CN106875376A - The construction method and lumbar vertebrae method for registering of lumbar vertebrae registration prior model - Google Patents
The construction method and lumbar vertebrae method for registering of lumbar vertebrae registration prior model Download PDFInfo
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Abstract
The present invention relates to the construction method and lumbar vertebrae method for registering of a kind of lumbar vertebrae registration prior model.The present invention carries out DRR projections by substantial amounts of lumbar vertebrae CT samples, obtains projected image sample, and to projected image sample extraction characteristic point, and then build the prior model of lumbar vertebrae registration.Lumbar vertebrae is being carried out with punctual, the lumbar vertebrae CT images of patient is first being obtained before surgery, and using the correlation of height between shape and projective parameter, set up attitude mode；After the form parameter for obtaining lumbar vertebrae by X ray images in the operation, its projective parameter directly can be obtained by attitude mode, so as to complete registration.Avoid and search for a projected image for best match degree in conventional method in substantial amounts of projected image so that the present invention can efficiently carry out the 3D/2D registrations of lumbar vertebrae, while precision is ensured, meet the requirement of high real-time in operation.
Description
Technical field
The present invention relates to image processing field, and in particular to a kind of construction method and lumbar vertebrae of lumbar vertebrae registration prior model
Method for registering.
Background technology
Doctor in spinal surgical procedures are carried out, it is necessary to image guide, so as to understand the position and attitude and hand of vertebra
Art inserts the particular location of thing (such as screw).Yet with CT scan (Computed in art
Tomography, CT) apparatus expensive, only a small number of hospital's configurations, therefore X-ray (X-ray) image comes in conventional preoperative CT and art
Auxiliary surgical.X-ray can provide bidimensional image in art, to obtain three-dimensional information, it is necessary to by X-ray in art and preoperative CT
Carry out registration, therefore the image registration of 3D/2D is key issue in image-guided surgery.
Current 3D/2D registrations are usually, by the data projection of 3D to 2D planes, then to carry out 2D/2D registrations, are finally existed
An optimal projective parameter is found in projective parameter space and enables projected image and target image optimal registration.But by
It is higher in projective parameter space complexity, so this 3D/2D method for registering amounts of calculation based on search strategy are huge.Also have
Processing method needs more manual intervention, again results in inefficiency.
The content of the invention
In order to solve above mentioned problem of the prior art, the present invention proposes a kind of structure side of lumbar vertebrae registration prior model
Method and lumbar vertebrae method for registering, while precision is ensured, substantially increase the efficiency of registration, and reach in art registration in real time will
Ask.
The present invention proposes a kind of construction method and lumbar vertebrae method for registering of lumbar vertebrae registration prior model, including following step
Suddenly：
Step A1, the lumbar vertebrae CT image patterns to predetermined number carry out digital reconstruction irradiation image (Digitally
Reconstructured Radiograph, DRR) projection, projected image sample is obtained, and to projected image sample extraction feature
Point；
Step A2, statistical shape model is set up according to the characteristic point that step A1 is extracted；
Step A3, sets up statistics gray level model；
Step A4, shape parameter and gray level model parameter are together in series and set up conjunctive model.
Preferably, step A2 is specially：
The characteristic point of extraction is mapped under a common two-dimensional coordinate system using Pu Shi analytic approach so that each perspective view
The center of gravity of decent eigen point overlaps with the origin of coordinates, and eliminates translation, scaling, rotation to different projected image sample characteristics points
Influence；Characteristic point to being mapped under common two-dimensional coordinate system utilizes PCA (principal components
Analysis, PCA) obtain the orthonormal basis P of shapes, then the characteristic point shape of each projected image sample be：
Wherein,It is average shape, the average shape is the mean place of each characteristic point, bsFor shape is joined
Number.
Preferably, step A3 is specially：
Each projected image sample is carried out shape normalised so that its characteristic point is deformed in average shape；By shape
The standardized image of shape, the region covered in its shape is sampled, and sampled point is normalized makes its gray scale
Average is 0, and variance is 1；Apparent model P is obtained using PCAg, then each projected image sample characteristics point gray level model
G is：
Wherein,It is average gray, PgIt is the orthonormal basis of gray level model, bgIt is gray level model parameter.
