CN114140504A - A three-dimensional interactive biomedical image registration method - Google Patents

A three-dimensional interactive biomedical image registration method Download PDF

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CN114140504A
CN114140504A CN202111479226.0A CN202111479226A CN114140504A CN 114140504 A CN114140504 A CN 114140504A CN 202111479226 A CN202111479226 A CN 202111479226A CN 114140504 A CN114140504 A CN 114140504A
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屈磊
罗文婷
吴军
王慧敏
李园园
韩婷婷
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Anhui University
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Abstract

The invention relates to a three-dimensional interactive biomedical image registration method, which overcomes the defect that the accuracy of a biomedical image registration algorithm is difficult to improve compared with the prior art. The invention comprises the following steps: acquiring a three-dimensional biomedical image to be registered and a template label image; generating a three-dimensional model of the template label image; visualizing the three-dimensional model and the image to be registered; interactively adjusting a template label image three-dimensional model according to an image to be registered; obtaining corresponding matching points; and obtaining a registration result. According to the invention, the three-dimensional biomedical image is three-dimensionally displayed, and the real-time extraction information of the characteristic points of the mouse feedback image is obtained, so that the existence of inaccurate characteristic points is avoided, and the precision of image registration is improved; the operation difficulty of interactive registration is greatly reduced, and the operation efficiency of man-machine interaction is improved.

Description

Three-dimensional interactive biomedical image registration method
Technical Field
The invention relates to the technical field of three-dimensional biomedical image processing, in particular to a three-dimensional interactive biomedical image registration method.
Background
The three-dimensional biomedical images have abundant information content, are convenient to observe and very intuitive, and occupy more and more important positions for analyzing the three-dimensional biomedical images, wherein the integration analysis of animal brain structural images and brain functional images becomes an important research subject along with the development of brain plans, and the registration is an important step before the brain image analysis.
Image registration is a process of matching and superimposing two images acquired under different conditions, and is widely applied to the fields of remote sensing data analysis, computer vision, image processing and the like. Visualization technology is a technology for displaying data on a computer screen in an image graph mode, and is one of the hot spots of computer graphics and image processing research at present. With the research of the registration technology by scientific researchers, a plurality of new technologies and new methods are also emerged in the image registration technology. The image registration techniques are various, because the various image registration techniques are not widely applicable to all fields, and different application environments need to integrate various factors to select the corresponding image registration techniques.
The existing image registration method mainly comprises a traditional method and a deep learning method. Conventional methods are generally classified into a grayscale-based method and a feature-based method according to image information used in image registration. Compared with the traditional medical image registration method, the greatest contribution of deep learning in research results of medical image registration is to improve the problem of low processing speed. Although registration based on deep learning is much faster than conventional algorithms, conventional algorithms are still much higher in accuracy than depth algorithms at present.
Meanwhile, most of the traditional image registration researches are focused on the registration method based on the image characteristics, and the registration method based on the image characteristics is more suitable for the registration between the images with complex space transformation compared with the method based on the image gray scale. Among many image features, the image interest point features are widely researched and applied due to the advantages that the positioning is accurate, and the matched interest point coordinates can be directly used for calculating the spatial transformation relation between the images.
Although the feature-based image registration method has been the mainstream of research to achieve better effect on image registration, it has a general problem: because the imaged three-dimensional biomedical image may have some conditions of cavities and artifacts, it is difficult to ensure all local optima by the acquired feature points. If the registration accuracy requirement is particularly high, it is difficult to improve the accuracy of the image registration in this case.
Therefore, how to further improve the local registration accuracy under the condition of high requirement on the registration accuracy becomes an urgent technical problem to be solved.
