CN115187640A - CT and MRI3D/3D image registration method based on point cloud - Google Patents
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Abstract
The invention discloses a point cloud-based CT and MRI3D/3D image registration method, which comprises the following steps: s1, extracting a pure contour of a face region: extracting the surface contour of an original tomogram before three-dimensional reconstruction to remove impurities; s2, three-dimensional reconstruction is carried out on the pure contour of the face region: performing three-dimensional reconstruction on the contour line extracted in the step S1 to obtain a pure surface contour; s3, defining a registration interval: the surface contour is put into a registration interval limiting platform which can be cut or modified for multiple times in six directions, so that the registration interval range of the CT and MRI surface contours tends to be consistent, and the method has the advantages that: and reconstructing surface contours of CT and MRI by using a surface drawing method, further dispersing dense dot matrixes on respective surface contours to serve as feature point clouds, and finally performing ICP registration by using the two groups of feature point clouds, namely solving the problem of registration of heterogeneous (CT and MRI) tomography image data sets by using software on the premise of low complexity algorithm and no deformation of volume data.
Description
Technical Field
The invention relates to the technical field of image registration, in particular to a point cloud-based CT and MRI3D/3D image registration method.
Background
The clinical significance and application value of the medical image registration technology are more and more prominent in clinical practice and are greatly developed in practice. The registration of medical images has been developed from the original pure 2D/2D image registration to 2D/3D registration and 3D/3D registration, and the trend of the medical imaging technology towards three-dimensional visualization makes the 3D/3D registration more important. However, the technical difficulties and requirements are also increasing. For example, 3D/3D registration has more types and complexity of geometric transformation due to the stereo data, which causes the spatial complexity and temporal complexity of the whole registration process to be much higher than that of 2D/2D registration, and also increases the occurrence probability of "locally optimal" error condition.
On the other hand, at present, there are two main methods for 3D/3D registration: a grayscale-based registration method and a feature-based registration method. The two registration methods are mostly used for registering the same focus and the same acquisition equipment at different time points, and the registration comparison is carried out on the two sets of CT of the same patient in different time periods, so that the disease change and the treatment effect are visually evaluated. I.e. a heterogeneous registration that is aimed at the homologous tomographic image dataset rather than the study interest. For the registration reconstruction of CT and MRI data of the same patient in the same time period, i.e. the registration of the heterogeneous tomographic image data set, due to differences of imaging devices, human body positioning, and imaging characteristics, it may be necessary to use means such as adding external markers and device modification as a registration basis in the scanning process, and the application thereof still has considerable limitations. In the aspect of heterogeneous registration, a Voxelman atlas is established by the German hamburger university, visibleman and Visible Woman visualized human body data are realized in China and America Han, and a certain foundation is laid for atlas registration.
In the current medical environment, due to factors such as cost and resource allocation efficiency, data acquisition of CT and MRI are mostly carried out respectively, and the existing complex heterogeneous registration fusion method is difficult to become a common means of clinical diagnosis and treatment. In practical clinical applications such as diagnosis and treatment of ophthalmic tumors, 3D/3D registration of CT and MRI still faces many problems, the existing image registration method cannot solve the registration problem of heterogeneous (CT and MRI) tomographic image data sets, excessive attention needs to be paid to positioning in the scanning process of a patient, and additional auxiliary equipment needs to be used for marking and positioning. Therefore, the existing registration method is not strong in reliability and high in registration precision.
Disclosure of Invention
The invention aims to provide a point cloud-based CT and MRI3D/3D image registration method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a point cloud-based CT and MRI3D/3D image registration method comprises the following steps:
s1, extracting a pure contour of a face region: extracting a surface profile of an original tomographic image before three-dimensional reconstruction to remove impurities;
s2, three-dimensional reconstruction is carried out on the pure contour of the face region: performing three-dimensional reconstruction on the contour line extracted in the step S1 to obtain a pure surface contour;
s3, defining a registration interval: putting the surface contour into a registration interval limiting platform which can be cut for multiple times in six directions or modified to ensure that the registration interval range of the CT and MRI surface contours is consistent;
s4, carrying out volume rendering three-dimensional reconstruction on the image set and carrying out coordinate transformation after registration: three-dimensional reconstruction of an MRI tomography data set is carried out by adopting volume rendering, volume is transformed, and CT and the transformed MRI are reconstructed under the same coordinate system in a surface rendering mode;
s5, image slicing: after the angle is selected, the volume data after CT and MRI registration transformation are sliced, preparation is made for subsequent image fusion, and the window width and the window level are respectively adjusted and then fused according to the requirements.
