A method for self-adapting registration of image landmarks based on a triangular mesh model

Abstract

The application discloses a kind of image mark point adaptive registration methods based on triangular mesh model, adopt computed tomography image as input data, utilize its clear display effect to carry out surgical navigation image registration.Subsequently, computer image processing technology is used to preliminarily simplify CT image, and the triangular mesh model is extracted in the binary image obtained by processing, to realize the efficient acquisition of mark point;The mark point extracted is adaptively grouped according to its area size, and then the geometric center point of the grouped mark point is extracted to reduce the complexity of subsequent data processing;Finally, the conversion matrix and error value are calculated according to the obtained center point and the input actual coordinate point, and the best matching result is obtained;Automatic registration method can ensure shorter registration time, more stable registration process and more accurate registration effect, adapt to the surgical navigation system required to improve image registration efficiency.

Description

Technical Field

[0001] This invention relates to the field of medical image registration technology, and in particular to an adaptive registration method for image markers based on a triangular mesh model. Background Technology

[0002] With the popularization of precision medicine and the development of minimally invasive surgical techniques, medical image-based navigation systems are playing an increasingly important role in surgical procedures. Medical image registration is a core technology of surgical navigation, its task being to achieve precise mapping between sensor positioning data and medical images by calculating the spatial transformation matrix from the sensor coordinate system to the image navigation coordinate system. Traditional medical image registration methods often rely on manual selection of special markers for registration, which suffers from drawbacks such as long operation time, dependence on physician experience, and poor registration stability. Especially in fields such as orthopedics and neurosurgery, where spatial positioning accuracy is extremely important, existing methods are insufficient to meet the dual requirements of registration efficiency and accuracy for real-time intraoperative navigation. Although automatic registration algorithms based on marker recognition have emerged in recent years, they still face problems such as low marker recognition efficiency and long matching time when dealing with complex image structures; in addition, existing methods lack effective utilization of marker features, making it difficult to achieve highly robust registration parameter estimation.

[0003] Therefore, it is necessary to develop an adaptive registration method for image markers based on a triangular mesh model to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to design an adaptive registration method for image markers based on a triangular mesh model in order to solve the above problems.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] An adaptive registration method for image markers based on a triangular mesh model includes:

[0007] S1. Input image data and sensor coordinates;

[0008] S2. Image preprocessing is performed using thresholding, erosion, and dilation operations;

[0009] S3. Use the isosurface extraction algorithm to extract the triangular mesh model of the marker points;

[0010] S4. The triangular mesh model is divided into multiple independent marker points by connected components. Determine whether the number of marker points is greater than the actual number of points. If yes, proceed to step S5; otherwise, proceed to step S6.

[0011] S5. Use an adaptive grouping algorithm to group the markers with similar area sizes together, and then proceed to the next step;

[0012] S6. Find the geometric center of the marker point;

[0013] S7. Match and combine the center point set with the sensor coordinate set;

[0014] S8. Calculate the transformation matrix and error value for each combination;

[0015] S9. Output the optimal matrix and error value to complete the registration.

[0016] Further, step S1 includes: selecting a 3D CT image file containing multiple sensor positioning patches as input. The 3D CT image is volume data formed by stacking multiple layers of 2D CT slices sequentially in the same direction, represented as:

[0017]

[0018] in, These represent the number of voxels in the cross-section, sagittal plane, and coronal plane of the three-dimensional volume data, respectively. This represents the CT value at the voxel position (x, y, z).

[0019] Further, step S2 includes: thresholding the image preprocessing into binary segmentation, the formula of which is:

[0020]

[0021] In the above formula, The threshold value is used; after thresholding, a 3×3×3 cube structuring element is used. Erosion operation on binary image:

[0022]

[0023] In the above formula, the structural element , The offset represents the displacement relative to the center point (x, y, z). This represents taking the minimum value; its mathematical morphological abbreviation is:

[0024]

[0025] In the above formula, This represents the erosion operation;

[0026] Subsequent use of 5×5×5 cube structural elements Perform expansion calculations on the corrosion results:

[0027]

[0028] In the above formula, the structural element , yes The offset represents the displacement relative to the center point (x, y, z). This represents taking the maximum value; its mathematical morphological abbreviation is:

[0029]

[0030] in, Let R represent the dilation operation and R represent the final result of the preprocessed image; the complete process is shown in the following formula:

[0031] .