Disclosure of Invention
The invention aims to solve the defect that the accuracy of a biomedical image registration algorithm in the prior art is difficult to improve, and provides a three-dimensional interactive biomedical image registration method to solve the problem.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a three-dimensional interactive biomedical image registration method, comprising the steps of:
11) acquiring a three-dimensional biomedical image to be registered and a template label image;
12) generating a three-dimensional model of the template label image: extracting contour points of the template label image in different regions according to different label values of the template label image, then carrying out voxel grid filtering, and then carrying out greedy projection triangulation processing to finally obtain a three-dimensional model of the template label image;
13) visualizing the three-dimensional model and the image to be registered: the three-dimensional visualization of the template label image three-dimensional model and the image to be registered is realized by using OpenGL;
14) and (3) interactively adjusting the template label image three-dimensional model according to the image to be registered: continuously three-dimensionally and interactively adjusting the three-dimensional model of the template label image according to the displayed slice of the image to be registered, so that the three-dimensional model of the template label image is preliminarily aligned with the image to be registered;
15) obtaining corresponding matching points: sampling a group of corresponding matching points of the three-dimensional model before and after adjustment to form a registration point set;
16) and obtaining a registration result: and solving a deformation field by using the registration point set, and then obtaining a biomedical image registration result image by using deformation field interpolation.
The method for generating the three-dimensional model of the template label image comprises the following steps:
21) setting different pixels in the template label image as different labels, traversing each pixel of the template label image, and recording each different pixel value ai(i<＝n,i∈N+) Obtaining the total number n of label values of the template label image, and recording all the label values A, namely A ═ a1,a2,...,an}；
22) For a three-dimensional template label image, the template label image is divided into a series of two-dimensional image slices along the X, Y, Z axis, and the X, Y, Z axis maximum values of the image are recorded as Xmax、Ymax、Zmax；
23) For each two-dimensional image slice of the previous step, according to the label value aiDifferent divisions into different zones mi(i<＝n,i∈N+) For each area m on the pictureiCarrying out contour extraction to obtain contour points of each region, and storing coordinate values of each point to obtain a dense point set C, C ═ Cm1, Cm2<N, i belongs to N +) is the point set of the ith region;
24) c downsampling the point set by voxel grid filtering, i.e. setting the length, height, width and size as Xmax/Q、Ymax/Q、ZmaxA 3D voxel grid of/Q, where every voxel, i.e. all points in the 3D box, are approximated by their centroid to a point to filter out a set of points that are too close together, where Q ═ X (X)max+Ymax+Zmax)*6.5f；
25) Performing surface rendering on the point set subjected to downsampling by greedy projection triangulation to obtain a three-dimensional model of each area of the template label image
Figure BDA0003394371840000031
Wherein, the number of the point sets corresponding to each three-dimensional model
Figure BDA0003394371840000032
Figure BDA0003394371840000033
Is a three-dimensional model of the ith region,
Figure BDA0003394371840000034
is the set of points of the three-dimensional model of the ith region.
The visual three-dimensional model and the image to be registered comprise the following steps:
31) describing the three-dimensional model of the template label image through a vertex sequence, displaying the three-dimensional models of the n regions in different colors at random, starting perspective and color mixing, and finally displaying the three-dimensional model of the template label image;
32) and respectively extracting X, Y, Z two-dimensional slices at the specified positions of the axes from the three-dimensional biomedical image to be registered to obtain a 2D texture image, mapping the texture to a quadrangle at the corresponding position, and specifying the part of each vertex corresponding to the texture to realize the cross display of the axis section of the image to obtain the three-dimensional visualization of the image to be registered.
