KR101298435B1 - Object registration apparatus and method thereof - Google Patents

Abstract

PURPOSE: An object matching device and a method thereof are provided to accurately match different coordinate systems by subdividing reference coordinate data and to improve accuracy by inputting the minimum coordinate data. CONSTITUTION: An object matching device (1000) includes a first receiving unit (100) receiving first coordinate group data including first coordinate data on the basis of a first coordinate system for an object; a precision modeling unit (200) subdividing the first coordinate group data by using the first coordinate group data; a second receiving unit (300) receiving second coordinate group data including second coordinate data on the basis of a second coordinate system for the object; and a matching unit (500) converting the second coordinate group data into the first coordinate system and matching the second coordinate group data with the subdivided first coordinate group data. [Reference numerals] (100) First receiving unit; (200) Precision modeling unit; (300) Second receiving unit; (400) Detection area designating unit; (500) Matching unit; (510) Conversion matrix calculation unit; (520) Coordinate conversion unit; (530) Error calculation unit

Description

Object registration apparatus and method

Embodiments relate to an object matching apparatus and a method thereof, and more particularly, to an apparatus and method for matching two or more coordinate systems representing an object.

The process of matching two shape data generated based on different coordinate systems with one coordinate system and arranging them to the nearest curved surface is commonly referred to as registration. The matching process is frequently used in the manufacture of products, such as the molding of ships or automobiles.

Recently, as the number of surgical operations using a robot increases, the necessity of a high precision matching device and a method for aligning virtual 3D image data prepared for surgery and a surgical part of a real patient is increasing. Pin based registration and image based registration methods are used. For pin reference registration, the CT image is taken while a plurality of pins inserted to the femur of the patient are inserted in the thigh before surgery, and then the machining path of the robot is set based on the CT image. At this time, the reference coordinate system on the machining path of the robot is set by a plurality of pins in the CT image. When the design of the machining path is completed, the matching is performed by matching the pins embedded in the surgical part of the patient with the pins in the CT image, that is, the reference pins of the robot's machining path. However, since the pin reference registration requires inserting a plurality of pins into the affected part of the patient before surgery, pain and discomfort occur in the patient, and there is an inconvenience that the pins must be inserted into the affected part until the surgery. In image-based registration, a CT image of a patient's femur is obtained before surgery and a processing path is set based on this image. The registration is performed by matching the 3D image obtained from the CT image with the 2D x-ray image of the patient's femur during the actual operation. However, this image reference registration involves a lot of errors in order to obtain 3D surface modeling of the femur from CT images, and also in the edge detection process in the 2D x-ray image obtained during surgery. . Although a clamp (registered patent 10-0921407) is used to match the position of the bone, there is a lack of a matching mechanism for precise matching within a short time.

Patent Registration 10-0921407

According to an aspect of the present invention, the reference coordinate data may be subdivided to precisely match different coordinate systems of the object.

According to one aspect of the present invention, only minimum coordinate data can be input to achieve a high level of accuracy.

According to another aspect of the present invention, it is possible to reduce the error range compared to the prior art.

According to an aspect of the present invention, the first receiver for receiving the first group of coordinate data for the object based on the first coordinate system and including a plurality of first coordinate data; A precision molding unit for subdividing first coordinate group data using the first coordinate group data; A second receiver configured to receive second object group data including a plurality of second coordinate data with respect to the object based on a second coordinate system; And a matching unit configured to coordinate-convert the second coordinate group data into the first coordinate system to match the second coordinate group data to the divided first coordinate group data.

According to another aspect of the present invention, the precision molding unit subdivids the first coordinate group data by generating additional coordinate data using the center of gravity coordinate data of a figure formed by a closed curve connecting the plurality of adjacent first coordinate data. An object matching device is provided.

According to another aspect of the present invention, the method further includes a detection area designator for designating a first area as the detection area among the subdivided first coordinate group data. An object matching device is provided that matches the granular first coordinate group data.

