CN115186139A - Method for quickly searching features of attachment surface of implant based on graph structure - Google Patents
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Abstract
The invention discloses a graph structure-based rapid retrieval method for characteristics of an implant binding face, which comprises the following steps: the method comprises the following steps: collecting the shape of the implant, and constructing the characteristics of the binding surface of the implant according to the shape of the implant; step two: processing the characteristics of the binding surface of the implant into a directed graph structure, and constructing a characteristic database; step three: and retrieving the characteristics of the attaching surface of the implant from the characteristic database to construct a personalized attaching surface of the implant. The method integrates the characteristics of the binding surface of the implant through the graph structure, avoids the problem of zero fragmentation of semantic parameters of the implant, has the characteristics of rapidness, high efficiency and the like, is convenient for characteristic retrieval, and has important significance for realizing rapid design of customized implants. The invention provides scientific basis for rapid retrieval of the characteristics of the implant and has important significance for improving the design efficiency of the customized implant.
Description
Technical Field
The invention relates to a graph structure-based rapid retrieval method for characteristics of an implant binding face, and belongs to the technical field of computer-aided design.
Background
With the ever-increasing demand for personalized medicine, the need for customization of orthopedic implants has also risen. Existing customized design methods have difficulty achieving repeated use of the implant, resulting in time and effort consuming each design from scratch. Studies have shown that inappropriate implants can lead to a variety of clinical complications. It is considered that bone diseases are closely related to bone local anatomical morphology, bone size, fracture type, and the like. It is seen that there is a great need to design implants for individual patients that conform to their own anatomical features and conditions.
In recent years, as feature technologies are gradually applied to implant design, semantic feature parameters are integrated into implant features, and the efficiency of implant design is greatly improved. In particular, the design of the abutment surface of the implant requires a high degree of conformity to the bone surface. The characteristic technology ensures that the design of the binding surface is more flexible and free. However, the mere use of semantic parameters causes fragmentation of information, which is inconvenient for the integrated management of features, and particularly, for the retrieval of feature information. This results in the need for iterative communication between the orthopaedic surgeon and the implant designer, undoubtedly adding to the complexity of the overall design process. Unfortunately, this problem has not been solved to date.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for quickly searching the characteristics of the binding surface of an implant based on a graph structure. The method comprises the steps of representing the characteristics of the binding surface of the implant by using a graph structure, then representing the implant directed graph by using the adjacency matrix, realizing the customized design of the implant based on the retrieval of the adjacency matrix, and the like.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the method for quickly searching the characteristics of the binding surface of the implant based on the graph structure comprises the following steps:
the method comprises the following steps: collecting the shape of the implant, and constructing the characteristics of the joint surface of the implant according to the shape of the implant;
step two: processing the characteristics of the binding surface of the implant into a directed graph structure, and constructing a characteristic database;
step three: and retrieving the characteristics of the attaching surface of the implant from the characteristic database to construct a personalized attaching surface of the implant.
Further, the first step comprises:
step 1a: constructing the geometrical shape of the abutting surface of the implant according to the type of the implant;
step 1b: setting semantic feature parameters of an implant binding surface;
step 1c: and characterizing the abutting surface of the implant.
Further, in step 1a, the geometric shape is defined by feature points E p And characteristic line E c Kneading surface E s Forming;
characteristic line E c Including inner level feature lines and boundary feature lines E c4 ；
The grade of the internal grade characteristic line sequentially comprises a first grade characteristic line E from high to low c1 Second level characteristic line E c2 And a third level characteristic line E c3 ；
The growth direction of the internal grade characteristic line is as follows: from high level to low level.