The interactive adjustment of the surface of the three-dimensional model according to the image to be registered comprises the following steps:
41) acquiring a two-dimensional coordinate of a QT interface currently clicked by a left mouse button, and converting the two-dimensional coordinate into a two-dimensional coordinate of an OpenGL window; then, assuming that the Z coordinate of 3D is 0, acquiring a viewport, a model and a projection matrix, performing matrix conversion, and converting the coordinate of a window into a world coordinate;
42) obtaining the actual position in the space according to the OpenGL window size, the rotation matrix, the scaling multiple and the left-right up-down translation distance;
43) assuming two different third-dimensional coordinate values, obtaining two three-dimensional coordinate points, establishing a spatial straight line, and obtaining the intersection position of the straight line and the current selected axis slice, wherein the position is the three-dimensional coordinate of the selected position;
44) after the three-dimensional coordinates of the selected position are obtained, calculating the range of the selected position along the surface of the contour of the selected area, wherein the range is determined by parameters of height and width, the height refers to the range selected on the surface of the contour of the area on the section, and the width refers to the range of the number of the front and rear selected sections; the method comprises the following specific steps:
calculating a point set of the three-dimensional model with the distance between the current section and the selected position within height, continuously searching the point set within a small range from the selected position to obtain a contour line point set of the three-dimensional model on the slice, searching the point sets with the distance between the front and the back of the slice within width through the contour line, and taking all the searched point sets as a selected area;
45) according to the distance selected and moved by the left mouse button, calculating the moving distance of each point of the currently selected area, ensuring that the selected area moves to the corresponding position and is smooth, wherein the calculation form of the moving distance G (x, y, z) is as follows:
Figure BDA0003394371840000041
wherein M is equal to the moving distance of the mouse, dx, dy, dz are the difference between the x, y, z value of each point from the mouse click position, sigma is the set sigma value,
Figure BDA0003394371840000042
if the value range of (1) is between (0) and (0), carrying out gradual change color visualization on the value of each point in the currently selected area, and ensuring that the edge is below 0.25 by moving each time;
46) and (4) slicing the sliding shaft, and repeating the steps from 41) to 43) according to the displayed slice of the image to be registered, and continuously changing the outline of the three-dimensional model until the adjusted outline of the three-dimensional model is initially aligned with the image to be registered, so as to obtain an initial curved surface adjustment result.
The obtaining of the corresponding matching points comprises the following steps:
51) specifying each area mi(i<＝n,i∈N+) Number of point sets
Figure BDA0003394371840000043
Default setting
Figure BDA0003394371840000044
52) Region-wise downsampling to a specified number for pre-and post-adjusted three-dimensional models
Figure BDA0003394371840000045
Figure BDA0003394371840000046
This set of corresponding matched point sets is the registration point set.
Advantageous effects
Compared with the prior art, the three-dimensional interactive biomedical image registration method has the advantages that the three-dimensional biomedical image is displayed in a three-dimensional mode, the real-time extraction information of the characteristic points of the image fed back by the mouse is obtained, the existence of inaccurate characteristic points is avoided, and the image registration precision is improved; the operation difficulty of interactive registration is greatly reduced, and the operation efficiency of man-machine interaction is improved.
The invention also comprehensively considers the conditions of artifacts, cavities and overlarge local difference in the three-dimensional biomedical images, and simply and efficiently improves the registration precision of the local details of the images.
Drawings
FIG. 1 is a sequence diagram of the method of the present invention;
FIG. 2 is a schematic process diagram of the method of the present invention;
FIG. 3a is a section of a selected three-dimensional rat brain MRI template image;
FIG. 3b is a section corresponding to the selected three-dimensional rat brain MRI image to be registered;
FIG. 3c is a graph of the result of registering FIG. 3a to FIG. 3b using the conventional image registration tool NiftyReg;
FIG. 3d is a graph of the result of registering FIG. 3a to FIG. 3b using the conventional image registration tool Elastix;
FIG. 3e is a graph of the result of registering FIG. 3a to FIG. 3b by the method of the present invention;
Detailed Description
So that the manner in which the above recited features of the present invention can be understood and readily understood, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings, wherein:
because the imaged biomedical image may have some continuous cavities, too large deformation and artifacts, the full-automatic registration algorithm often cannot achieve the optimal registration effect, and therefore, it is necessary to improve the registration accuracy of the image in a human-computer interaction manner. In some interactive registration methods, point set alignment on a slice is realized in only one direction, but cross-layer alignment cannot be realized, and region cross-layer alignment can be realized by freely adjusting the point set contour in three dimensions, so that better matched feature points are obtained, and the registration accuracy is finally improved. As shown in fig. 1 and fig. 2, a three-dimensional interactive biomedical image registration method according to the present invention includes the following steps:
the method comprises the steps of firstly, obtaining a three-dimensional biomedical image to be registered and a template label image.
Secondly, generating a three-dimensional model of the template label image: according to different label values of the template label image, extracting contour points of the template label image in different regions, then carrying out voxel grid filtering, and then carrying out greedy projection triangulation processing to finally obtain a three-dimensional model of the template label image.
