel sharpening processing and image magnification unification processing on the three-dimensional spliced sample image so as to obtain a spatial three-dimensional microscopic image of the sample.
CN202211609146.7A 2022-12-15 2022-12-15 Intelligent multi-dimensional microscopic image acquisition and processing method Active CN115598822B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211609146.7A CN115598822B (en) 2022-12-15 2022-12-15 Intelligent multi-dimensional microscopic image acquisition and processing method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211609146.7A CN115598822B (en) 2022-12-15 2022-12-15 Intelligent multi-dimensional microscopic image acquisition and processing method

Publications (2)

Publication Number Publication Date
CN115598822A true CN115598822A (en) 2023-01-13
CN115598822B CN115598822B (en) 2023-03-10

Family

ID=84853835

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211609146.7A Active CN115598822B (en) 2022-12-15 2022-12-15 Intelligent multi-dimensional microscopic image acquisition and processing method

Country Status (1)

Country Link
CN (1) CN115598822B (en)

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0667095A (en) * 1990-12-07 1994-03-11 Olympus Optical Co Ltd Scanning type laser microscope
US20020097490A1 (en) * 1999-03-23 2002-07-25 Olympus Optical Co., Ltd. Confocal microscope
JP3900368B1 (en) * 2006-08-17 2007-04-04 株式会社菱化システム LAYER SECTION MEASURING SYSTEM AND METHOD, AND COMPUTER PROGRAM
CN104603668A (en) * 2012-06-07 2015-05-06 索尼公司 Image processing apparatus, image processing program, and image processing method
CN104662463A (en) * 2012-09-28 2015-05-27 佳能株式会社 Image processing apparatus, imaging system, and image processing system
CN108982500A (en) * 2018-07-03 2018-12-11 怀光智能科技(武汉)有限公司 A kind of cervical liquid-based cells intelligence auxiliary diagosis method and system
CN109425978A (en) * 2017-08-25 2019-03-05 卡尔蔡司显微镜有限责任公司 High resolution 2 D microexamination with improved section thickness
CN110824686A (en) * 2019-12-09 2020-02-21 广东省特种设备检测研究院中山检测院 On-site portable super-depth-of-field 3D microscopic system and using method thereof
US20200242735A1 (en) * 2019-01-30 2020-07-30 Tsinghua University Super-resolution lattice light field microscopic imaging system and method
CN112363309A (en) * 2020-11-13 2021-02-12 杭州医派智能科技有限公司 Automatic focusing method and system for pathological image under microscope
CN114002190A (en) * 2020-07-28 2022-02-01 唐摩库柏公司 Three-dimensional optical diffraction tomography method and device
CN114067049A (en) * 2021-11-11 2022-02-18 四川泛华航空仪表电器有限公司 Model generation method based on optical microscopy

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0667095A (en) * 1990-12-07 1994-03-11 Olympus Optical Co Ltd Scanning type laser microscope
US20020097490A1 (en) * 1999-03-23 2002-07-25 Olympus Optical Co., Ltd. Confocal microscope
JP3900368B1 (en) * 2006-08-17 2007-04-04 株式会社菱化システム LAYER SECTION MEASURING SYSTEM AND METHOD, AND COMPUTER PROGRAM
CN104603668A (en) * 2012-06-07 2015-05-06 索尼公司 Image processing apparatus, image processing program, and image processing method
CN104662463A (en) * 2012-09-28 2015-05-27 佳能株式会社 Image processing apparatus, imaging system, and image processing system
CN109425978A (en) * 2017-08-25 2019-03-05 卡尔蔡司显微镜有限责任公司 High resolution 2 D microexamination with improved section thickness
CN108982500A (en) * 2018-07-03 2018-12-11 怀光智能科技(武汉)有限公司 A kind of cervical liquid-based cells intelligence auxiliary diagosis method and system
US20200242735A1 (en) * 2019-01-30 2020-07-30 Tsinghua University Super-resolution lattice light field microscopic imaging system and method
CN110824686A (en) * 2019-12-09 2020-02-21 广东省特种设备检测研究院中山检测院 On-site portable super-depth-of-field 3D microscopic system and using method thereof
CN114002190A (en) * 2020-07-28 2022-02-01 唐摩库柏公司 Three-dimensional optical diffraction tomography method and device
CN112363309A (en) * 2020-11-13 2021-02-12 杭州医派智能科技有限公司 Automatic focusing method and system for pathological image under microscope
CN114067049A (en) * 2021-11-11 2022-02-18 四川泛华航空仪表电器有限公司 Model generation method based on optical microscopy

Also Published As

Publication number Publication date
CN115598822B (en) 2023-03-10

Similar Documents

Publication Publication Date Title
CN108982500B (en) Intelligent auxiliary cervical fluid-based cytology reading method and system
EP3776458B1 (en) Augmented reality microscope for pathology with overlay of quantitative biomarker data
US8350905B2 (en) Microscope system, image generating method, and program for practicing the same
Rankov et al. An algorithm for image stitching and blending
EP2273302B1 (en) Image acquiring apparatus, image acquiring method and image acquiring program
JP6496708B2 (en) Computer mounting method, image analysis system, and digital microscope imaging system
US7756305B2 (en) Fast 3D cytometry for information in tissue engineering
JP5996334B2 (en) Microscope system, specimen image generation method and program
US10073258B2 (en) Microscope system
CN105026977A (en) Information processing device, information processing method, and information processing program
US9322782B2 (en) Image obtaining unit and image obtaining method
CN110793946A (en) Fungus sample microscopic imaging and intelligent identification system and method
CN110852999B (en) Image scanning system and image scanning method
JP2013044967A (en) Microscope system, specimen image forming method and program
He et al. Microscope images automatic focus algorithm based on eight-neighborhood operator and least square planar fitting
US7653260B2 (en) System and method of registering field of view
CN115598822B (en) Intelligent multi-dimensional microscopic image acquisition and processing method
CN114926357A (en) Self-correcting method for LED array light source pose of computed microscopy imaging system
US11356593B2 (en) Methods and systems for single frame autofocusing based on color- multiplexed illumination
JP5471715B2 (en) Focusing device, focusing method, focusing program, and microscope
CN115629072A (en) Bone marrow smear image analysis and diagnosis method and pathological section scanner device
CN112903675B (en) Sample analyzer and cell image processing method for sample analyzer
US11335454B2 (en) Biopsy device for digital pathology and artificial intelligent analytics
CN117237193A (en) Pathological image stitching method, system and storage medium based on pathological section association
Vogt III et al. Mosaic construction, processing, and review of very large electron micrograph composites

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant