Registration system and method for serial tilting projection images of needle-shaped sample of transmission electron microscope

Abstract

The invention discloses a serial tilt projection image registration system and method for a transmission electron microscope needle-shaped sample, and belongs to the technical field of transmission electron microscope three-dimensional reconstruction image registration, and the method comprises the following steps: obtaining a serial tilt projection image data set of the transmission electron microscope needle-shaped sample; carrying out binarization on the image data set; accumulating the series of binarized tilting projection images along the horizontal axis of the image, and then performing longitudinal drift correction on the series of tilting projection images based on the position of a needle tip; selecting any cross section of the series of tilting projection images after longitudinal drift correction, and then performing transverse drift correction on the series of tilting projection images, so that the area of a back projection intersection region of projections of the selected cross section at different tilting angles is maximized; and outputting the drift-corrected image and the drift parameter as a result. The method disclosed by the invention can still obtain accurate registration for a sample which is not allowed or not suitable for introducing an external marker; and the search range of optimization parameters can be fully reduced.

Description

Technical Field

[0001] This invention belongs to the field of transmission electron microscopy three-dimensional reconstruction image registration technology, specifically relating to a series of tilt projection image registration systems and methods for transmission electron microscopy needle-shaped samples. Background Technology

[0002] With the development of electron microscopy, transmission electron microscopy (TEM) plays a crucial role in research on nanomaterials, life sciences, and crystallography. The working principle of TEM is to use a high-energy electron beam to penetrate the sample and generate a contrast image after the beam leaves, reflecting information about the sample's internal structure. To obtain the three-dimensional structure of the sample, it is necessary to tilt the sample within a certain range in the TEM (typically -60° to +60°), and take at least one image at each tilt angle. Theoretically, if the changes in the projection positions of these images at each tilt angle in the direction parallel to the rotation axis conform to a sinusoidal function, i.e., without translation or rotation errors, then a high-precision three-dimensional reconstruction result can be directly obtained using classical back projection algorithms or weighted back projection (WBP) algorithms. Using a series of projection images taken by TEM at different tilt angles (i.e., a series of tilt projections), the three-dimensional structural information of the sample can be obtained through three-dimensional reconstruction techniques. This technique is commonly referred to as electron tomography.

[0003] However, in actual operation, due to factors such as heating of the sample support film and grid, or mechanical vibration of the goniometer stage, it is impossible to maintain absolute stillness during imaging. Furthermore, factors such as electron beam irradiation can cause the sample itself to drift to some extent. This results in images from different angles no longer being perfectly aligned. If these "misaligned" images are directly back-projected during 3D reconstruction, significant errors will be introduced into the reconstruction result, leading to distortion or reduced resolution of the final 3D reconstructed image. Therefore, image registration of projected images from different angles is a crucial and indispensable step in the electron tomography process.

[0004] The methods commonly used for registration of tilt projection images in transmission electron microscopy can be broadly classified into two categories:

[0005] (1) Marker-based registration method (Ultramicroscopy 35, 1991, 37-43): This method pre-places high-contrast, positionally stable markers such as gold nanoparticles on the sample surface or a grid, identifies their coordinates in the projected image, and estimates the drift and rotation between images to achieve accurate registration. Its advantages are that it is suitable for high-resolution 3D reconstruction and the algorithm is mature; however, its limitations include: some samples cannot introduce markers, improper number or distribution of markers will reduce the registration effect, and markers may interfere with experiments or not conform to specifications.

[0006] (2) Cross-correlation-based registration method (Ultramicroscopy 30, 1989, 337-348): When markers cannot be added, cross-correlation or image feature matching methods can be used to estimate the drift and rotation between images. Cross-correlation can be performed in the spatial or frequency domain, and can also be extended to phase correlation to handle small-angle rotations or scale changes. However, this method has high requirements for image signal-to-noise ratio and texture consistency, and is difficult to apply to transmission electron microscopy diffraction contrast images lacking significant structure.