Considering that the characteristics of the template label image are composed of a plurality of label values, and each label value delimits a certain area, the three-dimensional model closer to the original image can be fitted by distinguishing the label values to extract contour points of each area. Considering that the final three-dimensional model has incomplete parts of holes due to the fact that the contour point sets are extracted on only one fixed axis slice, the contour point sets are extracted once along the X, Y, Z axis, and therefore, although the redundant point sets exist, the whole contour is complete. Given that the excessive and redundant number of point sets can make subsequent three-dimensional model rendering and adjustment difficult, the point sets for each region are downsampled using a voxel grid filter without destroying the geometry of the point sets themselves. Considering that the density change of the point set after down sampling is uniform and smooth, rapid triangulation is carried out on the point set by greedy projection triangulation, and a three-dimensional model similar to the original module label image is rapidly obtained.
The specific steps of generating the three-dimensional model of the template label image are as follows:
(1) setting different pixels in the template label image as different labels, traversing each pixel of the template label image, and recording each different pixel value ai(i<＝n,i∈N+) Obtaining the total number n of label values of the template label image, and recording all the label values A, namely A ═ a1,a2,...,an}；
(2) For a three-dimensional template label image, the template label image is divided into a series of two-dimensional image slices along the X, Y, Z axis, and the X, Y, Z axis maximum values of the image are recorded as Xmax、Ymax、Zmax；
(3) For each two-dimensional image slice of the previous step, according to the label value aiDifferent divisions into different zones mi(i<＝n,i∈N+) For each area m on the pictureiExtracting the contour to obtain contour points of each region, storing the coordinate values of each point to obtain a dense point set C of each region,
Figure BDA0003394371840000061
wherein
Figure BDA0003394371840000062
Is the point set of the ith region;
(4) downsampling the set of points C with voxel grid filtering: because too many point sets bring difficulty to the subsequent drawing and adjustment of the three-dimensional model, the point sets of each region are down sampled by using a voxel grid filter, and the geometrical structure of the point sets is not damagedmax/Q、Ymax/Q、ZmaxA 3D voxel grid of/Q, where every voxel, i.e. all points in the 3D frame, approximates the other points in the voxel with their centroid, where Q ═ (X ═ ismax+Ymax+Zmax)*6.5f；
(5) To face downwardsPerforming surface rendering on the sampled point set by greedy projection triangulation to obtain a three-dimensional model of each area of the template label image: because the density change of the point set after down sampling is uniform and smooth, rapid triangulation is carried out on the point set by greedy projection triangulation, and a three-dimensional model similar to the original module label image is rapidly obtained
Figure BDA0003394371840000071
Wherein, the number of the point sets corresponding to each three-dimensional model
Figure BDA0003394371840000072
Figure BDA0003394371840000073
Is a three-dimensional model of the ith region,
Figure BDA0003394371840000074
is the set of points of the three-dimensional model of the ith region.
Thirdly, visualizing the three-dimensional model and the image to be registered: and realizing three-dimensional visualization of the three-dimensional model and the image to be registered by using OpenGL. The method comprises the following specific steps:
(1) describing the three-dimensional model of the template label image through a vertex sequence, setting random and different colors for the three-dimensional models of the n regions for display, starting perspective and color mixing, and finally displaying the three-dimensional model of the template label image. In order to facilitate the adjustment of subsequent operations, the three-dimensional model can define the transparency degree in a user-defined manner so as to better observe the change of the contours of the front and rear slices during the adjustment of the current slice; meanwhile, only the three-dimensional model of the current region needing to be adjusted can be displayed, and only the region which is interested or has overlarge difference can be adjusted; the edge is below 0.25 (blue part exists in the color), so that the convenience area moves smoothly.
(2) The method comprises the steps of respectively extracting X, Y, Z two-dimensional slices at the specified positions of an axis from a three-dimensional biomedical image to be registered to obtain a 2D texture image, mapping textures to quadrangles at corresponding positions, and specifying parts of each vertex corresponding to the textures to realize cross display of the axis section of the image, wherein the axis section can be switched through a mouse pulley or a scroll bar.
Step four, interactively adjusting the surface of the three-dimensional model according to the image to be registered: and adjusting the three-dimensional model through three-dimensional interaction to obtain an image to be registered, wherein each contour surface of the three-dimensional model is close to the image to be registered.