Preferably, the extracting of the clean profile in S1 includes the following steps:
a. assigning all the parts of human ears and behind ears in the original tomogram to zero, then filtering some prominent white points and black points by using a median filter, and then carrying out binarization, namely carrying out threshold segmentation;
b. and filtering noise points by using a median filter, then carrying out edge detection by using a Roberts gradient operator to obtain an image with various edges, and extracting an outermost contour line by using an iteration outer contour extraction method.
Preferably, the reberts operator is an operator for finding an edge by using local difference, the Roberts gradient operator uses the difference between two adjacent pixel values in the diagonal direction, and the operator is in the following form
Gx＝f(i,j)-f(i-1,j-1)
Gy＝f(i-1,j)-f(i,j-1)
The convolution template corresponding to the Roberts gradient operator is:
preferably, the method for iteratively extracting the outer contour includes the steps of putting all coordinates of points on an edge detected by a gradient operator in a two-dimensional array, finding a mark point which is located furthest to the right on the outer contour in the two-dimensional array, wherein the coordinates of the mark point are (x 0, y 0), then finding points in eight fields of the mark points in the two-dimensional array, replacing the mark point with the points in the eight fields, continuing to find the points in the eight fields of the points in the eight fields, iteratively finding the points in the eight fields in the array in sequence, recording the coordinates of the points in the eight fields until the vertical coordinate of the points in the eight fields is y0, judging whether the horizontal coordinate is x0 when the head faces to the left, namely, winding the head to the left, further creating a full-0 graph with the size equal to that of the original tomographic image, and assigning 255 to corresponding positions of the points in the eight fields under record to obtain the human face contour.
Preferably, the contour line in S1 is extracted on a visual studio development platform.
Preferably, the three-dimensional reconstruction is performed by performing three-dimensional reconstruction on CT and MRI after registration transformation in the same scene.
Preferably, the slice in S5 is a slice of the MRI volume data after CT and registration transformation in the same coordinate and the same direction, and the window adjustment work is performed on the slice to adjust the bone window by using the imaging advantage of the CT on the bone after the slice is cut.
Preferably, the slice of the MRI in S5 is taken at a default window level and the MRI is overlaid directly onto the CT with 50% transparency.
Compared with the prior art, the invention has the beneficial effects that:
1. the surface profiles of CT and MRI are reconstructed by using a surface drawing method, dense dot matrixes are further dispersed on the respective surface profiles to serve as characteristic point clouds, and finally, the two groups of characteristic point clouds are used for ICP registration, so that the application characteristics of practicability and convenience are highlighted, and the requirements of light weight application are met. The problem of registration of different source (CT and MRI) tomographic image data sets is solved by software on the premise of low complexity algorithm degree and no deformation of volume data, and excessive attention to positioning is not needed in the scanning process of a patient, and additional auxiliary equipment is not needed to be used for marking and positioning; the registration method has strong reliability and extremely high registration precision;
2. the registration method is realized by software, and the patient can be positioned to meet the general scanning requirements when performing CT and MRI scanning in the early stage without other entity devices, so that the cost is saved, and the method is more convenient and faster;
3. the registration method is rigid registration, the size is reconstructed by utilizing the pixelspaging and slice position parameters in the TAG label of a DICOM image, the size of the CT and MRI three-dimensional reconstruction of the same patient is the same, and no deformation is generated after coordinate mapping transformation;
4. 3D/3D registration, an improved ICP algorithm, the defect of extreme value trapping through one-time data source iteration, and the precision is reliable; the pure contour is adopted as the registered feature point cloud, which is equivalent to that all points of a face tend to be taken as feature points, and the registration precision quality is improved due to the improvement of the number of feature point sets;
5. the manual intervention of the method only limits the selection range of the characteristic points, and particularly, the positions of the characteristic points are not manually intervened, so that the registration result is slightly influenced.