According to another aspect of the present invention, the matching unit includes: a transformation matrix calculator configured to calculate a first transformation matrix between the second coordinate group data and the first coordinate data corresponding to the detection area; Provided is an object matching device including a coordinate transformation unit for transforming the second coordinate group data into the first coordinate system using the first transformation matrix.

According to another aspect of the present invention, the matching unit has a shortest distance with the coordinate-converted second coordinate group data and includes first first-lower point coordinate data included in the subdivided first coordinate group data within the detection area, An object matching device is further provided, including an error calculator configured to determine whether a first average of distances between the coordinate-converted second coordinate group data is equal to or less than a predetermined threshold.

According to another aspect of the present invention, when it is determined in the error calculator that the first distance average exceeds a predetermined threshold, the detection area designation unit selects a second area corresponding to the near point coordinate data as the detection area. And the transformation matrix calculation unit calculates a second transformation matrix between the second coordinate group data and the first coordinate group data corresponding to the detection area, and the coordinate transformation unit uses the second transformation matrix. An object matching device is provided, which coordinate-converts two-coordinate group data into the first coordinate system.

According to another aspect of the present invention, the error calculation unit has a shortest distance with the second coordinate group data coordinated using the second transformation matrix and is included in the subdivided first coordinate group data inside the detection area. An object matching device is provided that determines whether a second distance average between second root point coordinate data and the coordinate-converted second coordinate group data is equal to or less than an already set threshold.

According to another aspect of the present invention, when the coordinate-converted second coordinate group data is outside the first region, the detection area designator adjusts the size of the first region to include the coordinate-converted second coordinate group data. An object matching device is provided, which designates an enlarged third region as a detection region.

According to another aspect of the present invention, the method comprising: receiving first coordinate group data about an object based on a first coordinate system and including a plurality of first coordinate data; Subdividing first coordinate group data using the first coordinate group data; Receiving second coordinate group data for the object based on a second coordinate system and including a plurality of second coordinate data; And coordinating the second coordinate group data into the first coordinate system to match the second coordinate group data with the segmented first coordinate group data.

According to another aspect of the present invention, the subdividing of the first coordinate group data may include generating additional coordinate data using the center of gravity coordinate data of a figure formed by a closed curve connecting the plurality of adjacent first coordinate data. Including an object matching method is provided.

According to another aspect of the present invention, subdividing the first coordinate group data further includes designating a first region of the subdivided first coordinate group data as a detection region.

According to another aspect of the present invention, the step of matching the first coordinate group data based on the detection area, calculates a first transformation matrix between the second coordinate group data and the first coordinate data corresponding to the detection area. Doing; The method of claim 1, further comprising: transforming the second coordinate group data into the first coordinate system using the first transformation matrix.

According to another aspect of the present invention, the step of transforming the coordinates into the first coordinate system may include a first coordinate group having the shortest distance from the coordinate transformed second coordinate group data and included in the subdivided first coordinate group data within the detection area. And determining whether a first distance average between the first near point coordinate data and the coordinate-converted second coordinate group data is equal to or less than a predetermined threshold.

According to another aspect of the present invention, the determining of whether the first distance average is less than or equal to a predetermined threshold may include determining whether the first distance average exceeds a predetermined threshold; An object matching method is further provided, further comprising designating two regions as the detection regions.

According to another aspect of the present invention, after the step of designating the second region corresponding to the near-bottom coordinate data as the detection region, calculating the first transformation matrix, coordinate transformation to the first coordinate system, An object matching method is provided, which repeatedly executes determining whether the one-distance average is less than or equal to a predetermined threshold.

According to another aspect of the present invention, the step of matching the first coordinate group data based on the detection area may include: when the coordinate-converted second coordinate group data is outside the first area, the coordinate-converted second An object matching method is further provided, further comprising: designating a third region, in which the size of the first region is enlarged to include coordinate group data, as a detection region.

By subdividing the coordinate data of an object, extended data can be generated. By repeating the search for the root point using the extended data, it is possible to precisely match.

In addition, by receiving the coordinate data using the detection area representing the characteristics of the object, it is possible to precisely match even a small number of data. Therefore, there is an effect that can significantly reduce the error range compared to the prior art.

