Three-dimensional reconstruction simulation system for medical image processing and analysis

Abstract

The invention relates to a medical image processing and three-dimensional reconstruction simulation system. The medical image processing and three-dimensional reconstruction simulation system comprises image loading, image processing, three-dimensional reconstruction, simulation analysis and multi-view display. The system loads various medical image formats, provides real-time display of axial, sagittal and coronal slices, extracts information of key parts by using an image segmentation strategy, generates a high-precision three-dimensional model based on a Marching Cubes algorithm, supports thermal and electrophysiology analysis, can automatically input physiological parameters, simulates heat distribution and current distribution of tissues, supports interactive adjustment, and is high in accuracy and high in accuracy. And a user can conveniently observe image details and analysis results. By using the system, the medical image processing efficiency and the three-dimensional reconstruction precision can be improved, meanwhile, the multi-dimensional analysis requirement of a complex anatomical structure is met through a simulation analysis function, and the system can be widely applied to medical diagnosis, surgical planning and medical teaching scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical image processing and analysis, and particularly relates to a three-dimensional reconstruction and simulation system based on medical image data, which is used for processing, analysis and visualization of medical images. BACKGROUND

[0002] The existing medical image processing system is usually limited to image enhancement, segmentation and simple three-dimensional reconstruction operation, and it is difficult to meet the multi-dimensional data analysis and simulation requirements in complex medical scenarios, such as tissue thermal analysis and electrophysiological simulation. The traditional system has many deficiencies in precision, interactivity and function integration, and cannot provide efficient and convenient auxiliary diagnosis tools. SUMMARY

[0003] In order to solve the above problems in the prior art, the present application provides a medical image processing and three-dimensional reconstruction simulation system, which integrates image loading, image processing, three-dimensional reconstruction and multi-dimensional simulation analysis functions, can efficiently generate accurate three-dimensional models, and supports thermal analysis and electrophysiological simulation, specifically comprising:

[0004] An image loading and control module supports loading of medical image data (such as DICOM, NIfTI, etc.), and can set window width and window level to enhance image display effect.

[0005] An image segmentation and three-dimensional reconstruction module provides image processing functions based on threshold segmentation and region growing segmentation, and realizes high-precision three-dimensional reconstruction through Marching Cubes algorithm.

[0006] A thermal analysis module realizes simulation of tissue thermal behavior according to input thermal parameters (such as temperature range, thermal conductivity, etc.). An electrophysiological simulation module simulates the electrophysiological properties of the tissue by inputting conductivity, stimulation intensity and other parameters.

[0007] A multi-view display module supports real-time display and adjustment of axial, sagittal and coronal section slices.

[0008] An interactive operation module allows users to adjust the slice position through a slider and observe different layers of image data in real time.

[0009] The present application has at least the following advantages: one-stop operation from loading, processing to simulation analysis of medical image data is realized; the precision and efficiency of three-dimensional reconstruction are improved, and multi-view synchronous display function is provided; thermal and electrophysiological simulation functions are integrated to provide auxiliary support for medical research and clinical diagnosis; a friendly interactive interface is provided, which is convenient to operate and has high module function integration. BRIEF DESCRIPTION OF DRAWINGS

[0010] For further elucidation of the above and other advantages and features of the embodiments of the present application, reference will be made to the drawings.

[0011] More specific description of the embodiments of the present application. It is to be understood that these drawings only depict typical embodiments of the present application and therefore are not to be considered limiting of its scope. In the drawings, like or similar components are denoted by the same last two digits or by similar reference characters.

[0012] Figure 1 : System overall architecture function schematic diagram.

[0013] Figure 2 : Image segmentation and three-dimensional reconstruction module flow chart.

[0014] Figure 3 : Thermal and electrophysiological simulation module schematic diagram.

[0015] Figure 4 : Multi-view display interface interaction schematic diagram. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments.

[0017] Please refer to Figure 1 , the present application provides an embodiment: a three-dimensional reconstruction simulation system for medical image processing and analysis.

[0018] It is characterized in that it comprises the following specific steps:

[0019] Step 1: the user loads the medical image file.

[0020] Step 2: adjust the window width and window level to optimize the image display effect.

[0021] Step 3: observe the image data in the multi-view display area and adjust the slice position through the slider.

[0022] Step 4: select the segmentation method (threshold segmentation or region growing segmentation) to complete the image segmentation operation.

[0023] Step 5: click the "three-dimensional reconstruction" button to generate and display the three-dimensional model of the target area.

[0024] Step 6: perform mesh division, select the thermal analysis or electrophysiological simulation module, input the corresponding parameters, click the "start analysis" button, generate the simulation results, and display the distribution effect diagram on the three-dimensional model.

[0025] Please refer to Figure 2Another embodiment of the present application provides a specific method for image strategy:

[0026] Method 1 threshold segmentation, Otsu threshold segmentation is an automatic threshold selection method based on image gray histogram, aiming to achieve the best segmentation of the image by maximizing the inter-class variance. This method is suitable for two-class segmentation, that is, the pixels in the image can be divided into foreground and background two classes. Specifically, the core idea of Otsu method is to calculate the gray level distribution of the image and analyze all possible thresholds, and select the threshold that makes the inter-class variance of the segmented class maximum as the final segmentation threshold. First, get the gray histogram of the image, and calculate the probability distribution of all gray levels in the image. Let the gray level of the image be L (usually 0 to 255 gray levels), and for each gray level i, its probability is: Where n i is the number of pixel points of gray level i, and N is the total number of pixels in the image. Calculate the global average gray: the global average gray μ T of the image is calculated by the formula: This value represents the weighted average of all gray levels in the image.

[0027] Calculate the inter-class variance: for each possible threshold t, the Otsu method will divide the image into two classes: the foreground class (gray value greater than or equal to t) and the background class (gray value less than t). On this basis, the mean and variance of the foreground class and the background class are calculated respectively: the probability ω1(t) and the mean μ1(t)

[0028] of the foreground class

[0029] The inter-class variance is calculated by the formula:

[0030]

[0031] Where:

[0032]

[0033] The formula for calculating the class mean μ1(t) and μ2(t) is: Select the optimal threshold: traverse all possible thresholds t, calculate the inter-class variance of each threshold, and select the threshold t opt corresponding to the maximum inter-class variance, that is:

[0034]

[0035] The threshold t opt The threshold value t is considered to be the best segmentation threshold value of the image, and is applied to the image to complete the binaryzation segmentation of the image. Method 2 region growing segmentation, which is a region feature-based image segmentation method, its main idea is to gradually classify similar neighborhood pixels into the region from one or more seed points until certain stopping criteria are met. The core of region growing segmentation is to determine whether a certain pixel is classified into the current region according to the similarity of the pixels in the image (such as gray value, color, texture, etc.).

[0036] The region growing segmentation method first selects an initial seed region or seed point, and then checks each neighborhood pixel in turn whether it meets certain similarity criteria (such as gray value difference, color difference, etc.). If the criteria are met, the pixel is classified into the current region, and the pixel is used as a new seed point to continue expanding the region. Region growing segmentation will continue to expand until no pixel meeting the criteria can be found.

[0037] The specific steps of the three-dimensional reconstruction strategy are as follows:

[0038] Step 1: First, segment the medical image to identify the anatomical structure of interest. The system supports threshold segmentation and region growing segmentation.

[0039] Step 2: Perform morphological operations and smoothing on the segmentation results to remove noise and small isolated regions.

[0040] Step 3: Use the Marching Cubes algorithm to extract isosurfaces from the segmentation results to generate a three-dimensional surface mesh.

[0041] Step 4: Smooth the extracted surface to reduce jagged edges and generate normal information.

[0042] Step 5: Use VTK to render and display the generated three-dimensional model.