Further, the feature point E p The extraction method comprises the following steps:
step 1: implant engaging surface E s At point p i As a center, radius r d The area within the range is considered as a plane, denoted as f (x, y);
at f (x, y), to increase the characteristic points E p Extracting efficiency, and carrying out denoising treatment on the plane f (x, y), wherein the denoising formula is as follows:
wherein, delta d Is a scale weight factorAn ideal value thereof is 1; delta s Is a perturbation constant having a value of [0,1]；
Step 2: calculating all the points p on the denoised plane f (x, y) j And point p i The matrix of the cross-variance is denoted as cov (p) i )；
And step 3: calculating eigenvalues m (i, 1), m (i, 2), m (i, 3) of the covariance matrix, and sorting the eigenvalues in descending order according to the value size;
wherein i and j are variables; if there are M planes f (x, y), then the value of i is 1-M; if the number of all points on a certain plane f (x, y) is N, the value of j is 1 to N;
and 4, step 4: when m (i, 2)/m (i, 1) and m (i, 3)/m (i, 2) are both in the range of 0-1, a feature point E is searched p 。
Further, in step 1b, the semantic feature parameters include: first order characteristic line length parameter P 0 (ii) a Second order characteristic line length parameter P 1 (ii) a Three-level characteristic line length parameter P 2 (ii) a Parameter P of included angle between characteristic lines 3 。
Further, in step 1c, the features of the attachment surface of the implant are expressed as a multi-element group of geometric elements, semantic parameters, constraint relations and mapping relations;
implant faying surface feature F surface ＝{E surface ,P surface ,R surfacet ,F surface }，
Wherein E surface Represents a geometric element which is a function of,
P surface represents a parameter of a semantic meaning that,
R surface representing the constraint relationship between the geometric elements,
F surface representing a mapping relation between the geometric topology and the semantic parameters;
the method comprises the following specific steps:
E surface ＝{E p ,E c ,E s }，
P surface ＝{P 0 ,P 1 ,P 2 ,P 3 }，
R surface represents E surface And P surface The constraint relationship between the two groups of the first and the second,
F surfacet ＝{F 0 ,F 1 in which F 0 Representing a two-level mapping relationship, F 0 ＝{F 00 ,F 01 }，F 1 The representative semantic parameter mapping relationship is as follows:
F 00 ＝{X p →Y c ,|X p ∈E p, Y c ∈E c }；
F 01 ＝{Y c →E s ,|Y c ∈E c }；
F 1 ＝{90°＜α 1 ＜180°,90°＜α 2 ＜180°,u 1 ＝t 1 ,u 2 ＝t 2 ,l 1 ＝m 1 ,l 1 ＜l 2 ＜h 1 }；
wherein X p Is a feature point selected from E p ；Y c Is a characteristic line selected from E c 。
Further, the second step comprises:
step 2a: processing a directed graph structure of the characteristics of the contact surface of the implant;
and step 2b: processing the directed graph structure in the step 2a into an adjacency matrix;
and step 2c: the adjacency matrix is adjusted to generate a deformed implant structure, and the deformed implant structure is stored in the characteristic database for retrieval.
Further, in step 2a, the directed graph structure of the implant abutment surface feature is defined as G = (V, R), where V represents a graph vertex and R represents a graph edge;
V＝{x|x∈E p }
where x is a feature point from E p (ii) a The numbering order of the graph vertices is: from bottom to top and from left to right;
R＝{e|e∈E c1 ∪E c2 ∪E c3 }
e is a characteristic line from E c1 、E c2 And E c3 (ii) a Connecting to ordered feature pointsTo pair<i,j>Is a characteristic line e, ordered characteristic point pairs<i,j>From E p ；
The characteristic line e contains two properties: γ and v, which respectively represent the direction and length of the characteristic line e; when gamma is more than or equal to 0 and less than 2, determining the direction of the next-stage characteristic line by using gamma, and when gamma is more than 0 and less than 0.5, determining the direction of the next-stage characteristic line as the upper right direction; when gamma is more than 0.5 and less than 1, the direction of the next-stage characteristic line is the upper left direction; when gamma is more than 1 and less than 1.5, the direction of the next-level characteristic line is the lower left direction; when gamma is more than 1.5 and less than 2, the direction of the next-level characteristic line is the lower-right direction.
Further, in step 2b, the adjacency matrix includes: form information adjacency matrix M 1 And size information adjacency matrix M 2 ；M 1 Denotes γ, M in step 2a 2 Represents v in step 2 a;
further, the third step comprises:
step 3a: preliminarily determining a form information adjacency matrix M according to the number of the characteristic points of the binding surface of the implant 1 The size of (a);
and step 3b: adjacency matrix M using morphological information 1 Determining the overall shape of the bone fracture plate;
and step 3c: adjacency matrix M using size information 2 Determining whether the size of the bone plate meets the requirements;
and step 3d: according to the instantiation method, the adjacency matrix M is formed by adjusting the size information 2 The semantic parameters in (1) generate the required implant faying surface features.
The invention provides a method for quickly searching characteristics of an implant binding face based on a graph structure. The method starts from the processing of the characteristic structure of the binding surface of the implant, firstly, the characteristic of the binding surface of the implant is processed into a graph structure, then, the graph structure is represented by a form information adjacent matrix and a size information adjacent matrix respectively, and finally, the characteristic of the binding surface of the implant closest to the characteristic of the binding surface of the implant to be obtained is searched based on the adjacent matrixes, so that the customized design of the subsequent implant is facilitated. The method has the characteristics of rapidness, high efficiency and the like, can greatly improve the information integration efficiency, is convenient for characteristic retrieval, and has important significance for the design of customized implants.
The method has the advantages that the method for quickly searching the characteristics of the binding surface of the implant based on the graph structure is applied to the fields of medical orthopedic operations and medical equipment manufacturing, provides scientific basis for quickly searching and editing the characteristics of the implant, and has important significance for improving the design efficiency of the customized implant.
The method integrates the characteristics of the binding surface of the implant through the graph structure, avoids the problem of zero fragmentation of semantic parameters of the implant, has the characteristics of rapidness, high efficiency and the like, is convenient for characteristic retrieval, and has important significance for realizing rapid design of customized implants.
Drawings
FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a characteristic representation view of the abutment surface of the T-shaped bone plate;
FIG. 3 is a diagrammatic, pictorial, structural representation of a T-shaped bone plate;
FIG. 4 is a schematic view of a T-shaped bone plate in the direction of a characteristic line;
FIG. 5 is a modified T-shaped bone plate abutment matrix M 1 And M 2 Generating different characteristic structure schematic diagrams;
FIG. 6 is a schematic representation of the structural features and deformation of the L-shaped bone plate;
fig. 7 is a flow chart of a feature retrieval method.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a method for rapidly retrieving characteristics of an implant attachment surface based on a graph structure includes the following steps:
the method comprises the following steps: and acquiring the shape of the implant, and constructing the characteristics of the abutting surface of the implant according to the shape of the implant.
Step two: and processing the characteristics of the attaching surface of the implant into a directed graph structure, and constructing a characteristic database.
Step three: and (4) retrieving the characteristics of the attaching surface of the implant from the characteristic database, and constructing the personalized attaching surface of the implant.
Specifically, the first step comprises:
step 1a: the implant abutment face geometry is constructed according to the type of implant.
The geometry is composed of feature points, feature lines, and faces (i.e., faying faces).
The feature lines comprise internal level feature lines and boundary feature lines, wherein the internal level feature lines comprise primary feature lines, secondary feature lines and tertiary feature lines. For the convenience of feature retrieval, the feature line grades are defined as follows from high to low in sequence: a first-level characteristic line, a second-level characteristic line and a third-level characteristic line; the growth direction of the characteristic line is as follows: from high level to low level.
As shown in the T-shaped bone plate of fig. 2, the center line O 1 O 2 Is a first-level characteristic line, and a left-side second-level characteristic line O is branched from the lower end 1 A 1 And the right secondary characteristic line O 1 B 1 。O 1 A 1 And O 1 B 1 The length of the bone plate is used for controlling the width of the tail part of the bone plate. The upper end branches out a left secondary characteristic line O 2 A 2 And the right secondary characteristic line O 2 B 2 。O 2 A 2 Continuously branches out of a left tertiary characteristic line A 2 C 1 And the right tertiary characteristic line A 2 D 1 。O 2 B 2 Continuously branches out of a left tertiary characteristic line B 2 C 2 And the right tertiary characteristic line B 2 D 2 . The three-level characteristic lines are used for controlling the width of the bone fracture plate.