[0007] Traditional registration methods primarily rely on external markers (such as gold nanoparticles) or salient feature points in the image. By detecting the positions of these identifiable structures in different projections, drift and rotation corrections between images are achieved. However, due to considerations of minimizing damage and avoiding signal interference, external markers are often not introduced. Furthermore, such samples typically lack stable, identifiable features or structures, making conventional methods based on cross-correlation or feature point matching ineffective for registration. Under the condition of "no markers and no salient features," existing algorithms struggle to meet the requirements for high-quality 3D reconstruction in terms of convergence, repeatability, and registration accuracy.

[0008] Regarding samples used in 3D reconstruction applications, needle-shaped samples have advantages such as reducing loss wedges and being easy to combine with 3D atom probe technology, and have been widely used in the field of 3D reconstruction.

[0009] Advantages of needle-shaped samples:

[0010] (1) Eliminating the Missing Wedge Effect (Ultramicroscopy 107, 2007, 8–15): For traditional sheet-like samples, when tilted to greater than 70 degrees in a transmission electron microscope (TEM), the signal-to-noise ratio of the acquired images drops sharply. When the tilt angle increases further to 90 degrees, the electron beam of the TEM cannot penetrate the sample at all. This results in the fact that, in most cases, only projection image datasets with a tilt angle range of less than ±80 degrees can be acquired during 3D data acquisition, leading to the 3D reconstruction results being affected by the Missing Wedge Effect. However, for needle-like samples, due to their special geometry, the tilt range during data acquisition can exceed ±90 degrees, thereby eliminating the Missing Wedge Effect and ultimately improving the accuracy of 3D reconstruction.

[0011] (2) Applicable to three-dimensional atom probe technology (Nature Communication 15, 2024, 9870): The needle-shaped sample used for three-dimensional reconstruction by transmission electron microscopy can also be used for three-dimensional atom probe technology. The combination of three-dimensional reconstruction by transmission electron microscopy and three-dimensional atom probe technology can realize the coupled analysis of three-dimensional structure and elements with high spatial resolution.

[0012] Therefore, there is an urgent need to develop a universal registration method that does not require markers or rely on image features, and can achieve high-precision, automated drift correction of a series of tilt projection images of needle-shaped samples from transmission electron microscopy, providing a reliable basis for the three-dimensional structural analysis of needle-shaped samples. Summary of the Invention

[0013] This invention provides a series of tilt projection image registration systems and methods for transmission electron microscopy needle-shaped samples. The above method does not require markers or significant feature points, corrects longitudinal drift by aligning the needle tip apex, and corrects lateral drift by using an optimized algorithm.

[0014] This invention is achieved through the following technical solution:

[0015] On one hand, the present invention provides a method for registering a series of tilt projection images of a needle-shaped sample obtained by transmission electron microscopy, comprising the following steps:

[0016] Obtain a series of tilt projection image datasets of needle-shaped samples for transmission electron microscopy;

[0017] Binarize the image dataset;

[0018] The series of binarized tilt projection images are accumulated along the horizontal axis of the image, and then the longitudinal drift of the series of tilt images is corrected based on the position of the needle tip.

[0019] Select any section of the series of tilted projection images after longitudinal drift correction, and then perform lateral drift correction on the series of tilted images to maximize the area of ​​the back projection intersection region of the selected section at different tilt angles.

[0020] Output the drift-corrected image and the drift parameters as the result.

[0021] In some embodiments, when performing lateral drift correction on a series of tilted images, the lateral drift correction is performed based on an optimization algorithm; the optimization algorithm includes at least one of a genetic algorithm and a particle swarm optimization algorithm.

[0022] In some embodiments, when performing lateral drift correction on a series of tilt images based on an optimization algorithm, the drift amount is updated in each iteration, and the objective function is to maximize the area of ​​the intersection region of the back projection images of all angle contours of the cross section.

[0023] In some embodiments, when performing lateral drift correction on a series of tilted images, the lateral drift correction algorithm performs multiple global iterations in the outer loop; for each projection angle, the projection profile drift of the cross section under that projection angle and adjacent projection angles is used as a series of optimization variables, and a genetic algorithm is called to construct an optimization function; the optimization function adjusts the corresponding projection according to the specified drift parameters, calculates the superposition degree of the current projection set in the image domain through back projection operation, and records the drift amount when the maximum superposition degree is reached.