1 is a diagram illustrating an internal configuration of an object matching device according to an exemplary embodiment.
2A is a diagram illustrating a three-dimensional representation of an object, according to an exemplary embodiment.
2B is a view illustrating an object in three dimensions according to an embodiment.
2C is a diagram illustrating an object in 3D according to an embodiment.
3A is a first diagram representing an object segmentation method according to an exemplary embodiment.
3B is a first diagram representing an object segmentation method according to an exemplary embodiment.
3C is a first diagram representing an object segmentation method according to an exemplary embodiment.
3D is a first diagram illustrating an object segmentation method, according to an exemplary embodiment.
4 is a diagram illustrating a second receiver according to an exemplary embodiment.
5 is a diagram illustrating reception of second coordinate group data of the second receiver according to an exemplary embodiment.
6 is a diagram illustrating second coordinate group data, according to an exemplary embodiment.
7 is a diagram illustrating a detection area according to an exemplary embodiment.
FIG. 8A is a diagram illustrating coordinate transformed second coordinate group data, according to an exemplary embodiment. FIG.
FIG. 8B is a diagram illustrating retrieved nearest point coordinate data, according to an exemplary embodiment. FIG.
8C is a diagram illustrating an extension of a detection area according to an exemplary embodiment.
9 is a diagram illustrating an object matching method, according to an exemplary embodiment.
FIG. 10A illustrates a distal femoral center of the femur according to one embodiment. FIG.
FIG. 10B is a view showing the patella and anterior of the femur (Anterior Supra Pouch) according to an embodiment.
FIG. 10C is a diagram illustrating a medial epicondyle of the femur according to one embodiment.
FIG. 10D illustrates the outer and inner sides of the femur (Lateral Condyle Middle) according to an embodiment.
FIG. 10E is a view illustrating the medial long medial of the femur according to an embodiment.
FIG. 10F illustrates the long lateral side of the femur according to one embodiment.
FIG. 10G is a diagram illustrating a medial condyle superior of the femur according to one embodiment.
FIG. 10H is a diagram illustrating the medial condyle middle of the femur according to one embodiment. FIG.
FIG. 10I is a diagram illustrating a medial condyle posterior of the femur according to one embodiment. FIG.
FIG. 10J is a view illustrating the outer and upper portions of the femur (Lateral Condyle Superior) according to an embodiment.
FIG. 10K is a view showing the outer and middle of the femur (Lateral Condyle Middle) according to an embodiment.
FIG. 10L is a view illustrating the outer and posterior portion of the femur (Lateral Condyle Posterior) according to an embodiment.
FIG. 11A illustrates an articular middle of the tibia according to one embodiment. FIG.
FIG. 11B is a view illustrating a supra tuberosity of the tibia according to one embodiment. FIG.
FIG. 11C is a diagram illustrating a medial superior condyle of the tibia according to an embodiment.
FIG. 11D illustrates a Gerdy's Tubercle of the tibia, according to an embodiment. FIG.
FIG. 11E illustrates a medial anterior surface of the tibia according to one embodiment. FIG.
FIG. 11F is a view illustrating the medial long medial of the tibia, according to an embodiment.
11G is a view showing the long lateral of the tibia (Tong Lateral) according to an embodiment.
FIG. 11H is a diagram illustrating the medial surface and articular surface medial of the tibia according to one embodiment. FIG.
FIG. 11I is a view showing the outer surface and the articular surface of the tibia (Articular Surface Lateral) according to an embodiment.
11J is a diagram illustrating a medial inferior surface, according to an exemplary embodiment.
11K is a diagram illustrating a medial tuberosity of the tibia according to one embodiment.

DETAILED DESCRIPTION OF THE INVENTION The following detailed description of the invention refers to the accompanying drawings that show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with an embodiment. It is also to be understood that the position or arrangement of the individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, Those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

1 is a diagram illustrating an internal configuration of an object matching apparatus 1000 according to an exemplary embodiment. The object matching device 1000 serves to match the first coordinate group data and the second coordinate group data of the object. The object matching device 1000 may include a first receiving unit 100, a precision molding unit 200, a second receiving unit 300, a detection area designating unit 400, and a matching unit 500.