The T-shaped bone fracture plate binding surface set shape comprises: feature point set E p Set of characteristic lines E c And a bonding surface E s The elements are specifically as follows:
(1)E p representing a set of feature points, E p ＝{O 1 ,O 2 ,A 1 ,B 1 ,A 2 ,B 2 ,C 1 ,D 1 ,C 2 ,D 2 }；
The extraction method of these feature points is as follows:
step 1: to the implant abutment surface, point p i As a center, radius r d The area within the range can be approximately considered as a plane, which is denoted as f (x, y).
In this example, r d To a certain extent, represents the precision, r d The smaller the value, the higher the accuracy r d Typically 1mm or 2mm is chosen.
And f (x, y), in order to improve the extraction efficiency of the feature points, denoising the feature points. The invention uses an improved two-dimensional Gaussian function G (x, y, delta) d ) Wherein δ d The ideal value of the scale weighting factor is usually 1. Meanwhile, in order to realize fine adjustment of denoising effect, a disturbance constant delta is added s The concrete formula is as follows:
gaussian filtering is a linear smooth filtering and is widely applied to the noise reduction process of image processing. The gaussian filtering is a process of weighted average of the whole image, and the value of each pixel point is obtained by weighted average of the value of each pixel point and other pixel values in the neighborhood. In the gaussian filter function, the scale weight factor is used to control the denoising effect. In the invention, in order to realize fine adjustment of denoising effect, a disturbance constant delta is added s ，δ s Is taken to be [0,1]。
Step 2: calculating all the points p on the denoised plane f (x, y) j And p i The matrix of the cross-variance is denoted as cov (p) i )。
And step 3: the eigenvalues m (i, 1), m (i, 2), m (i, 3) of the covariance matrix are calculated and sorted in descending order according to the magnitude of the values.
Wherein i and j are both variables and are not constants; if the number of the planes is M, the value of i is 1-M; if the number of all points on a plane f (x, y) is N, then j takes on values from 1 to N.
Statistically, a covariance matrix is given, and the eigenvalue calculation is a mature existing calculation formula.
And 4, step 4: when m (i, 2)/m (i, 1) and m (i, 3)/m (i, 2) are both in the range of 0-1, a feature point is searched.
(2)E c Representing a set of characteristic lines, E c ＝{E c1 ,E c2 ,E c3 ,E c4 In which E c1 Representing a first-order characteristic line, E c2 Represents a secondary characteristic line, E c3 Representing a characteristic line of three levels, E c4 Representing boundary characteristic lines, typically by applying E p The characteristic points in (1) are connected. As shown in FIG. 2, wherein E c1 ＝{O 1 O 2 }，E c2 ＝{O 1 A 1 ,O 1 B 1 ,O 2 A 2 ,O 2 B 2 }，E c3 ＝{A 2 C 1 ,A 2 D 1 ,B 2 C 2 ,B 2 D 2 }，E c4 To connect A in sequence 1 ,B 1 ,D 2 ,C 2 ,D 1 ,C 1 ,A 1 I.e. boundary characteristic lines.
(3)E s Indicating the contact surface, generally at E p And E c On the basis of the surface reconstruction method (such as filling). The curved surface reconstruction method is a very mature curved surface construction method, and the following documents can be referred to:
he Kunjin, wang Lin, chen Zhengming, et al local area reconstruction and characterization based on CAD surface models [ J ] computer integrated manufacturing system, 2014,20 (10): 2360-2368.
Step 1b: and setting semantic feature parameters of the binding surface of the implant.
The semantic feature parameters include: first order characteristic line length parameter P 0 (ii) a Second order characteristic line length parameter P 1 (ii) a Three-level characteristic line length parameter P 2 (ii) a Parameter P of included angle between characteristic lines 3 。
The T-shaped bone plate shown in fig. 3 comprises the following elements:
(1)P 0 ＝{h 1 in which h is 1 Representing the first order characteristic line length.