[0024] In some embodiments, the optimization parameters of the optimization algorithm include at least one of population size, maximum number of iterations, and population initialization method.

[0025] In some embodiments, lateral drift correction of a series of tilt images includes:

[0026]

[0027] Among them, C i (Δx i ,Δy i This involves dividing the binarized image by Δx. i ,Δy i After translation, select a position at a distance from the needle tip and back-project the binarized profile at various tilt angles perpendicular to the tilt axis at that position. This indicates the size of the intersection region of the binary profile back projection at each of the above tilt angles; These are the optimal drift correction values ​​for the projected image in the horizontal and vertical directions, respectively.

[0028] In some embodiments, when binarizing the image dataset, each image is binarized by threshold segmentation.

[0029] On the other hand, embodiments of this application provide a series of tilt projection image registration systems for transmission electron microscope needle-shaped samples, characterized in that it includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the series of tilt projection image registration methods for transmission electron microscope needle-shaped samples according to any of the above embodiments.

[0030] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0031] (1) Marker-free applicability: It can still achieve accurate registration for samples that do not allow or are not suitable for introducing external markers.

[0032] (2) Computational efficiency: Iterative correction of the projection of adjacent tilt angles of the same cross section is performed, which greatly reduces the search range of optimization parameters.

[0033] (3) Global optimization: Global search methods such as genetic algorithms can effectively avoid getting trapped in local extrema and provide a global optimal solution for determining the drift amount.

[0034] (4) Ease of operation: Users only need to set the tilt image set and basic parameters (angle range, upper and lower limits of drift, maximum number of iterations, etc.) to run automatically, reducing human intervention. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A schematic diagram of the overall process of a series of tilt projection image registration methods for a needle-shaped sample under transmission electron microscopy provided in some embodiments of the present invention;

[0037] Figure 2 A schematic diagram of the overall process of a series of tilt projection image registration methods for a needle-shaped sample under transmission electron microscopy provided in some other embodiments of the present invention;

[0038] Figure 3 The following are transmission electron microscope bright field images of needle-shaped samples at different tilt angles in some embodiments of the present invention: (a) projection at 0°, (b) projection at -10°, (c) projection at 10°. The dashed line is the positioning reference line. The projections at different tilt angles show obvious drift.

[0039] Figure 4 for Figure 3 The corresponding binarized image;

[0040] Figure 5 The following are grayscale images of the projected images accumulated horizontally in some embodiments of the present invention, with the projection at 0 degrees as the reference image without drift. (a) Schematic diagram of the projected images accumulated horizontally, (b) Horizontal accumulation images of 0°, -10° and 10° from left to right, respectively. The black dashed lines 1, 2 and 3 represent the positions of their vertices, and the arrows indicate the difference between the vertices of -10° and 10° and the vertices of 0°.

[0041] Figure 6 Here are the images after longitudinal drift correction in some embodiments of the present invention (the result of aligning the horizontally accumulated images along the longitudinal direction): drift-corrected projections (a) 0°, (b) -10°.

[0042] (c) 10°, grayscale images of the aligned projected image accumulated horizontally: (d) 0°, (e) -10°,

[0043] (f) 10°, the dashed line is the baseline;

[0044] Figure 7 This is a schematic diagram of back projection involved in the optimization algorithm in some embodiments of the present invention. P1, P2, and P3 represent projections at different tilt angles. By back-projecting P1, P2, and P3 along their corresponding tilt angles, the intersection area can be obtained. The straight line shaded area represents the intersection area of ​​P1, P2, and P3, and the grid shaded area represents the reconstructed sample area obtained by back-projecting all tilt angles after drift correction.

[0045] Figure 8 In some embodiments of the present invention, (a) a sine curve of a cross section is projected after longitudinal alignment (but lateral drift is not corrected); and (b) the sine curve of the cross section after lateral drift is corrected using the method of the present invention.