[0032] Further, step S3 includes: using an isosurface extraction algorithm to extract the isosurface from the binary image. Extracting the triangular mesh model The vertex set V is generated through voxel edge interpolation.

[0033]

[0034] In the above formula, Indicates the vertex position of the triangular mesh, only when Calculations are performed at that time, and and This represents the coordinates of the two endpoints of the voxel edge e, c=128 represents the isosurface threshold, and U represents taking all... The union of the faces; the set F of faces in the triangular mesh is obtained by looking up a table using the voxel state index:

[0035]

[0036] in, Here, c=128 represents the isosurface threshold, which is the Heaviside function. This represents the corner position of voxel C in a 3D image, and LUT is a predefined triangulated lookup table, and U represents the union of all results, ultimately generating a set of triangular facets.

[0037] Further, in step S5: an adaptive grouping algorithm is used to group markers with similar region sizes into a group, including:

[0038] definition For the input data, where For area identification number, Let D be the size of the region, and n represent the total number of markers; for D... Arrange in descending order to obtain an ordered sequence :

[0039]

[0040] right Perform statistical calculations:

[0041]

[0042] in, Define grouping threshold This yields the set of grouping critical point indices:

[0043]

[0044] set up For group numbering, and Then the grouping rules for:

[0045] .

[0046] Further, step S6 includes: after grouping the marker points, their specific coordinate values ​​in the image need to be obtained, and the geometric center point is calculated using the vertices of the marker point region:

[0047]

[0048] in, Represents the set of all vertices in region r. This represents the number of vertices in region r. Let r represent the geometric center coordinates of region r. The set of geometric center points of all marked points is called the center point set.

[0049] Further, step S7 includes: matching the center point set with the actual coordinate point set transmitted by the sensor according to the grouping, and selecting the same number of center points from each group as the number of actual coordinate points to form multiple unique combinations.

[0050] Further, steps S8 and S9 include: calculating the transformation matrix and error value for all unique combinations of all groups, and taking the transformation matrix and center point group with the smallest error value among all combinations as the final result; the formula for calculating the transformation matrix is:

[0051]

[0052] Where m represents the number of points. For actual coordinates, With M as the center point, M is a 4×4 transformation matrix. This is the optimal transformation matrix.

[0053] The beneficial effects of this invention are as follows:

[0054] In this application, the final center point group obtained after image preprocessing has accurate positions, and its transformation matrix achieves a lower error value than traditional methods, with an error value below 1.0. Employing adaptive grouping and isosurface extraction algorithms, this method has shorter processing time, achieving several times the time reduction even with many redundant points. Validation on different types of data demonstrates that this method can achieve highly robust registration parameter estimation, meeting the practical needs of surgical navigation. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the process of this application. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0057] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0058] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0059] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0060] Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0061] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, terms such as "set" and "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0062] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0063] like Figure 1 As shown, an adaptive registration method for image markers based on a triangular mesh model includes:

[0064] S1. Input image data and sensor coordinates;

[0065] Select a 3D CT image file containing four sensor positioning patches as input. The 3D CT image is volume data formed by stacking multiple layers of 2D CT slices sequentially in the same direction, represented as:

[0066]

[0067] in, These represent the number of voxels in the cross-section, sagittal plane, and coronal plane of the three-dimensional volume data, respectively. This represents the CT value at the voxel position (x, y, z).