Preferably, step A4 is specially：
Step A41, by shape parameter bsWith gray level model parameter bgIt is together in series,
Wherein, WsIt is a diagonal matrix, for equilibrium configuration model and the dimension of gray level model parameter；
Step A42, shape and gray level model parameter to connecting obtain conjunctive model using PCA:
Because the average of shape parameter and gray level model parameter is 0, so averageIt is 0, then has：
B=Qc,
Wherein, c is the parameter of conjunctive model, and Q is the orthogonal basis of conjunctive model, QsIt is the orthogonal basis of shape, QgIt is ash
Spend the orthogonal basis of model；
Step A43, shape x and gray scale g is expressed with conjunctive model parameter c：
Wherein,It is average shape, PsIt is the orthonormal basis of shape,It is average gray, PgIt is gray level model
Orthonormal basis.
Preferably, when carrying out shape normalised to each projected image sample, using triangle deformation algorithm.
The present invention proposes a kind of lumbar vertebrae method for registering simultaneously, comprises the following steps：
Step B1, operation consent obtains target lumbar vertebrae CT images, and the DRR of DRR projection generation predetermined numbers is carried out to CT images
Image；
Step B2, to every width DRR images, according to default initial position and lumbar vertebrae registration prior model, to target lumbar vertebrae
Split and extracted form parameter.
Step B3, the projective parameter form parameter corresponding with its according to DRR images, sets up attitude mode；
Step B4, obtains X-ray images in surgical procedure, right according to lumbar vertebrae registration prior model on X-ray images
Target lumbar vertebrae is split, and extracts form parameter；
Step B5, according to the attitude obtained in the form parameter and step B3 of the target lumbar vertebrae obtained on X-ray images
Model, obtains corresponding projective parameter, so as to complete registration.
Preferably, attitude mode is set up described in step B3, specially：
Using linear model R=MB, attitude mode is obtained：
M=RBT(BBT)-1。
Wherein, R is projective parameter, and B is form parameter, and M is attitude mode.
Preferably, step B5 is specially：
According to the form parameter obtained in the attitude mode and step B4, the corresponding projection ginseng of X-ray images is calculated
Number：
RX-ray=MBX-ray,
Wherein BX-rayIt is the form parameter of the X-ray images of extraction in step B4, M is attitude mode.
Preferably, in step B2 and step B4, split to target lumbar vertebrae and extracted form parameter with AAM algorithms.
The present invention sets up the Two-dimensional Statistical model of lumbar vertebrae, and using the correlation of height between shape and projective parameter
Property, learn an attitude mode.After the form parameter that lumbar vertebrae is obtained in the operation, directly can be obtained by attitude mode
To its projective parameter, a perspective view for best match degree is searched in substantial amounts of projected image in conventional method so as to avoid
Picture so that the present invention can efficiently carry out the 3D/2D registrations of lumbar vertebrae, while precision is ensured, meet high real-time in art
Requirement.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of lumbar vertebrae registration prior model construction method in the present embodiment；
Fig. 2 is the schematic diagram for carrying out feature point extraction in the present embodiment to DRR projected images；
Fig. 3 is the schematic flow sheet of lumbar vertebrae method for registering in the present embodiment.
Specific embodiment
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little implementation methods are used only for explaining know-why of the invention, it is not intended that limit the scope of the invention.
In the present embodiment, the PC of Intel (R) Core (TM) [email protected], 4G internal memories is configured at one
On machine, algorithm platform Matlab R2014b are run.
The present invention proposes a kind of construction method and lumbar vertebrae method for registering of lumbar vertebrae registration prior model, as shown in figure 1, bag
Include following steps：
Step A1, the lumbar vertebrae CT image patterns to predetermined number carry out DRR projections, obtain projected image sample, and to throwing
Shadow image pattern extracts characteristic point；
Step A2, statistical shape model is set up according to the characteristic point that step A1 is extracted；
Step A3, sets up statistics gray level model；
Step A4, shape parameter and gray level model parameter are together in series and set up conjunctive model.
In the present embodiment, when statistical shape model is set up, the model that selection is set up on two dimensional image, because two dimensional image
Characteristic point be easier to mark and obtain.As shown in Fig. 2 93 characteristic points are extracted on every width DRR images, global shape such as son
Shown in figure a, 6 parts are broadly divided into, are respectively：Centrum profile (as shown in subgraph b), central gray scale depression are (such as subgraph c institutes
Show), close to the pedicle of vertebral arch (as shown in subgraph d and e) of physiological structure and centrum or so undercut angles (as shown in subgraph f and g).