(2)P 1 ＝{l 1 ,m 1 ,l 2 ,m 2 H, wherein l 1 、m 1 Respectively represents the length of the two-stage characteristic line of the left side and the right side of the tail part of the bone fracture plate, l 2 、m 2 Respectively represent the lengths of the secondary characteristic lines on the left side and the right side of the head of the bone fracture plate.
(3)P 2 ＝{u 1 ,t 1 ,u 2 ,t 2 }，u 1 、t 1 Respectively represent the length of the three-stage characteristic line u at the left side and the right side of the left branch of the head of the bone fracture plate 2 、t 2 Respectively represents the length of the left and right three-stage characteristic lines of the right branch of the head of the bone fracture plate.
(4)P 3 ＝{α 1 ,α 2 In which α is 1 Is an included angle alpha between a primary characteristic line and a secondary characteristic line on the left side of the head of the T-shaped bone fracture plate 2 Is the included angle between the first-level characteristic line and the right-side second-level characteristic line of the head of the T-shaped bone fracture plate.
Step 1c: and characterizing the abutting surface of the implant.
The features of the implant abutting surface are expressed as a multi-element group, including a multi-element group of geometric elements, semantic parameters, constraint relations and mapping relations.
Implant faying surface feature F surface ＝{E surface ,P surface ,R surfacet ,F surface In which E surface Represents a geometric element, P surface Representing a semantic parameter, R surface Representing a constrained relationship between geometric elements, F surface Representing the mapping relationship between the geometric topology and the semantic parameters. The method comprises the following specific steps:
(1)E surface ＝{E p ,E c ,E s }，
(2)P surface ＝{P 0 ,P 1 ,P 2 ,P 3 }
(3)R surface represents E surface And P surface The constraint relationship between them, mainly pointing, linear, and planarTopological constraint relationships between them.
The construction of such constraint relationships is common to curved surface construction technologies, and the following documents are specifically referred to:
[1] he Kunjin, wang Lin, chen Zhengming, et al local area reconstruction and characterization based on CAD surface models [ J ] computer integrated manufacturing system, 2014,20 (10): 2360-2368.
[2] Wang Lin, he Kunjin, chen Zhengming, et al, methods for designing bone plate seriation based on feature point mapping [ J ] CAD (computer aided design) and graphics, 2016,28 (9): 11.
(4)F surfacet ＝{F 0 ,F 1 In which F 0 Representing a two-level mapping relationship, F 0 ＝{F 00 ,F 01 }，F 1 Representing the mapping relationship of semantic parameters, which is specifically as follows:
F 00 ＝{X p →Y c ,|X p ∈E p, Y c ∈E c }；
F 01 ＝{Y c →E s ,|Y c ∈E c }；
F 1 ＝{90°＜α 1 ＜180°,90°＜α 2 ＜180°,u 1 ＝t 1 ,u 2 ＝t 2 ,l 1 ＝m 1 ,l 1 ＜l 2 ＜h 1 }；
wherein, X p Is a feature point selected from E p ；Y c Is a characteristic line selected from E c ；
The second step comprises the following steps:
step 2a: processing an implant directed graph structure;
as shown in fig. 3, the directed graph structure of the abutment surface feature of the implant is defined as G = (V, R), where V denotes a graph vertex and R denotes a graph edge.
The graph is a data structure composed of a vertex set (namely graph vertex) and a relation set (namely graph edge) between the vertices, and on the basis of the graph, the edge of the directed graph is directional, namely two connected vertices can only pass from one vertex to the other vertex according to the direction of the edge. The weighted directed graph is based on the directed graph, and the edges are endowed with weight information.
The method comprises the following specific steps:
(1)V＝{x|x∈E p }；
where x is a feature point from E p . The order of the numbering of the vertices is: from bottom to top and from left to right.
(2)R＝{e|e∈E c1 ∪E c2 ∪E c3 }；
E is a characteristic line from E c1 、E c2 And E c3 . Connecting to ordered pairs of characteristic points<i,j>Is a characteristic line e, ordered characteristic point pairs<i,j>From E p 。
The ordered pairs of feature points are ordered logarithms. A feature line starting at the feature point numbered i and ending at the feature point numbered j may be constructed from the ordered feature points < i, j >.