In order to adjust the three-dimensional model generated in the previous step, the premise is that the coordinates of the selected position are accurately picked up in real time, a space straight line is obtained through the coordinate system conversion of OpenGL, and then the intersection point of the plane and the space straight line is rapidly obtained by combining the currently selected axis slice. After the intersection point is obtained, considering the situation that the image to be registered and the template image have large appearance difference, it is necessary to smoothly adjust the three-dimensional model, and here, the moving intensity is constrained by setting parameters and the constraint is embodied by visualizing the gradual change color. The invention can freely adjust the point set outline in three dimensions, and the moving process is real-time and efficient.
The specific steps of interactively adjusting the surface of the three-dimensional model according to the image to be registered are as follows:
(1) three-dimensional coordinates of a selected location on the cut sheet are picked. Firstly, the two-dimensional coordinate of the QT interface currently clicked by the left mouse button is acquired and converted into the two-dimensional coordinate of the OpenGL window (because the QT screen coordinate system is opposite to the OpenGL in the vertical direction). Then, assuming that the Z coordinate of 3D is 0, the viewport, the model, and the projection matrix are acquired, and matrix conversion is performed by the gluunoproject function to convert the coordinates of the window into world coordinates. And then, obtaining the actual position in the space according to the size of the OpenGL window, the rotation matrix, the scaling multiple, the left-right up-down translation distance and the like. And finally, assuming two different third-dimensional coordinate values to obtain two three-dimensional coordinate points, establishing a spatial straight line, and obtaining the position where the straight line is intersected with the current selected axis slice, wherein the position is the three-dimensional coordinate of the selected position.
(2) After the three-dimensional coordinates of the selected position are obtained, a certain range of the selected position is calculated along the contour surface of the selected area and is determined by parameters of height and width, wherein height refers to the range selected on the contour surface of the area on the tangent plane, and width refers to the range of the number of the front and rear selected tangent planes. Specifically, a point set of the three-dimensional model with the distance between the current tangent plane and the selected position within height is calculated, then the point set within a small range is continuously searched from the selected position to obtain a contour line point set of the three-dimensional model on the slice, the point sets with the distance within width before and after the slice are searched through the contour line, and all the searched point sets are selected areas.
(3) And calculating the moving distance of each point of the currently selected area according to the distance selected and moved by the left mouse button, and ensuring that the selected area is moved to the corresponding position and is still smooth. The moving distance is calculated in the form:
Figure BDA0003394371840000081
wherein, M is equal to the moving distance of the current mouse, dx, dy and do are the difference of x, y and z values of each point from the selected position of the mouse respectively, and sigma is a set sigma value. In addition, the first and second substrates are,
Figure BDA0003394371840000082
if the value range of (1) is between (0), then the value range of (1) will be used for each point of the currently selected area
Figure BDA0003394371840000083
The value of (2) is visualized in a gradual change color, each movement ensures that the edge is below 0.25 (the blue part exists on the color), and the area is convenient to move smoothly;
(4) and (3) slicing the sliding shaft, repeating the steps (1) to (3) according to the displayed slice of the image to be registered, and continuously changing the profile of the three-dimensional model until the adjusted profile of the three-dimensional model is initially aligned with the image to be registered, so as to obtain an initial curved surface adjustment result.
And fifthly, obtaining corresponding matching points: and sampling a group of corresponding matching points of the three-dimensional model before and after adjustment to form a registration point set. The method comprises the following specific steps:
specifying each area mi(i<＝n,i∈N+) Number of point sets required
Figure BDA0003394371840000091
(1) Default setting
Figure BDA0003394371840000092
Figure BDA0003394371840000093
(2) Region-wise downsampling to a specified number for pre-and post-adjusted three-dimensional models
Figure BDA0003394371840000094
Figure BDA0003394371840000095
This set of corresponding matched point sets is the point set that is ultimately used for registration.
Sixthly, obtaining a registration result: and solving a deformation field by using a matched group of points by using a traditional method, and then obtaining a registration result image by using traditional deformation field interpolation.