In the present embodiment, step A2 is specially：
The characteristic point of extraction is mapped under a common two-dimensional coordinate system using Pu Shi analytic approach so that each perspective view
The center of gravity of decent eigen point overlaps with the origin of coordinates, and eliminates translation, scaling, rotation to different projected image sample characteristics points
Influence；Characteristic point to being mapped under common two-dimensional coordinate system obtains the normal orthogonal of shape using PCA
Base Ps, then shown in the characteristic point shape of each projected image sample, such as formula (1)：
Wherein,It is average shape, the average shape is the mean place of each characteristic point, bsIt is shape parameter.
In the present embodiment, step A3 is specially：
Each projected image sample is carried out shape normalised so that its characteristic point is deformed in average shape；By shape
The standardized image of shape, the region covered in its shape is sampled, and sampled point is normalized makes its gray scale
Average is 0, and variance is 1；Apparent model P is obtained using PCAg, then each projected image sample characteristics point gray level model
Shown in g, such as formula (2)：
Wherein,It is average gray, PgIt is the orthonormal basis of gray level model, bgIt is gray level model parameter.
In the present embodiment, step A4 is specially：
Step A41, by shape parameter bsWith gray level model parameter bgIt is together in series, such as shown in formula (3)：
Wherein, WsIt is a diagonal matrix, for equilibrium configuration model and the dimension of gray level model parameter；
Step A42, shape and gray level model parameter to connecting obtain conjunctive model using PCA, such as
Shown in formula (4)：
Because the average of shape parameter and gray level model parameter is 0, so averageIt is 0, then such as formula (5) institute
Show：
B=Qc (5)
Wherein, Q is the orthogonal basis of conjunctive model, shown in Q values such as formula (6)：
QsIt is the orthogonal basis of shape, QgIt is the orthogonal basis of gray level model, c is the parameter of conjunctive model；
Step A43, shape x and gray scale g is expressed with conjunctive model parameter c, respectively as shown in formula (7) and (8)：
Wherein,It is average shape, PsIt is the orthonormal basis of shape,It is average gray, PgIt is gray level model
Orthonormal basis.
In the present embodiment, when carrying out shape normalised to each projected image sample, using triangle deformation algorithm.
The present invention proposes a kind of lumbar vertebrae method for registering simultaneously, and this method obtains the lumbar vertebrae CT figures for treating operation patients in the preoperative
Picture, and CT images are processed, to the segmentation in art and registering offer information.So so that more evaluation works in the preoperative
Carry out so that can be split in a more efficient manner in art and registration, so as to reach requirement of real-time.
As shown in figure 3, comprising the following steps：
Step B1, operation consent obtains target lumbar vertebrae CT images, and the DRR of DRR projection generation predetermined numbers is carried out to CT images
Image；
Step B2, to every width DRR images, according to default initial position and lumbar vertebrae registration prior model, to target lumbar vertebrae
Split and extracted form parameter.
Step B3, the projective parameter form parameter corresponding with its according to DRR images, sets up attitude mode；
Step B4, obtains X-ray images in surgical procedure, right according to lumbar vertebrae registration prior model on X-ray images
Target lumbar vertebrae is split, and extracts form parameter；
Step B5, according to the attitude obtained in the form parameter and step B3 of the target lumbar vertebrae obtained on X-ray images
Model, obtains corresponding projective parameter, so as to complete registration.
In the present embodiment, attitude mode is set up described in step B3, specially：
Using linear model R=MB, attitude mode is obtained, such as shown in formula (9)：
M=RBT(BBT)-1 (9)
Wherein, R is projective parameter, and B is form parameter, and M is attitude mode.
In the present embodiment, step B5 is specially：
According to the form parameter obtained in the attitude mode and step B4, the corresponding projection ginseng of X-ray images is calculated
Number, this projective parameter is the registration result of needs；Shown in computational methods such as formula (10)：
RX-ray=MBX-ray (10)
Wherein BX-rayIt is the form parameter of the X-ray images of extraction in step B4, M is attitude mode.
In the present embodiment, in step B2 and step B4, with AAM (Active Appearance Model) algorithm to target
Lumbar vertebrae is split and is extracted form parameter.
Those skilled in the art should be able to recognize that, the side of each example described with reference to the embodiments described herein
Method step, can be realized with electronic hardware, computer software or the combination of the two, in order to clearly demonstrate electronic hardware and
The interchangeability of software, generally describes the composition and step of each example according to function in the above description.These
Function is performed with electronic hardware or software mode actually, depending on the application-specific and design constraint of technical scheme.
