Electrical impedance imaging method based on parameter optimization and boundary clustering

Abstract

The invention discloses an electrical impedance tomography method based on parameter optimization and boundary clustering, which combines penalty functions of two regularization algorithms Tikhonov and TV for application, proposes that a particle swarm algorithm is used for regularization parameter optimization of the combined penalty functions, and takes an image quality index (AL) as a fitness value of the particle swarm algorithm, so as to determine an optimal regularization parameter, and finally to obtain the electrical impedance tomography based on the parameter optimization and boundary clustering. The conductivity is obtained through a Newton iteration method, in order to further remove the artifacts, a boundary object B (artifacts) of the target is obtained through a Niblack algorithm, deep retrieval is conducted on the boundary object B of the target through a boundary clustering algorithm, all background pixels A are obtained, and the final conductivity distribution can be obtained. Simulation and actual measurement results show that the image reconstructed by the paper method can more accurately reflect the position information of the target object in the electric field, artifacts are effectively inhibited, and the reconstruction effect is improved.

Description

Technical Field

[0001] This invention belongs to the field of electrical impedance imaging technology, specifically relating to an electrical impedance imaging method based on parameter optimization and boundary clustering. Background Technology

[0002] Electrical impedance tomography (EIT) is a detection technique that visually presents changes in the internal conductivity of a measured object through images. EIT technology utilizes the different electrical response characteristics of different structural components within a conductive object to electrical excitation signals. By attaching a certain number of electrodes to the surface of the measured field, applying electrical excitation signals to different electrode pairs, and measuring the electrical response signals on the other electrode pairs sequentially, the changes in internal conductivity of the measured object are finally imaged using a corresponding inverse problem reconstruction algorithm. It is a non-invasive functional imaging method. Electrical impedance tomography has wide applications in medicine, geological exploration, fluid detection, and non-destructive testing of building interiors.

[0003] Electrical impedance imaging can accurately reflect the imaging effect of the contact surface, but the accuracy and stability of the imaging are still difficult to guarantee. There are often problems such as low resolution and poor reproduction of the reconstructed image, and the image quality is hard to be satisfactory. This phenomenon is also due to the inherent characteristics of the inverse problem, such as its ill-posedness and instability, as well as the limitations of electrical impedance imaging technology itself. Summary of the Invention

[0004] The purpose of this invention is to provide a parameter-optimized electrical impedance imaging method to address the inherent ill-posedness and instability of the inverse problem. To obtain a clearer reconstructed image, a method is proposed that combines two traditional algorithms to form a combined penalty function, and then optimizes the parameters of this combined penalty function.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A impedance imaging method based on regularized parameter optimization and boundary clustering includes the following steps:

[0007] S1. The target under test is divided into discrete elements using the finite element method, and the conductivity is discretized. Then, the conductivity change sensitivity matrix at different locations in the target under test is calculated.

[0008] S2. Combine the two regularization penalty functions of the Tikhonov regularization algorithm and the TV regularization algorithm, and use the particle swarm algorithm to select the optimal regularization parameters.

[0009] S3. Using the boundary potential distribution data of the target under test, the initial conductivity is obtained through regularized iteration;

[0010] S4, a Niblack algorithm is used to obtain a boundary object of the measured target;

[0011] S5, a boundary clustering algorithm is used for deep searching of the boundary object to obtain a final conductivity distribution, and electrical impedance imaging is completed.

[0012] As a further optimization scheme of the electrical impedance imaging method based on parameter optimization and boundary clustering, in step S1, a parameter matrix of each triangular subdivision unit is obtained according to impedance distribution data of the measured target and model data of the finite element, and then a total parameter matrix is obtained.

[0013] A boundary condition is applied to obtain a finite element equation, and a boundary voltage value is obtained by solving the finite element equation; the model data of the finite element refers to a grid on an image, and the boundary condition is an excitation current.

[0014] As a further optimization scheme of the electrical impedance imaging method based on parameter optimization and boundary clustering, in step S2, two regularization penalty functions of a Tikhonov regularization algorithm and a TV regularization algorithm are combined to obtain

[0015] ,

[0016] wherein J represents a sensitivity matrix, represents a conductivity difference, represents a voltage difference, represents a regularization parameter of the Tikhonov regularization algorithm, represents a regularization penalty function of the Tikhonov regularization algorithm, represents a regularization parameter of the TV regularization algorithm, represents a regularization penalty function of the TV regularization algorithm.

[0017] As a further optimization scheme of the electrical impedance imaging method based on parameter optimization and boundary clustering, in step S2, a fitness value of the particle swarm algorithm is an image quality index:

[0018] ,

[0019] wherein N is a total number of reconstructed pixels, and A' is a gradient of each pixel.

[0020] As a further optimization scheme of the electrical impedance imaging method based on parameter optimization and boundary clustering, the gradient A' at each pixel is calculated by averaging the abstract values of the differences between the pixel and its eight adjacent pixels.