[0043] The Marching Cubes algorithm is a classic algorithm for three-dimensional surface reconstruction. The algorithm traverses the voxels in a three-dimensional data set (usually medical image data) to extract isosurfaces and generate a three-dimensional model. The core idea of the algorithm is to compare the gray value of each voxel in a three-dimensional grid (cube) with a pre-set threshold value to determine whether there is an isosurface inside the voxel. According to different gray combinations, the algorithm uses a pre-defined lookup table to determine the surface geometry of the cube. First, convert the medical image data (such as CT or MRI) into three-dimensional voxel data, and each voxel has a corresponding gray value.

[0044] A threshold (i.e. isosurface) is then determined to determine whether the value of a voxel is greater than or less than the threshold. The threshold can be based on some feature extraction, such as computed by the Otsu method or other means; the three-dimensional data is then divided into multiple cubes (each cube consisting of 8 voxels), and each cube is traversed one by one. For each cube, the gray scale values of its 8 corners are checked, and based on the relationship between these gray scale values and the threshold, each corner is determined to be "in" (above the threshold) or "out" (below the threshold), forming a binary pattern. The binary pattern is used as an index to look up a predefined lookup table. The lookup table contains three-dimensional triangular configurations (i.e. how to connect points in a cube to form a surface) corresponding to different combinations of corners. Based on the triangular connection information provided by the lookup table, triangles are generated within the voxel. These triangles form part of the isosurface. The generated triangles are smoothed by interpolation or other methods, and adjacent triangles are merged in a global range to reduce the number of surfaces of the model and optimize the calculation efficiency. Finally, the generated three-dimensional model is output, which can be further visualized, analyzed or used for other processing.

[0045] Referring to Figure 3 Another embodiment provided by the present application is that the specific steps of the finite element analysis strategy are:

[0046] Step 1: meshing the three-dimensional model generated after three-dimensional reconstruction.

[0047] Step 2: selecting the corresponding analysis type (thermal analysis or electrophysiological simulation analysis), selecting the corresponding parameters (temperature range, thermal conductivity, electrical conductivity or stimulation intensity) input, and clicking Start Analysis to wait for the system to operate.

[0048] Step 3: after the analysis is completed, click Show Analysis Results to display the analysis results on the three-dimensional model display interface.

[0049] Mesh smoothing and division:

[0050] Thermal analysis: thermal blood analysis is based on the heat conduction equation to simulate the distribution of temperature in the tissue. The formula of the heat conduction equation is: Where ρ is the density, c is the specific heat capacity, T is the temperature, k is the thermal conductivity, and Q is the heat source term

[0051] The heat conduction equation is discretized into a linear equation set by setting temperature or heat flow conditions on the model boundary:

[0052] [K] {T} = {F}

[0053] where [K] is the thermal conductivity matrix, {T} is the node temperature vector, and {F} is the thermal load vector. The system of equations is solved using numerical methods to obtain the temperature at each node. The thermal analysis uses a simplified model that calculates the temperature distribution based on spatial location.

[0054] Electrophysiology simulation analysis: The electrophysiology simulation analysis is based on the electric field equation, which models the distribution of electric field in biological tissue, where the electric field equation is:

[0055]

[0056] where σ is the electrical conductivity and V is the electric potential.

[0057] The electrode positions and electric potential or current conditions are set. The electric field equation is discretized into a system of linear equations:

[0058] [K]{V}={I}

[0059] where [K] is the electrical conductivity matrix, {V} is the node electric potential vector, and {I} is the current load vector. The system of equations is solved using numerical methods to obtain the electric potential value at each node.

[0060] The electric field strength is calculated from the electric potential gradient, with the formula:

[0061]

[0062] The electric field analysis uses a simplified model that calculates the electric field strength based on the distance to the center point.

[0063] See Figure 4 The specific steps for displaying and interacting with the multi-view interface according to another embodiment of the present application are as follows:

[0064] Step 1: The system first loads the medical image data in DICOM or NIfTI format and converts it into three-dimensional volume data.