Those skilled in the art can realize described function to each specific application using distinct methods, but this reality
Now it is not considered that beyond the scope of this invention.
So far, combined preferred embodiment shown in the drawings describes technical scheme, but, this area
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
On the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to correlation technique feature, these
Technical scheme after changing or replacing it is fallen within protection scope of the present invention.
Claims (9)
1. the construction method and lumbar vertebrae method for registering of a kind of lumbar vertebrae registration prior model, it is characterised in that comprise the following steps：
Step A1, the lumbar vertebrae CT image patterns to predetermined number carry out DRR projections, obtain projected image sample, and to perspective view
As sample extraction characteristic point；
Step A2, statistical shape model is set up according to the characteristic point that step A1 is extracted；
Step A3, sets up statistics gray level model；
Step A4, shape parameter and gray level model parameter are together in series and set up conjunctive model.
2. method according to claim 1, it is characterised in that step A2 is specially：
The characteristic point of extraction is mapped under a common two-dimensional coordinate system using Pu Shi analytic approach so that each perspective view is decent
The center of gravity of eigen point overlaps with the origin of coordinates, and eliminates translation, scaling, rotation to the shadow of different projected image sample characteristics points
Ring；Characteristic point to being mapped under common two-dimensional coordinate system obtains the orthonormal basis of shape using PCA
Ps, then the characteristic point shape of each projected image sample be：
Wherein,It is average shape, the average shape is the mean place of each characteristic point, bsIt is shape parameter.
3. method according to claim 2, it is characterised in that step A3 is specially：
Each projected image sample is carried out shape normalised so that its characteristic point is deformed in average shape；By shape mark
The image of standardization, the region covered in its shape is sampled, and sampled point is normalized makes its gray average
It is 0, variance is 1；Apparent model P is obtained using PCAg, then each projected image sample characteristics point gray level model g be：
Wherein,It is average gray, PgIt is the orthonormal basis of gray level model, bgIt is gray level model parameter.
4. method according to claim 3, it is characterised in that step A4 is specially：
Step A41, by shape parameter bsWith gray level model parameter bgIt is together in series,
Wherein, WsIt is a diagonal matrix, for equilibrium configuration model and the dimension of gray level model parameter；
Step A42, shape and gray level model parameter to connecting obtain conjunctive model using PCA:
Because the average of shape parameter and gray level model parameter is 0, so averageIt is 0, then has：
B=Qc,
Wherein, c is the parameter of conjunctive model, and Q is the orthogonal basis of conjunctive model, QsIt is the orthogonal basis of shape, QgIt is gray scale mould
The orthogonal basis of type；
Step A43, shape x and gray scale g is expressed with conjunctive model parameter c：
Wherein,It is average shape, PsIt is the orthonormal basis of shape,It is average gray, PgIt is the standard of gray level model
Orthogonal basis.
5. method according to claim 3, it is characterised in that when carrying out shape normalised to each projected image sample, adopt
Use triangle deformation algorithm.
6. a kind of lumbar vertebrae method for registering, it is characterised in that comprise the following steps：
Step B1, operation consent obtains target lumbar vertebrae CT images, and the DRR images of DRR projection generation predetermined numbers are carried out to CT images；
Step B2, to every width DRR images, method builds according to any one of default initial position and Claims 1 to 5
Lumbar vertebrae registration prior model, split and extracted form parameter to target lumbar vertebrae；
Step B3, the projective parameter form parameter corresponding with its according to DRR images, sets up attitude mode；
Step B4, obtains X-ray images in surgical procedure, on X-ray images, based on the registration priori of lumbar vertebrae described in step B2
Model, splits, and extract form parameter to target lumbar vertebrae；
Step B5, according to the attitude mode obtained in the form parameter and step B3 of the target lumbar vertebrae obtained on X-ray images,
Corresponding projective parameter is obtained, so as to complete registration.
7. method according to claim 6, it is characterised in that attitude mode is set up described in step B3, specially：
Using linear model R=MB, attitude mode is obtained：
M=RBT(BBT)-1,
Wherein, R is projective parameter, and B is form parameter, and M is attitude mode.
8. method according to claim 7, it is characterised in that step B5 is specially：
According to the form parameter obtained in the attitude mode and step B4, the corresponding projective parameter of X-ray images is calculated：
RX-ray=MBX-ray；
Wherein, BX-rayIt is the form parameter of the X-ray images of extraction in step B4, M is attitude mode.