As shown in fig. 3a and 3b, both of them are rat brain MRI images, the former being used as template images and the latter being used as images to be registered, with the goal of registering fig. 3b to fig. 3 a. Fig. 3c shows the registration result obtained by using the conventional image registration tool Niftyreg, fig. 3d shows the registration result obtained by using the conventional image registration tool Elastix, and fig. 3e shows the registration result obtained by using the method of the present invention, and by comparison, the registration effect of the method proposed by the present invention is better, that is, the registration effect is more similar to the structure and brightness distribution of the template image of fig. 3a at the position marked by the box in the figure.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1.一种三维交互的生物医学图像配准方法，其特征在于，包括以下步骤：1. a three-dimensional interactive biomedical image registration method, is characterized in that, comprises the following steps: 11)获取待配准的三维生物医学图像和模板标签图像；11) Acquire the 3D biomedical image to be registered and the template label image; 12)生成模板标签图像的三维模型：根据模板标签图像的不同标签值，分区域提取模板标签图像的轮廓点，接着进行体素格滤波，然后进行贪婪投影三角化处理，最后得到模板标签图像三维模型；12) Generate a three-dimensional model of the template label image: According to the different label values of the template label image, the contour points of the template label image are extracted in sub-regions, then the voxel grid filtering is performed, and then the greedy projection triangulation is performed, and finally the three-dimensional template label image is obtained. Model; 13)可视化三维模型和待配准图像：运用OpenGL去实现模板标签图像三维模型和待配准图像的三维可视化；13) Visualize the 3D model and the image to be registered: use OpenGL to realize the 3D visualization of the 3D model of the template label image and the image to be registered; 14)根据待配准图像交互调整模板标签图像三维模型：根据显示的待配准图像切片，不断三维交互地调整模板标签图像的三维模型，使得模板标签图像的三维模型与待配准图像初步对齐；14) Interactively adjust the three-dimensional model of the template label image according to the image to be registered: According to the displayed image slices to be registered, continuously and interactively adjust the three-dimensional model of the template label image, so that the three-dimensional model of the template label image and the to-be-registered image are preliminarily aligned ; 15)对应匹配点的获得：对调整前和调整后的三维模型下采样一组对应的匹配点，形成配准点集；15) Obtaining corresponding matching points: down-sampling a set of corresponding matching points on the three-dimensional model before and after adjustment to form a set of registration points; 16)得到配准结果：利用配准点集求解出形变场，再使用形变场插值得出生物医学图像配准结果图像。16) Obtain the registration result: use the registration point set to solve the deformation field, and then use the deformation field interpolation to obtain the biomedical image registration result image. 2.根据权利要求1所述的一种三维交互的生物医学图像配准方法，其特征在于，所述生成模板标签图像的三维模型包括以下步骤：2. A kind of three-dimensional interactive biomedical image registration method according to claim 1, is characterized in that, described generating the three-dimensional model of template label image comprises the following steps: 21)设模板标签图像中不同像素为不同的标签，遍历模板标签图像各个像素，记录每个不同的像素值ai(i<＝n,i∈N+)，得到模板标签图像的标签值总数量n，并记录所有标签值A，即A＝{a1,a2,...,an}；21) Set different pixels in the template label image as different labels, traverse each pixel of the template label image, record each different pixel value a i (i<=n, i∈N + ), and obtain the total label value of the template label image. number n, and record all label values A, ie A={a 1 ,a 2 ,...,a n }; 22)针对三维的模板标签图像，将模板标签图像分别沿X、Y、Z轴划分为一系列的二维图像切片，同时将该图像的X、Y、Z轴最大值分别记为Xmax、Ymax、Zmax；22) For the three-dimensional template label image, the template label image is divided into a series of two-dimensional image slices along the X, Y, Z axes, and the maximum values of the X, Y, and Z axes of the image are respectively recorded as X max , Y max , Z max ; 23)对于上一步的每张二维图像切片，按标签值ai不同划分为不同的区域mi(i<＝n,i∈N+)，对图片上每个区域mi进行轮廓提取，得到每个区域的轮廓点，并将每个点的坐标值存储起来，得到每个区域密集的点集C，C＝{Cm1,Cm2,...,Cmn}，其中Cmi(i<＝n,i∈N+)是第i个区域的点集；23) For each two-dimensional image slice in the previous step, it is divided into different regions m i (i<=n, i∈N + ) according to the different label values a i , and contour extraction is performed on each region m i on the image to obtain each The contour points of each area are stored, and the coordinate value of each point is stored to obtain the dense point set C of each area, C={Cm1, Cm2,..., Cmn}, where Cmi(i<=n,i ∈N+) is the point set of the ith region; 24)用体素格滤波对点集C下采样，即设定长高宽大小分别为Xmax/Q、Ymax/Q、Zmax/Q的3D体素网格，在每个体素即3D框中的所有点都用它们的质心近似成一个点，以过滤掉距离过近的点集，其中，Q＝(Xmax+Ymax+Zmax)*6.5f；24) Downsample the point set C with voxel grid filtering, that is, set a 3D voxel grid whose length, height and width are X max /Q, Y max /Q, and Z max /Q respectively. All points in the box are approximated as one point with their centroids to filter out point sets that are too close, where Q=(X max +Y max +Z max )*6.5f; 25)对下采样后的点集采用贪婪投影三角化进行面绘制，得到模板标签图像的每个区域的三维模型
Figure FDA0003394371830000021
其中，每个三维模型对应的点集数量
Figure FDA0003394371830000022
是第i个区域的三维模型，
Figure FDA0003394371830000023
是第i个区域的三维模型的点集。25) Use greedy projection triangulation to draw the surface of the downsampled point set to obtain a three-dimensional model of each area of the template label image
Figure FDA0003394371830000021
Among them, the number of point sets corresponding to each 3D model
Figure FDA0003394371830000022
is the 3D model of the ith region,
Figure FDA0003394371830000023
is the point set of the 3D model of the ith region. 3.根据权利要求1所述的一种三维交互的生物医学图像配准方法，其特征在于，所述的可视化三维模型和待配准图像包括以下步骤：3. A three-dimensional interactive biomedical image registration method according to claim 1, wherein the visualized three-dimensional model and the image to be registered comprise the following steps: 31)将模板标签图像的三维模型通过顶点序列进行描述，对n个区域的三维模型设置随机地、不同地颜色进行显示，并启动透视、颜色混合，最终显示出模板标签图像的三维模型；31) The three-dimensional model of the template label image is described by the vertex sequence, the three-dimensional model of the n regions is set to be displayed in random and different colors, and perspective and color mixing are started, and finally the three-dimensional model of the template label image is displayed; 32)把待配准三维生物医学图像分别提取X、Y、Z轴指定位置的二维切片，得到2D纹理图像，再将纹理映射到对应位置的四边形上，指定每个顶点各自对应纹理的部分，实现图像的轴切面的交叉显示，得到待配准图像的三维可视化。32) Extract the two-dimensional slices of the X, Y, and Z axis specified positions of the three-dimensional biomedical image to be registered, respectively, to obtain a 2D texture image, and then map the texture to the quadrilateral of the corresponding position, and specify the part of the corresponding texture of each vertex. , realize the cross display of the axial section of the image, and obtain the three-dimensional visualization of the image to be registered. 4.根据权利要求1所述的一种三维交互的生物医学图像配准方法，其特征在于，所述根据待配准图像交互调整三维模型表面包括以下步骤：4. A three-dimensional interactive biomedical image registration method according to claim 1, wherein the interactive adjustment of the surface of the three-dimensional model according to the to-be-registered image comprises the following steps: 41)获取鼠标左键当前点击的QT界面的二维坐标，将其转换为OpenGL窗口的二维坐标；然后，假设3D的Z坐标为0，获取视口、模型、投影矩阵，进行矩阵转换，将窗口的坐标转换为世界坐标；41) Obtain the two-dimensional coordinates of the QT interface currently clicked by the left mouse button, and convert them into two-dimensional coordinates of the OpenGL window; then, assuming that the 3D Z coordinate is 0, obtain the viewport, model, and projection matrix, and perform matrix conversion, Convert the coordinates of the window to world coordinates; 42)根据OpenGL窗口大小、旋转矩阵、缩放倍数、左右上下平移距离操作得到实际在空间中的位置；42) Obtain the actual position in space according to the OpenGL window size, rotation matrix, zoom factor, left and right