[0065] Step 2: In the UI initialization phase, the system creates display areas for three orthogonal views (axial, sagittal, and coronal), with each view corresponding to a Matplotlib Figure and Canvas object.

[0066] Step 3: According to the user's selected slice position, the corresponding two-dimensional slices are extracted from the three-dimensional volume data: Axial (Axial): Extract the xy plane at a fixed position on the z axis; Sagittal (Sagittal): Extract the yz plane at a fixed position on the x axis; Coronal (Coronal): Extract the xz plane at a fixed position on the y axis.

[0067] Step 4: Apply window width and window level adjustments to the extracted slices to optimize the display effect.

[0068] Step 5 displays the processed slice on the corresponding Matplotlib canvas.

[0069] Step 6 controls the slice position by slider to achieve navigation in three-dimensional data.

[0070] Those skilled in the art can understand that the above description is only a preferred example of the application and is not used to limit the application, although the application is described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions recorded in the foregoing examples or make equivalent replacement for part of the technical features, and any modification, equivalent replacement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A medical image processing and three-dimensional reconstruction simulation method, characterized in that: The method comprises the following steps: Step 1: Obtain medical imaging data; Step 2: preprocessing the medical imaging data; Step 3: Segmentation is performed based on the characteristics of the image data; Step 4, create a 3D model; Step 5: Establish thermal analysis and electrophysiological simulation models; Step 6: Calculate and output the simulation results.

2. The medical image processing and 3D reconstruction simulation method according to claim 1, characterized in that: In step 1, image data is collected by an imaging device, supporting DICOM and NIfTI formats, and the data is parsed and stored.

3. The medical image processing and 3D reconstruction simulation method according to claim 1, characterized in that: The specific method of step 2 is as follows: Step 2.1, adjust the window width and window level of the image data to optimize the contrast of the image; Step 2.2, denoising the medical image data using Gaussian filtering algorithm; In step 2.3, the pre-processed image data is standardized to meet the needs of subsequent processing.

4. The medical image processing and 3D reconstruction simulation method according to claim 1, characterized in that: The specific implementation steps of step 3 are as follows: Step 3.1, extract the target area based on the threshold segmentation method; Step 3.2, apply the region growing algorithm to further optimize the segmentation results; Step 3.3, construct a three-dimensional structural model of the target area and extract key feature points; In step 3.4, a coordinate system of the image data is defined, in which the sagittal plane, coronal plane, and transverse plane are perpendicular to each other, forming a three-dimensional positioning basis.

5. The medical image processing and 3D reconstruction simulation method according to claim 1, characterized in that: The specific implementation steps of step 4 are as follows: Step 4.1, create a three-dimensional model of the target area based on the Marching Cubes algorithm; Step 4.2, optimize the mesh of the 3D model to reduce redundant triangles; Step 4.3: Generate a structured 3D model based on the model feature points, supporting rotation, scaling, and slicing operations.

6. The medical image processing and 3D reconstruction simulation method according to claim 5, characterized in that: The specific implementation steps of step 5 are as follows: Step 5.1, introduce the thermal analysis model and input parameters such as temperature range and thermal conductivity; Step 5.2: Establish an electrophysiological simulation model and input parameters such as conductivity and stimulation intensity; Step 5.3, combining the 3D model and simulation parameters to calculate the heat distribution and current distribution in the target area; Step 5.4: Visualize the simulation results to generate thermal distribution maps and electrophysiological response maps.

7. The medical image processing and 3D reconstruction simulation method according to claim 6, characterized in that: The specific implementation steps of step 6 are as follows: Step 6.1, superimpose the processed 3D model with the simulation results; Step 6.2, displaying the axial, sagittal, and coronal slices of the image using a multi-view display module; Step 6.3, calculate the comprehensive indicators of thermal and electrophysiological simulation; Step 6.4: Generate an interactive 3D model to observe the match between the simulation results and the real structure; Step 6.5: Output simulation data and 3D models for medical diagnosis and surgical planning.

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