As shown in the T-shaped bone plate of figure 3,
O 1 O 2 corresponding ordered pairs of feature points are<1,2>。
O 1 A 1 Corresponding ordered pairs of feature points are<1,3>。
O 1 B 1 Corresponding ordered pairs of feature points are<1,4>。
O 2 A 2 Corresponding ordered pairs of feature points are<2,5>。
O 2 B 2 Corresponding ordered pairs of feature points are<2,6>。
A 2 C 1 Corresponding ordered pairs of feature points are<5,7>。
A 2 D 1 Corresponding ordered pairs of feature points are<5,8>。
B 2 D 2 Corresponding ordered pairs of feature points are<6,9>。
B 2 C 2 Corresponding ordered pairs of feature points are<6,10>。
The characteristic line e contains two properties: γ and v, respectively, indicate the direction and length of the characteristic line. Let the next-level characteristic line direction perpendicular to the first-level characteristic line and pointing to the right be the reference direction, and be initially defined as: γ =0. When rotated counterclockwise, the angle increases by 90 ° and γ increases by 0.5. When gamma is more than or equal to 0 and less than 2, the direction of the next-level characteristic line can be determined by using gamma, and the specific steps are as follows:
gamma is more than 0 and less than 0.5 in the right upper direction;
gamma is more than 0.5 and less than 1 in the upper left direction;
gamma is more than 1 and less than 1.5 in the left lower direction;
gamma is more than 1.5 and less than 2, and the right lower direction is;
and step 2b: and processing the directed graph structure in the step 2a into an adjacency matrix. The adjacency matrix includes: form information adjacency matrix M 1 And size information adjacency matrix M 2 。M 1 Denotes γ, M in step 2a 2 V in step 2 a.
As shown in fig. 4, under the condition that the direction of the primary characteristic line is not changed, the direction of the secondary characteristic line directly determines the shapes of the left side and the right side of the T-shaped plate. The semantic feature parameter α shown in conjunction with FIG. 2 1 And alpha 2 The expression of γ is as follows:
M 1 and M 2 Is represented as follows:
and step 2c: the adjacency matrix is adjusted, and the deformed implant structure is generated and stored in the characteristic database for retrieval.
The data feature library storage is a graph structure feature and is stored in a form of a contiguous matrix.
As shown in fig. 5, by adjusting M 1 And M 2 Changing the semantic features of T-shaped bone platesAnd (5) characterizing parameters to realize the deformation of the implant structure.
In addition, as shown in fig. 6, taking the left L-shaped bone plate as an example, the central line of the tail portion is taken as a primary characteristic line, and the lower end branch forms a left secondary characteristic line and a right secondary characteristic line which define the width of the tail portion. In the head, the central line of the left branch is taken as a secondary characteristic line, and the right branch and the left branch are on the same straight line but opposite in direction. The secondary characteristic line is branched into a left tertiary characteristic line and a right tertiary characteristic line which are used for controlling the width of the left branch. Form adjacency matrix M of L-shaped bone fracture plate 3 And size adjacency matrix M 4 The definition is as follows:
by adjusting M 3 And M 4 The semantic characteristic parameters of the L-shaped bone fracture plate are changed, and the deformation of the implant structure can be realized.
The third step comprises:
as shown in fig. 7, step 3a: and preliminarily determining the size of the form information adjacency matrix according to the number of the characteristic points of the attachment surface of the implant. For example, a T-bone plate has 10 feature points, and its morphological information adjacency matrix is 10 × 10. Next, taking a T-shaped bone plate as an example, the following is further retrieved:
and step 3b: adjacency matrix M using morphological information 1 The overall shape of the bone plate is determined.
According to M 1 The key element gamma in the T-shaped bone fracture plate determines the overall shape of the T-shaped bone fracture plate. Calculating gamma error by delta 1 And (4) showing. If delta 1 Less than or equal to 0.005, the structural characteristics can be directly selected. If this condition is not met, then adjust α 1 And alpha 2 And placing the newly generated features into a feature database.