[0068] S2. Image preprocessing is performed using thresholding, erosion, and dilation operations;

[0069] 3D CT images describe anatomical structures through voxel locations and CT values, and are three-dimensional grayscale data. To improve registration accuracy, image processing techniques can be used to screen, filter, and refine the landmarks contained in CT images. The initial operation of image preprocessing is image thresholding, i.e., binary segmentation, with the following formula:

[0070]

[0071] In the above formula, The threshold value is used; after thresholding, a 3×3×3 cube structuring element is used. Erosion operation on binary image:

[0072]

[0073] In the above formula, the structural element , yes The offset represents the displacement relative to the center point (x, y, z). This represents taking the minimum value; its mathematical morphological abbreviation is:

[0074]

[0075] In the above formula, This represents the erosion operation;

[0076] Subsequent use of 5×5×5 cube structural elements Perform expansion calculations on the corrosion results:

[0077]

[0078] In the above formula, the structural element , yes The offset represents the displacement relative to the center point (x, y, z). This represents taking the maximum value; its mathematical morphological abbreviation is:

[0079]

[0080] in, Let R represent the dilation operation and R represent the final result of the preprocessed image; the complete process is shown in the following formula:

[0081] .

[0082] S3. Use the isosurface extraction algorithm to extract the triangular mesh model of the marker points;

[0083] The image preprocessing result contains only all possible marker points. To improve the efficiency of marker point recognition, an isosurface extraction algorithm is used to extract marker points from the binary image. Extracting the triangular mesh model The vertex set V is generated through voxel edge interpolation.

[0084]

[0085] In the above formula, Indicates the vertex position of the triangular mesh, only when Calculations are performed at that time, and and This represents the coordinates of the two endpoints of the voxel edge e, c=128 represents the isosurface threshold, and U represents taking all... The union of the faces; the set F of faces in the triangular mesh is obtained by looking up a table using the voxel state index:

[0086]

[0087] in, Here, c=128 represents the isosurface threshold, which is the Heaviside function. This represents the corner position of voxel C in a 3D image, and LUT is a predefined triangulated lookup table, and U represents the union of all results, ultimately generating a set of triangular facets.

[0088] S4. The triangular mesh model is divided into multiple independent marker points by connected components. Determine whether the number of marker points is greater than the actual number of points. If yes, proceed to step S5; otherwise, proceed to step S6.

[0089] S5. Use an adaptive grouping algorithm to group the markers with similar area sizes together, thereby reducing the time required for matching corresponding points, and then proceed to the next step.

[0090] An adaptive grouping algorithm is used to group markers of similar region size into a single group, including:

[0091] definition For the input data, where For area identification number, Let D be the size of the region, and n represent the total number of markers; for D... Arrange in descending order to obtain an ordered sequence :

[0092]

[0093] right Perform statistical calculations:

[0094]

[0095] in, Define grouping threshold This yields the set of grouping critical point indices:

[0096]

[0097] set up For group numbering, and Then the grouping rules for:

[0098] .

[0099] S6. Find the geometric center of the marker point;

[0100] After grouping the marker points, we need to obtain their specific coordinates in the image. We calculate the geometric center point using the vertices of the marker point regions:

[0101]

[0102] in, Represents the set of all vertices in region r. This represents the number of vertices in region r. Let r represent the geometric center coordinates of region r. The set of geometric center points of all marked points is called the center point set.

[0103] S7. Match and combine the center point set with the sensor coordinate set;

[0104] The set of center points is matched with the set of actual coordinate points transmitted from the sensor according to the grouping. The number of center points selected from each group is the same as the number of actual coordinate points, forming multiple unique combinations.

[0105] S8. Calculate the transformation matrix and error value for each combination;

[0106] S9. Output the optimal matrix and error value to complete the registration.

[0107] For all unique combinations of all groups, calculate the transformation matrix and error value. The transformation matrix and center point group with the minimum error value among all combinations are taken as the final result. The formula for calculating the transformation matrix is:

[0108]

[0109] Where m represents the number of points. For actual coordinates, With M as the center point, M is a 4×4 transformation matrix. This is the optimal transformation matrix.