Drawings
FIG. 1 is a flow chart of the two-dimensional contour extraction of a tomographic image according to the present invention;
FIG. 2 is a diagram illustrating the two-dimensional contour extraction effect of the tomographic image according to the present invention;
FIG. 3 is a diagram of the two-dimensional contour extraction result of CT and MRI respective tomograms and the superposition effect of the original image according to the present invention;
FIG. 4 shows the correspondence between two sets of volume data slices after registration, with MRI slices on the left and CT slices on the right, according to the present invention;
FIG. 5 shows the slice and fusion after MRI transformation according to the present invention, taking the 1 st, 124 th and 200 th frames of CT slice with CT as reference.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-5, the present invention provides a technical solution: a point cloud-based CT and MRI3D/3D image registration method comprises the following steps:
s1, extracting a pure contour of a face region: extracting a surface profile of an original tomographic image before three-dimensional reconstruction to remove impurities; when the contour is extracted, only the contour of the skin of the human face is taken, so that the features of the human face can be highlighted, namely the proportion of the features of the human face in the total data is improved, the contour extraction can be simplified, and the complex contour of the human ear is removed; the extraction speed is improved, and compared with the extraction of a complete contour, the extraction time is reduced by half theoretically;
s2, three-dimensional reconstruction is carried out on the pure contour of the face region: performing three-dimensional reconstruction on the contour line extracted in the step S1 to obtain a pure surface contour;
s3, defining a registration interval: putting the surface contour into a registration interval limiting platform which can be cut or modified for cutting for multiple times in six directions to enable the registration interval range of the CT and MRI surface contours to be consistent:
s4, carrying out volume rendering three-dimensional reconstruction on the image set and carrying out coordinate transformation after registration: three-dimensional reconstruction of an MRI tomography data set is carried out by adopting volume rendering, volume is transformed, and CT and the transformed MRI are reconstructed under the same coordinate system in a surface rendering mode;
s5, image slicing: after the angle is selected, slicing is carried out on the volume data subjected to CT and MRI registration transformation, preparation is made for subsequent image fusion, and the window width and the window level are adjusted and then fused according to needs.
Further, the extracting of the clean contour in S1 includes the following steps:
a. assigning all the parts of human ears and behind ears in the original tomogram as zero, filtering some prominent white points and black points by using a median filter, and then carrying out binaryzation, namely carrying out threshold segmentation; the median filter is a nonlinear smoothing filter which is based on the ordering statistical theory and can effectively inhibit noise, and the basic principle is that the value of one point in a digital image or a digital sequence is replaced by the median of each point value in a neighborhood of the point, and the pixel value of the point is determined by the surrounding pixel values, so that the isolated noise point is eliminated; wherein, the bubble sorting method is used for sorting; the median filter is used twice in the contour extraction, so that the prominent noise points can be well inhibited, the defect that the Roberts operator is sensitive to noise is effectively overcome, and the extraction precision and efficiency are obviously superior to the contour extraction without the median filter;
b. and filtering noise points by using a median filter, then carrying out edge detection by using a Roberts gradient operator to obtain an image with various edges, and extracting an outermost contour line by using an iteration outer contour extraction method.
Further, the reberts operator is an operator for finding an edge by using local difference, the Roberts gradient operator uses the difference between two adjacent pixel values in the diagonal direction, and the operator is in the following form
Gx＝f(i,j)-f(i-1,j-1)
Gy＝f(i-1,j)-f(i,j-1)
The convolution templates corresponding to the Roberts gradient operator are:
further, the method for iteratively extracting the outer contour includes the steps of putting all coordinates of points on the edge detected by the gradient operator in a two-dimensional array, finding a rightmost marking point on the outer contour in the two-dimensional array, wherein the coordinates of the marking point are (x 0, y 0), then finding points in eight fields of the marking points in the two-dimensional array, replacing the marking point with the points in the eight fields, continuing to find the points in the eight fields of the points in the eight fields, sequentially and iteratively finding the points in the eight fields in the array, recording the coordinates of the points in the eight fields until the vertical coordinate of the points in the eight fields is y0, judging whether the horizontal coordinate is x0 when the head faces left, namely, winding the head to the left, further building a full-0 graph with the same size as the original fault image, and assigning a face 255 at the corresponding position of the recorded points in the eight fields, so that the face contour of the person is obtained.