The first receiver 100 receives the first coordinate group data including the plurality of first coordinate data with respect to the object based on the first coordinate system. In this case, the object has a three-dimensional shape and may be a hull or a car according to the purpose of registration, and may be a human femur or tibia. The object 1 shown in FIG. 2A represents an abstract form for generalization of the description. The plurality of first coordinate group data 10 for the object illustrated in FIG. 2B is data representing a surface of the object 1 by coordinates, and includes a plurality of first coordinates. In one embodiment, the plurality of first coordinate data 10 may be an image by computed tomography (CT) of the object (1). The plurality of first coordinate data 10 is based on the first coordinate system. Based on the CT image, the coordinate system of the CT image will be the first coordinate system.

The precision molding unit 200 serves to subdivide the first coordinate group data by using the first coordinate group data 10. The first coordinate group data 10 of FIG. 2B is subdivided to generate the divided first coordinate group data 20 of FIG. 2c.

In one embodiment, the precision molding part 200 generates the first coordinate group data 10 by generating additional coordinate data using the center of gravity coordinate data of the figure formed by the closed curve connecting the plurality of adjacent first coordinate data. Can be broken down. The additional coordinate data may be any one of the center of gravity coordinate data, the intersection coordinate data in which a straight line connecting the center of gravity coordinate data with any one of the plurality of adjacent first coordinate data meets the closed curve. Referring to FIG. 3A, assume that there is adjacent coordinate data a1, a2, a3 among the first coordinate group data 10. Referring to Figure 3b, it can be seen that the closed curve connecting a1, a2, a3 is a triangle and the center of gravity is a4. Referring to FIG. 3C, points a5, a6, and a7 which meet a closed curve by connecting a1 and a4, a2 and a4, a3 and a4, respectively, are generated. Referring to FIG. 3D, data may be added by generating a9, a10, and a11 in the same process. Since the center of gravity can be said to be an average point having a characteristic of three points, it is a method of increasing the number of data while maintaining the properties of the first coordinate group data 10. As a result, the first coordinate group data 20 segmented is generated.

The second receiver 300 receives the second coordinate group data with respect to the object 1 based on the second coordinate system and includes a plurality of second coordinate data. 4 and 6, in one embodiment, the second receiver may include a digitizer 310, and the digitizer 310 includes a plurality of second coordinate data in contact with the object 10. It serves to receive the second coordinate group data (91, 92, 93, 94). The second coordinate group data 91, 92, 93, 94 are based on the second coordinate system as described above. Matching is the operation of matching the first coordinate system and the second coordinate system described above.

The detection area designation unit 400 serves to designate a specific area of the subdivided first coordinate group data 20 as the detection area. The second receiver 300 finds the coordinates most corresponding to the detection area of the first coordinate group data 20 subdivided from the surface of the object 1 and receives the coordinates as the second coordinate group data. Referring to FIG. 7, it can be seen that the detection areas 210, 220, 230, and 240 having a predetermined size are designated on the surface of the subdivided first coordinate group data 20, and data representing the detection area is detected. It was set as the coordinate data 21, 22, 23, 24 which are in the midpoint of an area | region. However, the data representing the detection area is not limited to the central point data, and it is sufficient that the data corresponding to the detection area is fixed by a certain standard. Although it will be described later, by specifying the detection area repeatedly, the accuracy of matching is improved.

In one embodiment, with reference to FIGS. 10A-10L, the detection area is the distal femoral center of the femur (Distal Center), the patella and anterior of the femur (Anterior Supra Pouch), the medial epicondyle of the femur, and the outside of the femur. Lateral Condyle Middle, Long Medial of the Femur, Long Lateral of the Femur, Medial Condyle Superior of the Femur, Medial Condyle Middle of the Femur, Medial of the Femur Medial and posterior (Medial Condyle Posterior), the outer and upper portion of the femur (Lateral Condyle Superior), the outer and middle of the femur (Lateral Condyle Middle), the lateral and posterior of the femur (Lateral Condyle Posterior). The twelve regions described above can recombine the morphology of the femur.

