CN114898036A - Liver blood vessel model generation method based on exploration operator - Google Patents
Liver blood vessel model generation method based on exploration operator Download PDFInfo
- Publication number
- CN114898036A CN114898036A CN202210443152.3A CN202210443152A CN114898036A CN 114898036 A CN114898036 A CN 114898036A CN 202210443152 A CN202210443152 A CN 202210443152A CN 114898036 A CN114898036 A CN 114898036A
- Authority
- CN
- China
- Prior art keywords
- blood vessel
- operator
- hepatic
- exploration
- initial point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 210000004204 blood vessel Anatomy 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 27
- 210000004185 liver Anatomy 0.000 title description 6
- 230000002440 hepatic effect Effects 0.000 claims abstract description 27
- 238000012800 visualization Methods 0.000 claims abstract description 7
- 238000012952 Resampling Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 13
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 2
- 230000003993 interaction Effects 0.000 abstract description 5
- 230000033115 angiogenesis Effects 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 abstract description 2
- 230000002792 vascular Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000004195 computer-aided diagnosis Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Abstract
The invention relates to the technical field of data processing, in particular to a hepatic blood vessel model generation method based on an exploration operator, which comprises the steps of establishing original three-dimensional data; giving an initial point of a blood vessel, and simultaneously initializing basic attributes of an exploration operator to obtain an initialization operator; the initialization operator is explored in the blood vessel based on an initial point to obtain position information; fitting the position information by using a spline to obtain a similar central line; resampling points on the class center line to obtain an accurate center line; visualization is carried out based on the accurate central line and in combination with threshold information to obtain a visualized blood vessel; based on a visual angiogenesis hepatic vessel model, the full-automatic reconstruction of hepatic vessels is completed through exploration operator heuristics, and the problems that the existing scheme for extracting the central line of the vessel tree depends on user interaction and the labor cost is too high are solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a hepatic blood vessel model generation method based on an exploration operator.
Background
The reconstruction technology of blood vessels is the key of the creation of blood vessel models in medicine, and the production of liver blood vessel models is greatly helpful for clinical diagnosis and operation planning.
At present, the prior art discloses a scheme for extracting the center line of a blood vessel tree, in the scheme, a user interactively selects and calculates the center line of each branch and finally combines the center lines to obtain a complete center line, a blood vessel is reconstructed through the center line, and finally a hepatic blood vessel model is produced.
By adopting the mode, the vessel wall is required to be obtained firstly, a section of vessel without branches can be found, the coordinate point needs to be extracted manually, the user interaction is strongly depended on, and the labor cost is too high.
Disclosure of Invention
The invention aims to provide a hepatic vessel model generation method based on an exploration operator, and aims to solve the problems that the existing centerline extraction scheme of a vessel tree depends on user interaction and the labor cost is too high.
In order to achieve the above object, the present invention provides a hepatic blood vessel model generation method based on an exploration operator, comprising the following steps:
s1 creating original three-dimensional data;
s2, an initial point of a blood vessel is given, and basic attributes of an exploration operator are initialized at the same time to obtain an initialization operator;
s3 searching in the blood vessel by the initialization operator based on the initial point to obtain position information;
s4, fitting the position information by using a spline to obtain a similar center line;
s5, resampling points on the quasi-center line to obtain an accurate center line;
s6, performing visualization based on the accurate central line and by combining threshold information to obtain a visualized blood vessel;
s7 generating a hepatic blood vessel model based on the visualization.
The specific way of creating the original three-dimensional data is as follows:
s11 reads the original image data;
s12, carrying out format conversion on the original image data to obtain converted data serving as original three-dimensional data;
wherein, the initialization operator searches in the blood vessel based on the initial point, and the specific way of obtaining the position information is as follows:
s31, issuing a traveling instruction to the initialization operator based on the initial point;
s32, the initialization operator receives an instruction, and searches in the blood vessel according to the initialized step length based on the initial point to obtain a position point;
s33 updates the initial point in step S32 to the location point, and loops back to step S32 until the blood vessel is searched for, thereby obtaining location information.
The specific way of obtaining the similar center line by fitting the position information by using a spline is as follows:
s41 defines a B spline basis function;
s42, constructing a B spline curve function based on the B spline basis function;
and S43, fitting the position information based on the B spline curve function to obtain a similar center line.
The B-spline basis function is a k-order piecewise polynomial determined by a sequence of non-degressive parameters of a node vector, the sequence is the node vector, the node vector is obtained by a Hartley-Giardian method, and the sequence is the node vector.