Furthermore, the contour line in the S1 is extracted on a visual studio development platform, so that the operation complexity is reduced conveniently.
Furthermore, the three-dimensional reconstruction is to perform three-dimensional reconstruction on the CT and the MRI after registration transformation in the same scene, so that the registration accuracy can be evaluated conveniently according to the fitting degree of the CT and the MRI.
Furthermore, the slice in S5 is a slice of the MRI volume data after CT and registration transformation in the same coordinate and the same direction, and the imaging advantage of CT on the bone is utilized after the slice is cut, and the window adjustment work is performed on the slice to adjust the bone window, so that the imaging advantage of CT on the bone is conveniently utilized.
Further, the slice of the MRI in S5 adopts a default window level, and the MRI is directly overlaid on the CT with 50% transparency, so as to facilitate registration and fusion.
The embodiment is as follows: s1, assigning all the parts of human ears and behind the ears in an original tomogram to zero, filtering some prominent white points and black points by using a median filter, carrying out binarization, namely carrying out threshold segmentation, filtering noise points by using the median filter again, carrying out edge detection by using a Roberts gradient operator to obtain an image with various edges, putting all coordinates of points on the edges detected by the gradient operator in a two-dimensional array, and finding the rightmost point (x 0, y 0) on the outline in the array; because the noise points are well removed through the processing of the median filter, no edge exists outside the outer contour, the edge which is the rightmost edge is necessarily the point on the outer contour, then the point in the eight fields of the middle points (x 0, y 0) in the array is found, the point (x 0, y 0) is replaced by the point in the eight fields, the points in the eight fields of the points in the eight fields are continuously found, the points in the eight fields in the array are sequentially found in an iterative manner, and the coordinates of the points in the eight fields are recorded until the vertical coordinate of the point in the eight fields is y0; judging whether the horizontal coordinate is x0 when the head faces to the left; further creating a full-0 image with the same size as the original tomogram until the half-cycle is completed, and assigning 255 values to corresponding positions of points in the eight recorded fields to obtain the human face contour; when the contour is extracted, only the contour of the skin of the human face is taken, so that the features of the human face can be highlighted, namely the proportion of the features of the human face in the total data is improved, the contour extraction can be simplified, and the complex contour of the human ear is removed; the extraction speed is improved, and the time is reduced by half compared with the time for extracting the complete contour theoretically;
s2, performing three-dimensional reconstruction on the contour line extracted in the S1 to obtain a pure surface contour;
s3, narrowing the registration interval by cutting or modifying the cut registration interval limiting platform for multiple times in six directions, enabling the facial features to be sufficiently prominent in the registered data, and enabling the registration interval range of the CT and MRI surface contour to be consistent as much as possible:
s4, the registration interval of the CT and the MRI is approximately consistent through the registration interval limitation of S3; obtaining a mapping relation Matrix between the coordinate systems of the CT and MRI three-dimensional images after registration, and then carrying out coordinate transformation on MRI volume data to ensure that the coordinate system is consistent with that of the CT so as to further carry out resampling work; performing three-dimensional reconstruction of the MRI tomography data set by adopting volume rendering, and transforming volume; reconstructing the CT and the transformed MRI under the same coordinate system in a surface drawing mode;
s5, after the angle is selected, slicing is carried out on the volume data after CT and MRI registration transformation, and after slices of the CT and MRI volume data after the registration transformation in the same coordinate and the same direction are obtained, the two groups of two-dimensional slices are fused; because the corresponding slices correspond to the same position of the human head, and a spatial corresponding relation exists in the rigid registration process, the registration of a two-dimensional layer is not needed, and the two-dimensional layer is directly fused.