[0046] It should be noted that the above figures are only used to further illustrate the core principles and main processes of the present invention, and the specific implementation may be adjusted or refined according to the algorithm requirements. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0048] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., used to indicate the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this invention is usually placed in during use. They are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0049] Furthermore, the use of terms such as "horizontal" and "vertical" in the description of this invention does not imply that the components are required to be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0050] The terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0051] In the description of this invention, the terms "registration" and "alignment" should be understood as meaning that the images achieve pixel-level precision, rather than absolute registration with no errors. This implies that the goal of registration is to minimize the differences between images to ensure consistency at the microscopic level. Furthermore, in this invention, "diffraction contrast" and "projection" refer to sample images captured under a transmission electron microscope. The images involved can be acquired at different tilt angles, and the two terms are used interchangeably in the context without strict distinction.

[0052] On one hand, the present invention provides a method for registering a series of tilt projection images of a needle-shaped sample obtained by transmission electron microscopy, comprising the following steps:

[0053] Obtain a series of tilt projection image datasets of needle-shaped samples for transmission electron microscopy;

[0054] Binarize the image dataset;

[0055] The series of binarized tilt projection images are accumulated along the horizontal axis of the image, and then the longitudinal drift of the series of tilt images is corrected based on the position of the needle tip.

[0056] Select any section of the series of tilted projection images after longitudinal drift correction, and then perform lateral drift correction on the series of tilted images to maximize the area of ​​the back projection intersection region of the selected section at different tilt angles.

[0057] Output the drift-corrected image and the drift parameters as the result.

[0058] In some embodiments, when performing lateral drift correction on a series of tilted images, the lateral drift correction is performed based on an optimization algorithm; the optimization algorithm includes at least one of a genetic algorithm and a particle swarm optimization algorithm.

[0059] In some embodiments, when performing lateral drift correction on a series of tilt images based on an optimization algorithm, the drift amount is updated in each iteration, and the objective function is to maximize the area of ​​the intersection region of the back projection images of all angle contours of the cross section.

[0060] In some embodiments, when performing lateral drift correction on a series of tilted images, the lateral drift correction algorithm performs multiple global iterations in the outer loop; for each projection angle, the projection profile drift of the cross section under that projection angle and adjacent projection angles is used as a series of optimization variables, and a genetic algorithm is called to construct an optimization function; the optimization function adjusts the corresponding projection according to the specified drift parameters, calculates the superposition degree of the current projection set in the image domain through back projection operation, and records the drift amount when the maximum superposition degree is reached.

[0061] In some embodiments, the optimization parameters of the optimization algorithm include at least one of population size, maximum number of iterations, and population initialization method.

[0062] In some embodiments, lateral drift correction of a series of tilt images includes:

[0063]

[0064] Among them, C i (Δx i ,Δy i This involves dividing the binarized image by Δx. i ,Δy i After translation, select a position at a distance from the needle tip and back-project the binarized profile at various tilt angles perpendicular to the tilt axis at that position. This indicates the size of the intersection region of the binary profile back projection at each of the above tilt angles; These are the optimal drift correction values ​​for the projected image in the horizontal and vertical directions, respectively.

[0065] In some embodiments, when binarizing the image dataset, each image is binarized by threshold segmentation.

[0066] On the other hand, embodiments of this application provide a series of tilt projection image registration systems for transmission electron microscope needle-shaped samples, characterized in that it includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the series of tilt projection image registration methods for transmission electron microscope needle-shaped samples according to any of the above embodiments.

[0067] This invention provides a method for registering a series of tilt projection images of a needle-like sample obtained by transmission electron microscopy, comprising the following steps: First, import a series of projection images of the needle-like sample obtained at different tilt angles, and binarize each image by threshold segmentation; then, using the tip of the needle as a reference, longitudinal drift correction is achieved by aligning the tip of the needle in each image; based on the longitudinal drift correction, a sample cross-section is further selected as a reference, and an optimization algorithm is used to correct the lateral drift of the projection images at each tilt angle; the optimization algorithm takes maximizing the intersection area of ​​the cross-section and the back projection of adjacent angles of the cross-section as the objective function, and continuously optimizes the drift parameters through iteration, finally finding a global or near-global solution that can accurately register the images, obtaining a series of accurately registered tilt projection images and drift amounts, thus achieving high-precision registration of the series of tilt images. The optimization problem of correcting lateral drift can be described by Equation 1.