The invention relates to a hepatic vascular model generation method based on an exploration operator, which comprises the steps of establishing original three-dimensional data to obtain spatial information conforming to the advancing of the exploration operator; giving an initial point of a blood vessel, and simultaneously initializing basic attributes of an exploration operator to obtain an initialization operator; the initialization operator searches in the blood vessel based on the initial point to obtain position information; fitting the position information by using a spline to obtain a similar central line; resampling points on the quasi-center line to obtain an accurate center line; visualizing based on the accurate central line and in combination with threshold information to obtain a visualized blood vessel; based on the visual angiogenesis hepatic vessel model, the full-automatic reconstruction of hepatic vessels is completed through exploration operator heuristics, and the problems that the existing scheme for extracting the center line of the vessel tree depends on user interaction and the labor cost is too high are solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for generating a hepatic blood vessel model based on an exploration operator according to the present invention.
Fig. 2 is a schematic diagram of location information.
Fig. 3 is a schematic illustration of a class centerline.
Fig. 4 is a schematic view of a precise centerline.
Fig. 5 is a schematic view of visualizing a blood vessel.
Fig. 6 is a schematic diagram of a hepatic blood vessel model of an embodiment.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
Referring to fig. 1 to 5, the present invention provides a method for generating a hepatic vascular model based on an exploration operator, comprising the following steps:
s1 creating original three-dimensional data;
the concrete mode is as follows:
s11 reads the original image data;
specifically, the raw image data is raw CT image data of hepatic vessels.
S12, carrying out format conversion on the original image data to obtain converted data serving as original three-dimensional data;
specifically, data with different formats are uniformly converted into required NRRD arrays to obtain converted data.
S2, an initial point of a blood vessel is given, and basic attributes of an exploration operator are initialized at the same time to obtain an initialization operator;
s3 searching in the blood vessel by the initialization operator based on the initial point to obtain position information;
the concrete mode is as follows:
s31, issuing a traveling instruction to the initialization operator based on the initial point;
s32, the initialization operator receives an instruction, and searches in the blood vessel according to the initialized step length based on the initial point to obtain a position point;
specifically, the initialization operator starts to explore in different directions according to the initialization step length until the initialization operator is explored to a certain direction, and the initialization operator is still in the blood vessel when the initialization operator moves to the next step along the certain direction, so that the position is considered to be appropriate, a position point is obtained, and the next step is executed.
S33 updates the initial point in step S32 to the location point, and loops step S32 until the blood vessel is searched for, thereby obtaining location information.
S4, fitting the position information by using a spline to obtain a similar center line;
the concrete method is as follows:
s41 defines a B spline basis function;
in particular, the B-spline basis function is a piecewise polynomial of order k determined by a sequence of non-decreasing parameters t, called a node vector. The number of node vectors is m + 1. Wherein m is n + k +1, k is the number of splines, n +1 is the number of control points, the node vector is obtained by a Hartley-Giardia method, and the sequence is a node vector.
B spline basis function
Wherein k represents the number of basis functions;
s42, constructing a B spline curve function based on the B spline basis function;
specifically, with the B-spline basis function defined above, a representation of a B-spline curve can be derived as follows:
the node vectors are obtained by a Hartley-Judd (Hartley-giard) method, and 3-order B-splines are used in the design to fit the control points, so that k is 3 in the representation of the B-spline curve.
And S43, fitting the position information based on the B spline curve function to obtain a similar center line.
S5, resampling points on the quasi-center line to obtain an accurate center line;
s6 visualization is carried out on the basis of the accurate central line and in combination with threshold information to obtain a visualized blood vessel;
s7 generating a hepatic blood vessel model based on the visualization.
The implementation case is as follows:
using the raw DICOM data as input data for the present invention, the final reconstructed hepatic blood vessel model results are shown in fig. 6.
The following is a detailed description of the automated nature of the exploration operator:
operator traveling direction adaptation: the invention provides a plurality of advancing directions for the exploring operator, the exploring in different directions is carried out in the blood vessel according to a certain rule when advancing each time, and the advancing direction of the next time is determined according to the priority of the direction, thereby realizing the automation.
Operator position threshold adaptation: for the purposes of the present invention, a threshold is used to represent a limit to limit the movement of an exploration operator. Once the blood vessel is not within the threshold range at some point in the progression, the different conditions encountered are then automatically processed accordingly.