In the description of the present invention, it is to be understood that the terms "coaxial", "bottom", "one end", "top", "middle", "other end", "upper", "one side", "top", "inner", "front", "center", "both ends", and the like are used in the orientations and positional relationships indicated in the drawings only for the convenience of description and simplicity of description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, are not to be construed as limiting the present invention.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "disposed," "connected," "secured," "screwed" and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A point cloud-based CT and MRI3D/3D image registration method is characterized in that: the method comprises the following steps:
s1, extracting a pure contour of a face region: extracting a surface profile of an original tomographic image before three-dimensional reconstruction to remove impurities;
s2, three-dimensional reconstruction is carried out on the pure contour of the face region: performing three-dimensional reconstruction on the contour line extracted in the step S1 to obtain a pure surface contour;
s3, defining a registration interval: putting the surface contour into a registration interval limiting platform which can be cut for multiple times in six directions or modified to ensure that the registration interval range of the CT and MRI surface contours is consistent;
s4, carrying out volume rendering three-dimensional reconstruction on the image set and carrying out coordinate transformation after registration: carrying out three-dimensional reconstruction on the MRI tomography data set by adopting volume rendering, transforming the volume, and reconstructing the CT and the transformed MRI in the same coordinate system in a surface rendering mode;
s5, image slicing: after the angle is selected, slicing is carried out on the volume data subjected to CT and MRI registration transformation, preparation is made for subsequent image fusion, and the window width and the window level are adjusted and then fused according to needs.
2. The point cloud based CT and MRI3D/3D image registration method of claim 1, wherein: the extraction of the clean contour in the S1 comprises the following steps:
a. assigning all the parts of human ears and behind ears in the original tomogram as zero, filtering some prominent white points and black points by using a median filter, and then carrying out binaryzation, namely carrying out threshold segmentation;
b. and filtering noise points by using a median filter, then carrying out edge detection by using a Roberts gradient operator to obtain an image with various edges, and extracting an outermost contour line by using an iteration outer contour extraction method.
3. The point cloud-based CT and MRI3D/3D image registration method of claim 2, wherein: the Reboerts operator is an operator for finding an edge by using local difference, the Roberts gradient operator adopts the difference between two adjacent pixel values in the diagonal direction, and the operator is in the following form:
Gx＝f(i,j)-f(i-1,j-1)
Gy＝f(i-1,j)-f(i,j-1)
the convolution template corresponding to the Roberts gradient operator is:
4. the point cloud based CT and MRI3D/3D image registration method of claim 2, wherein: the method for iteratively extracting the outer contour comprises the steps of putting all coordinates of points on an edge detected by a gradient operator into a two-dimensional array, finding a mark point which is located rightmost on the outer contour in the two-dimensional array, wherein the coordinates of the mark point are (x 0, y 0), then finding points in eight fields of the mark points in the two-dimensional array, replacing the mark point with the points in the eight fields, continuing to find the points in the eight fields of the points in the eight fields, iteratively finding the points in the eight fields in the array in sequence, recording the coordinates of the points in the eight fields until the vertical coordinate of the points in the eight fields is y0, judging whether the horizontal coordinate is x0 when the head faces to the left, namely, building a full-0 graph with the same size as an original fault image after a half-cycle is completed, and assigning 255 to corresponding positions of the points in the eight fields which are recorded, so as to obtain the human face contour.
5. The point cloud based CT and MRI3D/3D image registration method of claim 1, wherein: and extracting the contour line in the S1 on a visual studio development platform.
6. The point cloud based CT and MRI3D/3D image registration method of claim 1, wherein: the three-dimensional reconstruction is to perform three-dimensional reconstruction on CT and MRI after registration transformation in the same scene.
7. The point cloud based CT and MRI3D/3D image registration method of claim 1, wherein: and in the S5, the slice is a slice of the MRI volume data after the CT and the registration transformation in the same coordinate and the same direction, and the window adjustment work is performed on the slice by utilizing the imaging advantage of the CT on the bone after the slice is cut, so that the bone window is adjusted.
8. The point cloud-based CT and MRI3D/3D image registration method of claim 1, wherein: the slice of the MRI in S5 takes the default window level and the MRI is overlaid directly onto the CT with 50% transparency.
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| CN116797726B (en) * | 2023-05-20 | 2024-05-07 | 北京大学 | Organ three-dimensional reconstruction method, device, electronic equipment and storage medium |
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