[0068]

[0069] C i (Δx i ,Δy i This involves dividing the binarized image by Δx. i ,Δy i After translation, select a position at a distance from the needle tip and back-project the binarized profile at various tilt angles perpendicular to the tilt axis.

[0070] This represents the size of the intersection region of the binary profile back projection at each of the above tilt angles.

[0071] These are the optimal drift correction values ​​for the projected image in the horizontal and vertical directions, respectively.

[0072] In some embodiments, the optimization algorithm can be a genetic algorithm, which iteratively optimizes a series of tilt images to achieve better alignment. In some specific embodiments, a genetic algorithm (GA) is used to optimize the drift parameters between projections to maximize the intersection area of ​​the back projections of all tilt angle profiles of the cross section. The larger the intersection area, the better the drift correction effect and the more accurate the drift amount, thus enabling the evaluation of the quality of the current drift correction.

[0073] In some embodiments, the lateral drift correction algorithm performs multiple global iterations in the outer loop. For each projection angle, the drift of the projected profile of the cross-section at that projection angle and adjacent projection angles is used as a series of optimization variables. A genetic algorithm is invoked to construct an optimization function. The optimization function first adjusts the corresponding projection according to the specified drift parameters, then calculates the superposition degree of the current projection set through backprojection in the image domain, and records the drift amount when the maximum superposition degree is reached. The genetic algorithm continuously tries different drift amounts, iterating the drift amounts at different angles, thereby finding a global or near-global solution for the drift amounts at all angles.

[0074] In some embodiments, the optimization parameters of the optimization algorithm include population size, maximum number of iterations, and population initialization method. These parameters can be flexibly adjusted according to specific hardware conditions and data scale to obtain better optimization results within a reasonable computation time.

[0075] In some embodiments, to accelerate the optimization process and improve computational efficiency, the algorithm can utilize MATLAB's Parallel Computing Toolbox to parallelize the computation of the optimization function. Parallel computing can significantly reduce the algorithm's execution time. Simultaneously, the program also limits the search range of the drift by defining appropriate upper and lower bounds, thereby ensuring that the optimization does not deviate from a reasonable search space. This constraint reduces the search difficulty of the optimization algorithm and helps it converge faster.

[0076] The following describes in detail, step by step, a method for registering a series of tilt projection images of a needle-shaped sample for transmission electron microscopy, provided by an embodiment of the present invention. The main steps are as follows:

[0077] S1. First, input the diffraction contrast image dataset of needle-shaped samples acquired in a transmission electron microscope at different tilt angles, and determine the size, tilt angle range, and other relevant parameters for each projected image. In this step, each image in the diffraction contrast image dataset does not need to contain obvious markers or features; the algorithm is still applicable to images with inconspicuous features. The tilt angle range should be consistent with the actual acquisition range used in the experiment. Other parameters, such as the save path and the upper and lower bounds of the drift parameters, can be set according to actual needs to ensure the efficiency and stability of data storage and algorithm operation.

[0078] S2. Use image processing methods to binarize each image to obtain binarized images projected at different tilt angles.

[0079] S3. Run the image registration program to begin registration processing on a series of tilt projection images. Using the tip of a pin as a reference, longitudinal drift correction is achieved by aligning the tips of each image.