[0021] The application further provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium.

[0022] The application further provides an electronic device, and the electronic device comprises:

[0023] a memory for storing the computer program;

[0024] a processor for executing the computer program to implement the steps of the electrical impedance imaging method based on parameter optimization and boundary clustering.

[0025] Compared with the prior art, the application has the following technical effects:

[0026] The electrical impedance imaging method based on parameter optimization provided by the application combines two regularization penalty functions of Tikhonov regularization algorithm and TV regularization algorithm, thereby effectively approximating the inverse problem of electrical impedance imaging, making the reconstructed image of electrical impedance more accurately reflect the shape and position information of the target object in the electric field, and effectively suppressing the generation of artifacts. Meanwhile, the particle swarm algorithm is used to optimize the parameters of the combined penalty function, thereby determining the two optimal parameters of the combined algorithm and obtaining the initial electrical conductivity. Then, the Niblack algorithm is combined with the boundary clustering algorithm to further process the initial electrical conductivity. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is an algorithm flowchart of the embodiment of the application.

[0028] Figure 2 is a real image of the measured object in the example of the application.

[0029] Figure 3 is the imaging effect generated by the method of the application. DETAILED DESCRIPTION

[0030] To make the purpose, technical effects and technical solutions of the embodiments of the application clearer, the technical solutions in the embodiments of the application are described clearly and completely in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application. It should be understood that the specific embodiments described herein are only used to explain the application and not used to limit the application.

[0031] This invention provides a parameter-optimized electrical impedance tomography (EIT) method. Building upon existing EIT research, it obtains the boundary voltage of a conductive thin film using non-destructive testing equipment, resulting in a measured dataset. To address the ill-conditioned nature and uncertainty of the EIT inverse problem, the commonly used and well-established Tikhonov regularization method is employed. However, considering the limitations of Tikhonov regularization in reconstructing images, such as numerous artifacts and unclear boundaries, a new penalty function (TV regularization) is introduced into the penalty function term. Then, a particle swarm optimization (PSO) algorithm is used to select the optimal regularization parameters for the new penalty function term, achieving the best imaging effect. The image quality index AL (Artifact Level) is used as the fitness value for the PSO algorithm; a smaller AL value indicates better imaging performance. Figure 1 As shown, the specific steps include:

[0032] S1. First, the region is divided into discrete elements using the finite element method, and the conductivity is discretized. Then, the sensitivity matrix of conductivity change at different locations in the measured region is calculated.

[0033] S2. In order to make the reconstructed image accurately reflect the size and position of the image, improve the image resolution, make the image boundary clear, and avoid artifact and staircase effects, the two regularization penalty functions of the Tikhonov regularization algorithm and the TV regularization algorithm are combined and applied through the study of two regularization methods.

[0034] Specifically, the combinatorial algorithm is as follows:

[0035] ,

[0036] Where J represents the sensitivity matrix. Indicates the difference in electrical conductivity. Indicates voltage difference. This represents the regularization parameter of the Tikhonov algorithm. This represents the Tikhonov algorithm function. This represents the regularization parameter of the TV algorithm. This represents the TV algorithm function.

[0037] Right now:

[0038] ,

[0039] Where L represents the regularization matrix, For the conductivity variance corresponding to different regions, β is a positive number that infinitely approaches 0. The range is all within (0, 1).

[0040] S3. The particle swarm optimization algorithm is used to select the two optimal regularization parameters. The image quality metric AL (Artifact Level) is used as the fitness value for the particle swarm optimization algorithm.

[0041] Specifically, for The regularization parameters are processed using a particle swarm optimization algorithm to select the two optimal regularization parameters. .

[0042] Set the population particle count, then initialize the individual particles, their optimal positions and values, and the global optimal position and value. Next, iterate according to the formula until the required precision or number of iterations is met. Finally, obtain the optimal regularization parameters. .

[0043] Specifically, the image quality metric AL (Artifact Level) serves as the fitness value:

[0044] ,

[0045] Where N is the total number of reconstructed pixels, and A' is a one-dimensional representation of the artifact map within the region, i.e., the gradient of each pixel. The lower the AL value, the better the EIT image quality. The gradient A' at each pixel is calculated by averaging the differences between that pixel and its eight neighboring pixels.

[0046] ,

[0047] Where H is the original reconstructed source image; A is the gradient of H; q is the current pixel; and k is a positive integer from 1 to 12, representing the 12 pixels adjacent to the current pixel.

[0048] ,

[0049] Specifically, regularization iteration:

[0050] ,

[0051] Right now

[0052]

[0053] Where J represents the sensitivity matrix; L represents the Tikhonov regularization matrix; M corresponds to the sparse matrix of gray-level variance characteristics; P is the diagonal matrix; and Q is the diagonal matrix.

[0054] ,

[0055] .

[0056] S4. Using the boundary voltage value of the object to be measured (such as carrots, cucumbers, etc. in a brine tank solution) as input data, the initial conductivity is obtained using the combined algorithm after parameter optimization.