[0110] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An adaptive registration method for image markers based on a triangular mesh model, characterized in that, include: S1. Input image data and sensor coordinates; Step S1 includes: selecting a 3D CT image file containing multiple sensor positioning patches as input. The 3D CT image is volume data formed by stacking multiple layers of 2D CT slices sequentially in the same direction, represented as: ， in, These represent the number of voxels in the cross-section, sagittal plane, and coronal plane of the three-dimensional volume data, respectively. This represents the CT value at the voxel position (x, y, z); S2. Image preprocessing is performed using thresholding, erosion, and dilation operations; step S2 includes: thresholding the image preprocessing into binary segmentation, the formula of which is: ， In the above formula, The threshold value is used; after thresholding, a 3×3×3 cube structuring element is used. Erosion operation on binary image: ， In the above formula, the structural element , yes The offset represents the displacement relative to the center point (x, y, z). This represents taking the minimum value; its mathematical morphological abbreviation is: ， In the above formula, This represents the erosion operation; Subsequent use of 5×5×5 cube structural elements Perform expansion calculations on the corrosion results: ， In the above formula, the structural element , yes The offset represents the displacement relative to the center point (x, y, z). This represents taking the maximum value; its mathematical morphological abbreviation is: ， in, Let R represent the dilation operation and R represent the final result of the preprocessed image; the complete process is shown in the following formula: ； S3. Extract the triangular mesh model of the marker points using the isosurface extraction algorithm; Step S3 includes: using the isosurface extraction algorithm from the binary image Extracting the triangular mesh model The vertex set V is generated through voxel edge interpolation. ， In the above formula, Indicates the vertex position of the triangular mesh, only when Calculations are performed at that time, and and This represents the coordinates of the two endpoints of the voxel edge e, c=128 represents the isosurface threshold, and U represents taking all... The union of the faces; the set F of faces in the triangular mesh is obtained by looking up a table using the voxel state index: ， in, Here, c=128 represents the isosurface threshold, which is the Heaviside function. This represents the corner position of voxel C in a 3D image, and LUT is a predefined triangulated lookup table, and U represents the union of all results, ultimately generating a set of triangular facets; S4. The triangular mesh model is divided into multiple independent marker points by connected components. Determine whether the number of marker points is greater than the actual number of points. If yes, proceed to step S5; otherwise, proceed to step S6. S5. Use an adaptive grouping algorithm to group markers with similar region sizes together, and then proceed to the next step; In step S5: Use an adaptive grouping algorithm to group markers with similar region sizes together, including: definition For the input data, where For area identification number, Let D be the size of the region, and n represent the total number of markers; for D... Arrange in descending order to obtain an ordered sequence : ， right Perform statistical calculations: ， in, Define grouping threshold This yields the set of grouping critical point indices: ， set up For group numbering, and Then the grouping rules for: ； S6. Find the geometric center of the marker point; S7. Match and combine the center point set with the sensor coordinate set; S8. Calculate the transformation matrix and error value for each combination; S9. Output the optimal matrix and error value to complete the registration.

2. The image marker adaptive registration method based on a triangular mesh model according to claim 1, characterized in that, Step S6 includes: After grouping the marker points, their specific coordinates in the image need to be obtained, and the geometric center point is calculated using the vertices of the marker point regions. ， in, Represents the set of all vertices in region r. This represents the number of vertices in region r. Let r represent the geometric center coordinates of region r. The set of geometric center points of all marked points is called the center point set.

3. The image marker adaptive registration method based on a triangular mesh model according to claim 2, characterized in that, Step S7 includes: matching the center point set with the actual coordinate point set transmitted by the sensor according to the grouping, and selecting the same number of center points from each group as the number of actual coordinate points to form multiple unique combinations.

4. The image marker adaptive registration method based on a triangular mesh model according to claim 3, characterized in that, Steps S8 and S9 include: calculating the transformation matrix and error value for all unique combinations of all groups, and taking the transformation matrix and center point group with the minimum error value among all combinations as the final result; the formula for calculating the transformation matrix is: ， Where m represents the number of points. For actual coordinates, With M as the center point, M is a 4×4 transformation matrix. This is the optimal transformation matrix.

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