And 3c: adjacency matrix M using size information 2 It is determined whether the size of the bone plate is satisfactory.
Comparison M 2 Middle h 1 The error value of (D) is recorded as delta 2 . When delta 2 When the size is less than or equal to 0.1, the T-shaped plate has the requirement of Fu Gegu approximately and can be used as a characteristic structure. If the condition is not satisfied, h can be adjusted 1 And placing the newly generated features into a feature database.
And step 3d: according to the instantiation method, the required implant fitting face features are generated by adjusting the remaining semantic parameters in the size information adjacency matrix.
The instantiation method comprises the following steps: the process of generating models of different sizes is done by setting the parameterized model to different parameters.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or groups of devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. Modules or units or groups in embodiments may be combined into one module or unit or group and may furthermore be divided into sub-modules or sub-units or sub-groups. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention according to instructions in said program code stored in the memory.
By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer readable media includes both computer storage media and communication media. Computer storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (10)
1. The method for quickly searching the characteristics of the binding surface of the implant based on the graph structure is characterized by comprising the following steps of:
the method comprises the following steps: collecting the shape of the implant, and constructing the characteristics of the joint surface of the implant according to the shape of the implant;
step two: processing the characteristics of the binding surface of the implant into a directed graph structure, and constructing a characteristic database;
step three: and retrieving the characteristics of the attaching surface of the implant from the characteristic database to construct a personalized attaching surface of the implant.
2. The method for rapidly retrieving characteristics of the abutment surface of the implant based on the graph structure as claimed in claim 1, wherein the first step comprises:
step 1a: constructing the geometry of the abutting surface of the implant according to the type of the implant;
step 1b: setting semantic feature parameters of the binding surface of the implant;
step 1c: and characterizing the abutting surface of the implant.
3. The method for rapidly retrieving characteristics of fit surface of implant based on graph structure as claimed in claim 2, wherein in step 1a, said geometric shape is represented by a characteristic point E p And characteristic line E c Kneading surface E s Forming;
characteristic line E c Including inner level feature lines and boundary feature lines E c4 ；
The grade of the internal grade characteristic line sequentially comprises a first grade characteristic line E from high to low c1 Second level characteristic line E c2 And a third level characteristic line E c3 ；
The growth direction of the internal grade characteristic line is as follows: from high level to low level.
4. The method for rapidly retrieving characteristics of the attachment surface of the implant based on the graph structure as claimed in claim 2 or 3, wherein the characteristic point E is p The extraction method comprises the following steps:
step 1: facing surface E of the implant s At point p i As a center, radius r d The area within the range is considered as a plane, denoted as f (x, y);
at f (x, y), to increase the characteristic point E p Extracting efficiency, and carrying out denoising treatment on the plane f (x, y), wherein the denoising formula is as follows:
wherein, delta d Is a scale weight factor, and the ideal value is 1; delta s Is a perturbation constant having a value of [0,1]；
Step 2: calculating all the points p on the denoised plane f (x, y) j And point p i The matrix of the cross-variance is denoted as cov (p) i )；
And step 3: calculating eigenvalues m (i, 1), m (i, 2), m (i, 3) of the covariance matrix, and sorting the eigenvalues in descending order according to the value size;
wherein i and j are variables; if there are M planes f (x, y), then the value of i is 1-M; if the number of all points on a certain plane f (x, y) is N, the value of j is 1 to N;
and 4, step 4: when m (i, 2)/m (i, 1) and m (i, 3)/m (i, 2) are both in the range of 0-1, a feature point E is searched p 。