The threshold is an important condition for judging whether the exploration operator goes out of the blood vessel in the advancing process, and the method can obtain a more accurate result by filtering the original CT image and obtain the threshold range of the next advancing to realize automatic threshold acquisition.
Self-adaptation of operator traveling step length: the basic conditions required for the movement of the exploration operator are direction and step size, the initial step size is manually given, and each subsequent step of movement automatically obtains a proper step size.
The invention introduces the concept of an exploration operator, substantially endows a three-dimensional point with information such as direction, step length, threshold value of the three-dimensional point in a blood vessel and the like, and combines the thought of a three-dimensional maze, so that the three-dimensional point can quickly and accurately find the blood vessel in a read medical image, point cloud data is obtained through the method, and accurate blood vessel reconstruction is finally realized through further processing of the point cloud data.
The invention aims to distribute a large amount of labor-consuming work to a computer in the complex process of blood vessel reconstruction, realize the automation of blood vessel reconstruction to the maximum extent, obtain a faster and more accurate reconstruction result in the shortest time and further provide guarantee for the authenticity and the accuracy of a liver blood vessel model. The invention has fast reconstruction speed and high precision, and provides important reference value for liver surgery through the obtained liver internal blood vessel model.
The method is characterized in that a self-defined exploration operator is used for exploring the inner cavity of the blood vessel to obtain the blood vessel sparse point cloud, the point cloud is fitted into a curve by using a B-spline, accurate key points of the blood vessel are obtained by resampling, and finally, full-automatic blood vessel reconstruction is rapidly realized on the given accurate points and threshold values. Compared with the traditional technology of manually reconstructing blood vessels, the method uses the exploration operator as an automatic reconstruction basis, greatly reduces manual interaction and reconstruction time, and improves reconstruction precision and efficiency.
The invention can complete the full-automatic reconstruction of hepatic vessels by exploring operator, and can bring remarkable effect for computer-aided diagnosis of hepatic vessel diseases through an accurate hepatic vessel model.
While the invention has been described with reference to a preferred embodiment for a method for generating a hepatic vascular model based on search operators, it will be understood by those skilled in the art that the invention is not limited thereto, and that all or a portion of the process flow for implementing the embodiment described above may be equally varied, and still fall within the scope of the invention.
Claims (5)
1. A hepatic blood vessel model generation method based on an exploration operator is characterized by comprising the following steps:
s1 creating original three-dimensional data;
s2, an initial point of a blood vessel is given, and basic attributes of an exploration operator are initialized at the same time to obtain an initialization operator;
s3 searching in the blood vessel by the initialization operator based on the initial point to obtain position information;
s4, fitting the position information by using a spline to obtain a similar center line;
s5, resampling points on the quasi-center line to obtain an accurate center line;
s6 visualization is carried out on the basis of the accurate central line and in combination with threshold information to obtain a visualized blood vessel;
s7 generating a hepatic blood vessel model based on the visualization.
2. The method of generating a hepatic vessel model based on an exploration operator according to claim 1,
the specific way of creating the original three-dimensional data is as follows:
s11 reads the original image data;
s12 performs format conversion on the original image data to obtain converted data as original three-dimensional data.
3. The method of generating a hepatic vessel model based on an exploration operator according to claim 1,
the initialization operator searches in the blood vessel based on the initial point, and the specific way of obtaining the position information is as follows:
s31, issuing a traveling instruction to the initialization operator based on the initial point;
s32, the initialization operator receives an instruction, and searches in the blood vessel according to the initialized step length based on the initial point to obtain a position point;
s33 updates the initial point in step S32 to the location point, and loops back to step S32 until the blood vessel is searched for, thereby obtaining location information.
4. The method of generating a hepatic vessel model based on an exploration operator according to claim 1,
the specific way of fitting the position information by using splines to obtain the similar center line is as follows:
s41 defines a B spline basis function;
s42, constructing a B spline curve function based on the B spline basis function;
and S43, fitting the position information based on the B spline curve function to obtain a similar center line.