In another embodiment, the detection zone may include the articular middle of the tibia, the supra tuberosity of the tibia, the medial superior condyle of the tibia, the gerdy`s tubercle of the tibia, Medial Anterior Surface of the Tibia, Long Medial of the Tibia, Long Lateral of the Tibia, Articular Surface Medial of the Tibia, Lateral and Articular Surface of the Tibia Lateral, medial and lower (Medial Inferior Surface), tibial medial medial (Medial Tuberosity). The tibia can be recombined into the eleven regions described above.

11A through 11K, the matching unit 500 coordinate-converts the second coordinate group data 91, 92, 93, and 94 into a first coordinate system, thereby converting the second coordinate group data 91, 92,. 93, 94) to match the first coordinate group data. The matching unit 500 may include a transformation matrix calculator 510, a coordinate converter 520, and an error calculator 530.

The transformation matrix calculator 510 calculates a first transformation matrix between the second coordinate group data 91, 92, 93, and 94 and the first coordinate data 21, 22, 23, and 24 corresponding to the detection area. It plays a role.

를 계산하면, 수학식1과 같다.The second coordinate group data (91, 92, 93, 94) is called the set P and the first coordinate data (21, 22, 23, 24) is called the set X, and the center of mass coordinates of each of P and Q

Is calculated as Equation 1.

는 수학식 2와 같이 계산하였다.Cross-Covariance Matrix of P, X

Was calculated as in Equation 2.

를 수학식 3과 같이 정의할 수 있다. Symmetric matrix using each term in Equation 2

May be defined as in Equation 3.

는 교차 공분산 행렬

의 대각선 요소들의 합을 의미하며, 는 단위 행렬이다. 열 벡터

는 교차 공분산 행렬

의 비대칭 요소 값인

로부터 정의한 값으로

를 의미한다. 수학식3에서 제시한 Q의 최대 고유값과 최대 고유 벡터

를 계산한다. 이 때 고유 벡터

은 최적 회전을 나타내며 수학식 4와 같이 정의한다.here

Cross covariance matrix

Is the sum of the diagonal elements of, and is the identity matrix. Column vector

Cross covariance matrix

Is an asymmetric value of

As defined by

. Maximum Eigenvalues and Maximum Eigenvectors of Q in Equation 3

. Eigenvectors

Denotes the optimum rotation and is defined as in Equation 4.

The eigenvectors defined in Equation 4 represent the optimal rotation transformation. The eigenvectors may be used to define a rotation matrix as shown in Equation 5 below.

The optimal motion vector is calculated using the rotation matrix R and the mass center vector calculated in Equation 1 as shown in Equation 6.

Finally, the homogeneous transformation matrix can be defined as shown in Equation 7 by using the rotation matrix and the optimal motion vector calculated above.

It becomes a first transformation matrix. However, the method of deriving the first transformation matrix is not limited to the above-described calculation method.

The coordinate transformation unit 520 coordinates the transformation of the second coordinate group data 91, 92, 93, and 94 into the first coordinate system using the first transformation matrix. In one embodiment, the coordinate transformation unit 520 may multiply the inverse matrix of the first transformation matrix by the second coordinate group data to convert the coordinates into the first coordinate system. Referring to FIG. 8A, the coordinate-converted second coordinate group data is 91 ′, 92 ′, 93 ′, 94 ′.

The error calculator 530 has the shortest distance from the coordinate-converted second coordinate group data 91 ', 92', 93 ', 94' and is included in the subdivided first coordinate group data inside the detection area. It is determined whether a first distance average between first near-bottom coordinate data and the coordinate-converted second coordinate group data is equal to or less than a predetermined threshold. If it is determined that it is less than or equal to the threshold already set, the conversion matrix is appropriate in the tolerance range, and the matching is completed.