5. The method for rapidly retrieving characteristics of the attachment surface of the implant based on the graph structure as claimed in claim 3, wherein in step 1b, the semantic characteristic parameters comprise: first order characteristic line length parameter P 0 (ii) a Second order characteristic line length parameter P 1 (ii) a IIIStage characteristic line length parameter P 2 (ii) a Parameter P of included angle between characteristic lines 3 。
6. The graph structure-based rapid retrieval method for characteristics of the attachment surface of the implant of claim 3, wherein in step 1c, the characteristics of the attachment surface of the implant are represented as a multivariate group of geometric elements, semantic parameters, constraint relations and mapping relations;
implant faying surface feature F surface ＝{E surface ,P surface ,R surfacet ,F surface }，
Wherein E surface Represents a geometric element which is a function of,
P surface represents a parameter of a semantic meaning that,
R surface representing the constraint relationship between the geometric elements,
F surface representing the mapping relation between the geometric topology and the semantic parameters;
the method comprises the following specific steps:
E surface ＝{E p ,E c ,E s }，
P surface ＝{P 0 ,P 1 ,P 2 ,P 3 }，
R surface represents E surface And P surface The constraint relationship between the two groups of the first and the second,
F surfacet ＝{F 0 ,F 1 in which F 0 Representing a two-level mapping relationship, F 0 ＝{F 00 ,F 01 }，F 1 The representative semantic parameter mapping relationship is as follows:
F 00 ＝{X p →Y c ,|X p ∈E p, Y c ∈E c }；
F 01 ＝{Y c →E s ,|Y c ∈E c }；
F 1 ＝{90°＜α 1 ＜180°,90°＜α 2 ＜180°,u 1 ＝t 1 ,u 2 ＝t 2 ,l 1 ＝m 1 ,l 1 ＜l 2 ＜h 1 }；
wherein, X p Is a feature point selected from E p ；Y c Is a characteristic line selected from E c 。
7. The method for rapidly retrieving characteristics of the attachment surface of the implant based on the graph structure as claimed in claim 1, wherein the second step comprises:
step 2a: processing a directed graph structure of the characteristics of the contact surface of the implant;
and step 2b: processing the directed graph structure in the step 2a into an adjacency matrix;
and step 2c: the adjacency matrix is adjusted, and the deformed implant structure is generated and stored in the characteristic database for retrieval.
8. The method for rapidly retrieving an implant fitting surface feature based on a graph structure according to claim 7, wherein in step 2a, the directed graph structure of the implant fitting surface feature is defined as G = (V, R), where V represents a graph vertex and R represents a graph edge;
V＝{x|x∈E p }
where x is a feature point from E p (ii) a The numbering order of the graph vertices is: from bottom to top and from left to right;
R＝{e|e∈E c1 ∪E c2 ∪E c3 }
e is a characteristic line from E c1 、E c2 And E c3 (ii) a Connecting to ordered pairs of characteristic points<i,j>Is a characteristic line e, ordered characteristic point pairs<i,j>From E p ；
The characteristic line e contains two properties: γ and v, which respectively represent the direction and length of the characteristic line e; when gamma is more than or equal to 0 and less than 2, determining the direction of the next-stage characteristic line by using gamma, and when gamma is more than 0 and less than 0.5, determining the direction of the next-stage characteristic line as the upper right direction; when gamma is more than 0.5 and less than 1, the direction of the next-stage characteristic line is the upper left direction; when gamma is more than 1 and less than 1.5, the direction of the next-level characteristic line is the lower left direction; when gamma is more than 1.5 and less than 2, the direction of the next-level characteristic line is the lower-right direction.
9. The method for rapidly retrieving characteristics of a fitting surface of an implant according to claim 8, wherein in the step 2b, the adjacency matrix comprises: form information adjacency matrix M 1 And size information adjacency matrix M 2 ；M 1 Denotes γ, M in step 2a 2 Represents v in step 2 a;
10. the method for rapidly retrieving characteristics of the attachment surface of the implant based on the graph structure as claimed in claim 1, wherein the third step comprises:
step 3a: preliminarily determining a form information adjacency matrix M according to the number of the feature points of the binding surface of the implant 1 The size of (a);
and step 3b: adjacency matrix M using morphological information 1 Determining the overall shape of the bone fracture plate;
and step 3c: adjacency matrix M using size information 2 Determining whether the size of the bone plate meets the requirements;
and step 3d: according to the instantiation method, the adjacency matrix M is formed by adjusting the size information 2 The semantic parameters in (1) generate the required implant faying surface features.
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