5. The method of generating a hepatic vessel model based on an exploration operator according to claim 4,
the B-spline basis function is a k-order piecewise polynomial determined by a sequence of non-decreasing parameters of a node vector, the sequence is the node vector, the node vector is obtained by a Hartley-Giardian method, and the sequence is the node vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210443152.3A CN114898036A (en) | 2022-04-26 | 2022-04-26 | Liver blood vessel model generation method based on exploration operator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210443152.3A CN114898036A (en) | 2022-04-26 | 2022-04-26 | Liver blood vessel model generation method based on exploration operator |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114898036A true CN114898036A (en) | 2022-08-12 |
Family
ID=82717655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210443152.3A Pending CN114898036A (en) | 2022-04-26 | 2022-04-26 | Liver blood vessel model generation method based on exploration operator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114898036A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184567A (en) * | 2011-05-04 | 2011-09-14 | 北京师范大学 | Method for constructing three-dimensional blood vessel model based on ball B-spline curve |
| CN104835112A (en) * | 2015-05-07 | 2015-08-12 | 厦门大学 | Liver multi-phase CT image fusion method |
| US20170323587A1 (en) * | 2014-10-08 | 2017-11-09 | EBM Corporation | Blood-vessel-shape construction device for blood-flow simulation, method therefor, and computer software program |
| CN112419276A (en) * | 2020-11-25 | 2021-02-26 | 苏州润迈德医疗科技有限公司 | Method for regulating blood vessel contour and central line and storage medium |
| CN113538618A (en) * | 2021-08-30 | 2021-10-22 | 复旦大学附属中山医院 | Rapid curved surface reconstruction method and storage medium for multi-modal multi-contrast medical image |
-
2022
- 2022-04-26 CN CN202210443152.3A patent/CN114898036A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184567A (en) * | 2011-05-04 | 2011-09-14 | 北京师范大学 | Method for constructing three-dimensional blood vessel model based on ball B-spline curve |
| US20170323587A1 (en) * | 2014-10-08 | 2017-11-09 | EBM Corporation | Blood-vessel-shape construction device for blood-flow simulation, method therefor, and computer software program |
| CN104835112A (en) * | 2015-05-07 | 2015-08-12 | 厦门大学 | Liver multi-phase CT image fusion method |
| CN112419276A (en) * | 2020-11-25 | 2021-02-26 | 苏州润迈德医疗科技有限公司 | Method for regulating blood vessel contour and central line and storage medium |
| CN113538618A (en) * | 2021-08-30 | 2021-10-22 | 复旦大学附属中山医院 | Rapid curved surface reconstruction method and storage medium for multi-modal multi-contrast medical image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109559326B (en) | Hemodynamic parameter calculation method and system and electronic device | |
| CN102184567B (en) | Method for constructing three-dimensional blood vessel model based on ball B-spline curve | |
| US9824492B2 (en) | Hollow object model visualization in medical images | |
| CN112884826B (en) | Method and device for extracting center line of blood vessel | |
| JPH0883296A (en) | Method and device for preparing three-dimensional shape preparing | |
| WO2022148415A1 (en) | Coronary vessel segmentation method and apparatus, device, and computer readable storage medium | |
| Livesu et al. | Explicit cylindrical maps for general tubular shapes | |
| CN104050720A (en) | Point cloud curve plane reconstruction method and system | |
| CN113160189A (en) | Blood vessel center line extraction method, device, equipment and storage medium | |
| CN111159872B (en) | Three-dimensional assembly process teaching method and system based on human-machine engineering simulation analysis | |
| CN112906106A (en) | Method for establishing parameterized TBM shield tunnel model | |
| CN114491718B (en) | Geological profile multi-segment line optimization method and system for finite element analysis | |
| CN114898036A (en) | Liver blood vessel model generation method based on exploration operator | |
| CN103136788B (en) | The visual method for reconstructing of a kind of three-dimensional blood vessel bifurcation | |
| CN113902715A (en) | Method, apparatus and medium for extracting vessel centerline | |
| CN114882172B (en) | Quick generation method of vascular teaching aid based on bounding box automatic advancing | |
| Ma et al. | Real-time skeletonization for sketch-based modeling | |
| CN113628346B (en) | Method for freely browsing VB and method and system for marking VB | |
| CN114119620A (en) | Skeleton line extraction method suitable for complex multi-cavity three-dimensional model | |
| CN113536408B (en) | Residential core tube area calculation method based on CAD external reference collaborative mode | |
| CN114547716A (en) | Multi-degree-of-freedom 3D printing curved surface slice and path planning method | |
| CN108921950B (en) | Method for three-dimensional fracture simulation and related device | |
| CN114283261A (en) | Path planning method of virtual endoscope and related product | |
| Durikovic et al. | Animation of Biological Organ Growth Based on L‐systems | |
| CN115422856B (en) | CFD teaching-oriented teaching blood vessel model generation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220812 |
|
| RJ01 | Rejection of invention patent application after publication |