8A is a diagram illustrating coordinates in which a second coordinate group data and a detection region are coordinate-converted into a first coordinate system, according to an exemplary embodiment. FIG. 8B is an enlarged view of second coordinate data 91 'coordinated to one detection area portion 210 of FIG. 8A. Referring to FIG. 8B, it can be seen that the root point coordinate data representing the distance closest to the coordinate-converted second coordinate data 91 ′ is 31. The distance between the first near-bottom coordinate data 31 and the coordinate-converted second coordinate data 91 'is a first distance and the first distance average calculated for each of the coordinate-converted second coordinate data each exceeds a predetermined threshold. Determine if In one embodiment, 0.5 mm can be set as the threshold for femoral registration. In one embodiment the distance average may be an RMS average. Referring to 8a, when the coordinate-converted second coordinate data 92 ′ is outside the detection region 220, the detection region 220 includes the coordinate-converted second coordinate data 92 ′ as in 8c. The size of the area can be enlarged. In one embodiment, the detection area designation 400 may be implemented by designating an enlarged detection area. The coordinate-converted second coordinate data 92 'must be included in the detection area 220 so that the root point can be calculated as close to the coordinate-converted second coordinate data 92' as possible. Otherwise, the exact root point could not be measured, resulting in a large error.

If it is determined by the error calculator 530 that the first distance average exceeds a threshold already set, the detection area designation unit 400 designates a second area corresponding to the near point coordinate data 32 as the detection area. do. After that, a series of operations of the transformation matrix calculation unit 510, the coordinate transformation unit 520, and the error calculation unit 530 are repeatedly performed until the distance average is less than or equal to a threshold that is already set. In detail, the detection area designation unit 400 re-designates a second detection area (not shown), which is similar in size to the existing first detection area 210, focusing on the near-bottom coordinate data 31. The transformation matrix calculator 510 calculates a second transformation matrix between the second coordinate group data 92 ′ and the first coordinate group data corresponding to the second detection region (not shown), and the coordinate transformation unit 520. Coordinate transforms the second coordinate group data 92 to the first coordinate system using a second transform matrix. The error calculator 530 has a shortest distance from the second coordinate group data (not shown) coordinate-transformed using a second transformation matrix and is included in the subdivided first coordinate group data inside the second detection area. The second distance average between the second near point coordinate data (not shown) and the second coordinate group data (not shown) coordinate-transformed using the second transformation matrix is again determined to be equal to or less than a predetermined threshold. Repeated operations can minimize the range of error.

9 is a diagram illustrating an object matching method according to an exemplary embodiment. First, a first coordinate group data including a plurality of first coordinate data is received with respect to an object based on a first coordinate system (S10). In one embodiment, the first coordinate group data may be computed tomography (CT) data. Thereafter, the first coordinate group data is subdivided using the first coordinate group data (S20). In one embodiment, the additional coordinate data may be generated using the center of gravity coordinate data of the first coordinate group data. Thereafter, the second coordinate group data including the plurality of second coordinate data is received with respect to the object based on the second coordinate system (S30). In one embodiment, the second coordinate group data may be received through the digitizer. Thereafter, a first region of the subdivided first coordinate group data is designated as a detection region (S40), and a first transformation matrix between the second coordinate group data and the first coordinate data corresponding to the detection region is calculated (S50). Coordinate transformation of the second coordinate group data into the first coordinate system is performed using the first transformation matrix (S60). Thereafter, the first distance between the first near point coordinate data included in the subdivided first coordinate group data within the detection area and having the shortest distance with the coordinate transformed second coordinate group data, and the first distance between the coordinate transformed second coordinate group data The processes of S40, S50 and S60 are repeated until the average is less than or equal to the threshold already set (S70). Matching is complete when the distance average falls below the already set threshold.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, Those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

Therefore, the spirit of the present invention should not be limited to the embodiments described above, and all of the equivalents or equivalents of the claims, as well as the claims below, are included in the scope of the spirit of the present invention. I will say.