[0080] S4. Based on longitudinal drift correction, a sample cross-section is further selected as a reference, and an optimization algorithm is used to perform lateral drift correction on the projected images at each tilt angle. The optimization algorithm takes maximizing the intersection area of ​​the cross-section and the back projection of adjacent angles of the cross-section as the objective function. Through iterative optimization of the drift parameters, it finally finds a global or near-global solution that can accurately register the images, obtaining a series of accurately registered tilted projected images and drift amounts, thus achieving high-precision registration of the series of tilted images. The optimization algorithm can be a genetic algorithm, which achieves better alignment of the series of tilted images through iterative optimization. In a specific embodiment, a genetic algorithm (GA) is used to optimize the drift parameters between each projection to maximize the intersection area of ​​the cross-section and the back projection of adjacent angles of the interface. The larger the intersection area, the better the drift correction effect and the more accurate the drift amount, thus enabling the evaluation of the current drift correction. The genetic algorithm improves the computing speed through parallel computation, and the stability and reliability of the algorithm are ensured by setting a maximum number of iterations. After the maximum number of iterations is reached, the program will automatically stop and output the final optimization result.

[0081] S5. Finally, save the obtained drift amount and the corrected projection image for further analysis and application.

[0082] The method provided by this invention can achieve high-precision registration and crystallographic orientation imaging of transmission electron microscopy images in a short time, without requiring strict feature calibration of diffraction contrast images or requiring images to have completely consistent features, thus exhibiting strong adaptability. Compared with existing technologies, this invention provides a more efficient, reliable, and flexible solution, especially suitable for the high-precision imaging needs of complex samples, and has broad application prospects and practical value.

[0083] The steps and their corresponding numbers are provided for ease of understanding only and do not impose any restrictions on the order of the steps. Within reasonable limits, the order of the steps may be partially adjusted or replaced.

[0084] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for registering a series of tilt projection images of a needle-shaped sample obtained by transmission electron microscopy, characterized in that, Includes the following steps: Obtain a series of tilt projection image datasets of needle-shaped samples for transmission electron microscopy; Binarize the image dataset; The series of binarized tilt projection images are accumulated along the horizontal axis of the image, and then the longitudinal drift of the series of tilt images is corrected based on the position of the needle tip. Select any section of the series of tilted projection images after longitudinal drift correction, and then perform lateral drift correction on the series of tilted images to maximize the area of ​​the back projection intersection region of the selected section at different tilt angles. Output the drift-corrected image and the drift parameters as the result.

2. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to claim 1, characterized in that, When performing lateral drift correction on a series of tilted images, the lateral drift correction is performed based on an optimization algorithm; the optimization algorithm includes at least one of genetic algorithm and particle swarm optimization algorithm.

3. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to claim 2, characterized in that, When performing lateral drift correction on a series of tilt images based on an optimization algorithm, the drift amount is updated in each iteration, and the objective function is to maximize the area of ​​the intersection region of the back projection images of all angle contours of the cross section.

4. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to claim 2, characterized in that, When performing lateral drift correction on a series of tilt images, the lateral drift correction algorithm performs multiple global iterations in the outer loop; for each projection angle, the projection profile drift of the cross section under that projection angle and adjacent projection angles is used as a series of optimization variables, and a genetic algorithm is called to construct an optimization function; The optimization function adjusts the corresponding projection according to the specified drift parameters, calculates the superposition degree of the current projection set in the image domain through back projection operation, and records the drift amount when the maximum superposition degree is reached.

5. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to claim 2, characterized in that, The optimization parameters of the optimization algorithm include at least one of the following: population size, maximum number of iterations, and population initialization method.

6. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to any one of claims 1-5, characterized in that, Lateral drift correction for a series of tilt images includes: Among them, C i (Δx i ,Δy i This involves binarizing the image according to Δx. i ,Δy i After translation, select a position at a distance from the needle tip and back-project the binarized profile at various tilt angles perpendicular to the tilt axis at that position. This indicates the size of the intersection region of the binary profile back projection at each of the above tilt angles; These are the optimal drift correction values ​​for the projected image in the horizontal and vertical directions, respectively.

7. The method for registering a series of tilt projection images of a needle-shaped sample under transmission electron microscopy according to claim 1, characterized in that, When binarizing an image dataset, each image is binarized using threshold segmentation.

8. A series of tilt projection image registration systems for transmission electron microscopy needle-shaped samples, characterized in that, The device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the series of tilt projection image registration methods for transmission electron microscopy needle samples according to any one of claims 1-7.

Images