[0057] S5. The boundary pixels (boundary object B) are obtained using the Niblack algorithm, thus obtaining the target boundary object B.

[0058] ,

[0059] ,

[0060] ,

[0061] Where O is the mean, P is the standard deviation, and Q is the gray value; Represents the i-th conductivity. This represents the i-th average value. This represents the i-th standard deviation. Let represent the conductivity of the i-th pixel, and α be a correction coefficient. k is a positive integer from 1 to 12, representing the 12 pixels adjacent to the current pixel i.

[0062] ,

[0063] ,

[0064] ,

[0065] Among them, integer values Take the value between the maximum and minimum values ​​in the standard deviation P. Standard deviation The probability of occurrence and Representing the two types of variance respectively and Probability of occurrence:

[0066] ,

[0067] when The larger the variance, the greater the deviation between the two types, and the more distinct the distinction. For an image, This indicates that the current region contains only target pixels or background pixels; The region represents the presence of both target pixels and background pixels.

[0068] ,

[0069] ,

[0070] Where O, P, Q, and B are all two-dimensional arrays with one row and X columns.

[0071] S6. Combined with the boundary clustering algorithm, the obtained initial conductivity is processed to obtain the final conductivity distribution and obtain image reconstruction. Figure 2 It is a true image of the object being measured. Figure 3 The image is generated according to the method of the present invention.

[0072] Specifically, the boundary voltage of the target object is obtained through non-destructive testing equipment to form a dataset. This involves attaching 16 electrode plates at equal intervals to the boundary of a two-dimensional circular region. The conductivity of the background region is set to 0.1 S / m, and the conductivity of the target region is set to 0.01 S / m. MATLAB software is used for programming, and an adjacent excitation-adjacent measurement mode is adopted to obtain the imaging effect of the measured object.

[0073] It should be noted that the above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​this application. For those skilled in the art, several improvements and modifications can be made to this application without departing from the principle of this application, and these improvements and modifications are also within the protection scope of the claims of this application.

[0074] Based on the same technical solution, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the electrical impedance imaging method based on parameter optimization and boundary clustering as described above.

[0075] Based on the same technical solution, the present invention also provides an electronic device, comprising:

[0076] Memory, used to store computer programs;

[0077] A processor is configured to execute the computer program to implement the steps of the electrical impedance imaging method based on parameter optimization and boundary clustering as described above.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A impedance imaging method based on parameter optimization and boundary clustering, characterized in that, Includes the following steps: S1. The target under test is divided into discrete elements using the finite element method, and the conductivity is discretized. Then, the conductivity change sensitivity matrix at different locations in the target under test is calculated. S2. Combine the two regularization penalty functions of the Tikhonov regularization algorithm and the TV regularization algorithm, and use the particle swarm algorithm to select the optimal regularization parameters. S3. Using the boundary potential distribution data of the target under test, the initial conductivity is obtained through regularized iteration; S4. Use the Niblack algorithm to obtain the boundary object of the target under test; S5. Based on the boundary clustering algorithm, perform deep retrieval of boundary objects to obtain the final conductivity distribution and complete electrical impedance imaging.

2. The electrical impedance imaging method based on parameter optimization and boundary clustering as described in claim 1, characterized in that, In step S1, the parameter matrix of each triangulated element is obtained based on the impedance distribution data of the target under test and the model data of the finite element method, and then the overall parameter matrix is ​​obtained. Apply boundary conditions to obtain the finite element equations, and solve the finite element equations to obtain the boundary voltage values; The finite element model data refers to the mesh on the image, and the boundary condition is the excitation current.

3. The electrical impedance imaging method based on parameter optimization and boundary clustering as described in claim 1, characterized in that, In step S2, the two regularization penalty functions of the Tikhonov regularization algorithm and the TV regularization algorithm are combined to obtain; ， Where J represents the sensitivity matrix. Indicates the difference in electrical conductivity. Indicates voltage difference. This represents the regularization parameters of the Tikhonov regularization algorithm. This represents the regularization penalty function of the Tikhonov regularization algorithm. This represents the regularization parameters of the TV regularization algorithm. This represents the regularization penalty function of the TV regularization algorithm.

4. The electrical impedance imaging method based on parameter optimization and boundary clustering as described in claim 3, characterized in that, In step S2, the fitness value of the particle swarm optimization algorithm is the image quality index: ， Where N is the total number of reconstructed pixels, and A' is the gradient of each pixel.

5. The electrical impedance imaging method based on parameter optimization and boundary clustering as described in claim 4, characterized in that, The gradient A' at each pixel is calculated by averaging the difference between that pixel and its eight neighboring pixels.

6. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the electrical impedance imaging method based on parameter optimization and boundary clustering as described in claims 1 to 5.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the electrical impedance imaging method based on parameter optimization and boundary clustering as described in claims 1 to 5 when executing the computer program.

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