1: object
10: first coordinate group data
20: Granular first coordinate group data
21, 22, 23, 24: first coordinate data which is the center of the detection area
31: Near point coordinate data
91, 92, 93, 94: Second coordinate data
91 ', 92', 93 ', 94': Coordinate-converted second coordinate data
100: first receiving unit
200: precision molding part
210, 220, 230, 240: detection range
300: second receiving unit
310: second receiver in the form of a digitizer
400: detection area designation unit
500: matching part
510: transformation matrix calculation unit
520: coordinate conversion unit
530: error calculation unit

Claims (25)

A first receiving unit receiving first coordinate group data including a plurality of first coordinate data with respect to the object based on the first coordinate system;
A precision molding unit for subdividing first coordinate group data using the first coordinate group data;
A second receiver configured to receive second object group data including a plurality of second coordinate data with respect to the object based on a second coordinate system;
And a matching unit which coordinate-converts the second coordinate group data into the first coordinate system to match the second coordinate group data to the subdivided first coordinate group data.
The method of claim 1,
And the precision molding unit subdivids the first coordinate group data by generating additional coordinate data using the center-of-center coordinate data of a figure formed by a closed curve connecting the plurality of adjacent first coordinate data.
The method of claim 2,
The additional coordinate data is any one of the center of gravity coordinate data of the plurality of adjacent first coordinate data, the intersection coordinate data of any one of the plurality of adjacent first coordinate data and the straight line connecting the center of gravity coordinate data meets the closed curve. Object matching device comprising a.
The method of claim 1,
And the second receiver includes a digitizer.
The method of claim 1,
And a detection area designator for designating a first area as the detection area among the subdivided first coordinate group data.
And the matching unit matching the second coordinate group data to the first coordinate group data subdivided based on the detection area.
The method of claim 5,
The matching unit may include:
A transformation matrix calculator for calculating a first transformation matrix between the second coordinate group data and the first coordinate data corresponding to the detection area;
And a coordinate transformation unit configured to transform the second coordinate group data into the first coordinate system using the first transformation matrix.
The method according to claim 6,
The matching unit may include:
Between first coordinate coordinate data of the first coordinate group included in the subdivided first coordinate group data within the detection area and having the shortest distance with the coordinate-converted second coordinate group data, and between the coordinate converted second coordinate group data. And an error calculator configured to determine whether the first distance average is less than or equal to a predetermined threshold.
The method of claim 7, wherein
If it is determined by the error calculator that the first distance average exceeds a threshold already set,
The detection area designator designates a second area corresponding to the near point coordinate data as the detection area,
The transformation matrix calculator calculates a second transformation matrix between the second coordinate group data and the first coordinate group data corresponding to the detection area.
And the coordinate transformation unit coordinate-converts the second coordinate group data to the first coordinate system using the second transformation matrix.
9. The method of claim 8,
The error calculator includes a second root point coordinate data included in the first coordinate group data subdivided within the detection area and having the shortest distance with the second coordinate group data coordinated using the second transformation matrix. And determining whether a second distance average between the coordinate-converted second group of coordinate data is equal to or less than a predetermined threshold.
The method according to claim 6,
If the coordinate-converted second coordinate group data is outside the first area,
And the detection area designator designating a third area in which the size of the first area is enlarged to include the coordinate transformed second coordinate group data as a detection area.
The method of claim 5,
The detection area is the distal femoral center of the femur (Distal Center), the patella and anterior of the femur (Anterior Supra Pouch), the medial epicondyle of the femur, the Lateral Condyle Middle of the femur, and the long axis of the femur. Long Medial, Long Lateral of the Femur, Medial Condyle Superior of the Femur, Medial Condyle Middle of the Femur, Medial Condyle Posterior of the Femur, of the Femur An object registration device, which is any one of lateral and upper (Lateral Condyle Superior), lateral and middle of the femur (Lateral Condyle Middle), and the lateral and posterior part of the femur (Lateral Condyle Posterior).
The method according to claim 6,