9. method according to claim 6, it is characterised in that in step B2 and step B4, with AAM algorithms to target lumbar vertebrae
Split and extracted form parameter.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108846830A (en) * | 2018-05-25 | 2018-11-20 | 妙智科技（深圳）有限公司 | The method, apparatus and storage medium be automatically positioned to lumbar vertebrae in CT |
| CN112989081A (en) * | 2021-05-20 | 2021-06-18 | 首都医科大学附属北京安贞医院 | Method and device for constructing digital reconstruction image library |
| CN117237426A (en) * | 2023-09-18 | 2023-12-15 | 北京大学第三医院（北京大学第三临床医学院） | Vertebra registration method based on lumbar vertebra double-oblique X-ray film |
| WO2024016248A1 (en) * | 2022-07-21 | 2024-01-25 | 中国人民解放军总医院第一医学中心 | Image registration system and method for femoral surgery |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1931097A (en) * | 2006-09-30 | 2007-03-21 | 南方医科大学 | Human body orthopedic navigation system |
| US7653226B2 (en) * | 2004-04-21 | 2010-01-26 | Siemens Medical Solutions Usa, Inc. | Flexible generation of digitally reconstructed radiographs |
| CN102208117A (en) * | 2011-05-04 | 2011-10-05 | 西安电子科技大学 | Method for constructing vertebral three-dimensional geometry and finite element mixture model |
| CN102968633A (en) * | 2012-10-29 | 2013-03-13 | 东软飞利浦医疗设备***有限责任公司 | Lumbar vertebra identification method and device based on computed tomography (CT) side position positioning sheet |
| CN103310219A (en) * | 2012-03-08 | 2013-09-18 | 佳能株式会社 | Method and equipment for precision evaluation of object shape registration, and method and equipment for registration |
| CN103514441A (en) * | 2013-09-21 | 2014-01-15 | 南京信息工程大学 | Facial feature point locating tracking method based on mobile platform |
| US20160260220A1 (en) * | 2015-03-05 | 2016-09-08 | Broncus Medical Inc. | Gpu-based system for performing 2d-3d deformable registration of a body organ using multiple 2d fluoroscopic views |
-
2016
- 2016-12-29 CN CN201611241396.4A patent/CN106875376B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7653226B2 (en) * | 2004-04-21 | 2010-01-26 | Siemens Medical Solutions Usa, Inc. | Flexible generation of digitally reconstructed radiographs |
| CN1931097A (en) * | 2006-09-30 | 2007-03-21 | 南方医科大学 | Human body orthopedic navigation system |
| CN102208117A (en) * | 2011-05-04 | 2011-10-05 | 西安电子科技大学 | Method for constructing vertebral three-dimensional geometry and finite element mixture model |
| CN103310219A (en) * | 2012-03-08 | 2013-09-18 | 佳能株式会社 | Method and equipment for precision evaluation of object shape registration, and method and equipment for registration |
| CN102968633A (en) * | 2012-10-29 | 2013-03-13 | 东软飞利浦医疗设备***有限责任公司 | Lumbar vertebra identification method and device based on computed tomography (CT) side position positioning sheet |
| CN103514441A (en) * | 2013-09-21 | 2014-01-15 | 南京信息工程大学 | Facial feature point locating tracking method based on mobile platform |
| US20160260220A1 (en) * | 2015-03-05 | 2016-09-08 | Broncus Medical Inc. | Gpu-based system for performing 2d-3d deformable registration of a body organ using multiple 2d fluoroscopic views |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108846830A (en) * | 2018-05-25 | 2018-11-20 | 妙智科技（深圳）有限公司 | The method, apparatus and storage medium be automatically positioned to lumbar vertebrae in CT |
| CN112989081A (en) * | 2021-05-20 | 2021-06-18 | 首都医科大学附属北京安贞医院 | Method and device for constructing digital reconstruction image library |
| WO2024016248A1 (en) * | 2022-07-21 | 2024-01-25 | 中国人民解放军总医院第一医学中心 | Image registration system and method for femoral surgery |
| CN117237426A (en) * | 2023-09-18 | 2023-12-15 | 北京大学第三医院（北京大学第三临床医学院） | Vertebra registration method based on lumbar vertebra double-oblique X-ray film |
| CN117237426B (en) * | 2023-09-18 | 2024-03-22 | 北京大学第三医院（北京大学第三临床医学院） | Vertebra registration method based on lumbar vertebra double-oblique X-ray film |
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