up and down translation distance operations; 43)假定两个不一样的第三维坐标值，得到两个三维坐标点，建立空间直线，求得该直线与当前选定轴切片相交的位置，这个位置就是选中位置的三维坐标；43) Assuming two different third-dimensional coordinate values, two three-dimensional coordinate points are obtained, a space straight line is established, and the position where the straight line intersects with the currently selected axis slice is obtained, and this position is the three-dimensional coordinate of the selected position; 44)得到选中位置的三维坐标后，沿选中区域轮廓表面计算选中位置的范围，范围由参数height和width决定，其中height指切面上区域轮廓表面选中的范围，width指前后选中切面数量范围；其具体步骤是：44) After obtaining the three-dimensional coordinates of the selected position, the range of the selected position is calculated along the contour surface of the selected area, and the range is determined by the parameters height and width, wherein height refers to the selected range of the contour surface of the area on the cut surface, and width refers to the range of the number of selected cut surfaces before and after; The specific steps are: 先计算当前切面上和选中位置距离在height内的三维模型的点集，然后从选中位置起不断搜索小范围内的点集，得到在切片上的三维模型的一个轮廓线点集，通过这个轮廓线向切片前后搜索距离在width内的点集，搜索到的所有点集则为选中区域；First calculate the point set of the 3D model on the current slice and the selected position within the height, and then continuously search for the point set in a small range from the selected position to obtain a contour point set of the 3D model on the slice, through this contour Search the point set within the width before and after the line slice, and all the searched point sets are the selected area; 45)根据鼠标左键选中并移动的距离，计算当前选中区域的每个点的移动距离，保证选中区域移动到对应的位置且光滑，移动距离G(x,y,z)计算形式如下：45) According to the distance selected and moved by the left mouse button, calculate the moving distance of each point in the currently selected area to ensure that the selected area moves to the corresponding position and is smooth. The calculation form of the moving distance G(x, y, z) is as follows:
Figure FDA0003394371830000031
Figure FDA0003394371830000031
 其中，M等于鼠标移动距离，dx、dy、dz分别是每个点距离鼠标点击位置的x、y、z值之差，σ为设定sigma值，
Figure FDA0003394371830000032
的取值范围在(0,1)之间，则对当前选中区域的每个点的值进行渐变颜色可视化，每次移动确保边缘在0.25以下；Among them, M is equal to the moving distance of the mouse, dx, dy, and dz are the difference between the x, y, and z values of each point from the mouse click position, and σ is the set sigma value,
Figure FDA0003394371830000032
The value range of is between (0,1), then the value of each point in the currently selected area is visualized with a gradient color, and each movement ensures that the edge is below 0.25; 46)滑动轴切片，根据显示的待配准图像切片，重复进行41)-43)步骤，不断更改三维模型轮廓，直到调整后的三维模型轮廓与待配准图像初步对齐，得到初期曲面调整结果。46) Sliding axis slices, according to the displayed image slices to be registered, repeat steps 41)-43), constantly changing the outline of the 3D model, until the adjusted outline of the 3D model is preliminarily aligned with the image to be registered, and the initial surface adjustment result is obtained . 5.根据权利要求1所述的一种三维交互的生物医学图像配准方法，其特征在于，所述对应匹配点的获得包括以下步骤：5. A three-dimensional interactive biomedical image registration method according to claim 1, wherein the obtaining of the corresponding matching points comprises the following steps: 51)指定每个区域mi(i<＝n,i∈N+)的点集数量
Figure FDA0003394371830000033
默认设定
Figure FDA0003394371830000034
Figure FDA0003394371830000035
51) Specify the number of point sets for each region m i (i<=n, i∈N + )
Figure FDA0003394371830000033
Default setting
Figure FDA0003394371830000034
Figure FDA0003394371830000035
 52)对调整前和调整后的三维模型按区域下采样到指定数量
Figure FDA0003394371830000036
Figure FDA0003394371830000037
这一组对应的匹配的点集为配准点集。52) Downsample the 3D model before and after adjustment to a specified number by region
Figure FDA0003394371830000036
Figure FDA0003394371830000037
This set of corresponding matched point sets is a registration point set.
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