The detection area includes the articular middle of the tibia, the supra tuberosity of the tibia, the medial superior condyle of the tibia, the gerdy`s tubercle of the tibia, and the medial side of the tibia. Medial Anterior Surface, Long Medial of Tibia, Long Lateral of Tibia, Medial and Articular Surface Medial of Tibia, Lateral and Articular Surface Lateral of Tibia, An object registration device, which is any one of a medial inferior surface and a medial tuberosity region of the tibia.
Receiving first coordinate group data for the object based on the first coordinate system and including a plurality of first coordinate data;
Subdividing first coordinate group data using the first coordinate group data;
Receiving second coordinate group data for the object based on a second coordinate system and including a plurality of second coordinate data;
And converting the second coordinate group data into the first coordinate system to match the second coordinate group data with the divided first coordinate group data.
The method of claim 13,
The subdividing the first coordinate group data may include:
And generating additional coordinate data using the center of gravity coordinate data of the figure formed by the closed curve connecting the plurality of adjacent first coordinate data.
15. The method of claim 14,
The additional coordinate data is any one of the center of gravity coordinate data of the plurality of adjacent first coordinate data, the intersection coordinate data of any one of the plurality of adjacent first coordinate data and the straight line connecting the center of gravity coordinate data meets the closed curve. Object matching method comprising a.
The method of claim 13,
Receiving the second coordinate group data,
And receiving, by the digitizer, the second coordinate group data.
The method of claim 13,
The subdividing the first coordinate group data may include:
And specifying a first area of the subdivided first coordinate group data as a detection area.
18. The method of claim 17,
The step of matching the first coordinate group data,
And matching the second coordinate group data to the subdivided first coordinate group data based on the detection area.
19. The method of claim 18,
Matching the first coordinate group data based on the detection area,
Calculating a first transformation matrix between the second coordinate group data and the first coordinate data corresponding to the detection area;
And transforming the second coordinate group data into the first coordinate system by using the first transformation matrix.
20. The method of claim 19,
Converting the coordinates to the first coordinate system,
Between first coordinate coordinate data of the first coordinate group included in the subdivided first coordinate group data within the detection area and having the shortest distance with the coordinate-converted second coordinate group data, and between the coordinate converted second coordinate group data. Determining whether the first distance average is less than or equal to a threshold already established.
21. The method of claim 20,
The determining of whether the first distance average is less than or equal to a predetermined threshold may include:
If it is determined that the first distance average exceeds a threshold already set,
And specifying a second area corresponding to the near point coordinate data as the detection area.
The method of claim 21,
After the step of designating a second area corresponding to the near point coordinate data as the detection area,
Calculating the first transformation matrix, performing coordinate transformation into the first coordinate system, and determining whether the first distance average is less than or equal to a predetermined threshold.
19. The method of claim 18,
Matching the first coordinate group data based on the detection area,
If the coordinate-converted second coordinate group data is outside the first region, designating a third region in which the size of the first region is enlarged to include the coordinate-converted second coordinate group data as a detection region. The object matching method further comprising.
18. The method of claim 17,
The detection area is the distal femoral center of the femur (Distal Center), the patella and anterior of the femur (Anterior Supra Pouch), the medial epicondyle of the femur, the Lateral Condyle Middle of the femur, and the long axis of the femur. Long Medial, Long Lateral of the Femur, Medial Condyle Superior of the Femur, Medial Condyle Middle of the Femur, Medial Condyle Posterior of the Femur, of the Femur A method of object registration, which is one of lateral and upper (Lateral Condyle Superior), lateral and middle of the femur (Lateral Condyle Middle), and lateral and posterior (Lateral Condyle Posterior) areas of the femur.
18. The method of claim 17,
The detection area includes the articular middle of the tibia, the supra tuberosity of the tibia, the medial superior condyle of the tibia, the gerdy`s tubercle of the tibia, and the medial side of the tibia. Medial Anterior Surface, Long Medial of Tibia, Long Lateral of Tibia, Medial and Articular Surface Medial of Tibia, Lateral and Articular Surface Lateral of Tibia, A method of object registration, which is any one of a medial inferior surface and a medial tuberosity region of the tibia.

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