CN104867104A - Method for obtaining anatomical structural atlas for target mouse based on XCT image non-rigid registration - Google Patents
Method for obtaining anatomical structural atlas for target mouse based on XCT image non-rigid registration Download PDFInfo
- Publication number
- CN104867104A CN104867104A CN201510259870.5A CN201510259870A CN104867104A CN 104867104 A CN104867104 A CN 104867104A CN 201510259870 A CN201510259870 A CN 201510259870A CN 104867104 A CN104867104 A CN 104867104A
- Authority
- CN
- China
- Prior art keywords
- mouse
- point set
- registration
- target
- image
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 75
- 239000011159 matrix material Substances 0.000 claims abstract description 73
- 210000003484 anatomy Anatomy 0.000 claims abstract description 72
- 238000013507 mapping Methods 0.000 claims abstract description 42
- 210000002615 epidermis Anatomy 0.000 claims description 55
- 238000006073 displacement reaction Methods 0.000 claims description 20
- 238000003384 imaging method Methods 0.000 claims description 18
- 238000002922 simulated annealing Methods 0.000 claims description 10
- 210000003491 skin Anatomy 0.000 claims description 9
- 238000003708 edge detection Methods 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 6
- 210000000988 bone and bone Anatomy 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- JUVIOZPCNVVQFO-HBGVWJBISA-N rotenone Chemical group O([C@H](CC1=C2O3)C(C)=C)C1=CC=C2C(=O)[C@@H]1[C@H]3COC2=C1C=C(OC)C(OC)=C2 JUVIOZPCNVVQFO-HBGVWJBISA-N 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 210000004556 brain Anatomy 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 5
- 210000000056 organ Anatomy 0.000 abstract description 4
- 238000011160 research Methods 0.000 abstract description 3
- 241000699666 Mus <mouse, genus> Species 0.000 description 135
- 230000003287 optical effect Effects 0.000 description 6
- 210000004872 soft tissue Anatomy 0.000 description 5
- 238000003325 tomography Methods 0.000 description 4
- 241000699670 Mus sp. Species 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000009543 diffuse optical tomography Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000005481 NMR spectroscopy Methods 0.000 description 1
- 241001481798 Stochomys longicaudatus Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000002224 dissection Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 238000009666 routine test Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/14—Transformations for image registration, e.g. adjusting or mapping for alignment of images
- G06T3/147—Transformations for image registration, e.g. adjusting or mapping for alignment of images using affine transformations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a method for obtaining anatomical structural atlas for a target mouse based on XCT image non-rigid registration. The method includes basic steps of setting Digimouse model as referential mouse anatomical structural atlas, setting XCT images corresponding to Digimouse as referential images, obtaining and pretreating XCT images of the target mouse, building referential images to registration mapping matrix of the target images by non-rigid registration technology, acting registration mapping matrix onto the referential mouse anatomical structural atlas to build a target mouse anatomical structural atlas and mark tissue and organs of the target mouse. The registration method is high in precision, and tissue and organs of a mouse can be marked on a mouse XCT image in a simple and easy way through image registration method. The method is also applicable to other medical science application fields, such as brain structure research, i.e. the anatomical structure of a target brain can be obtained by standard brain anatomical images.
Description
Technical field
The invention belongs to technical field of image processing, the finite element being specifically related to non-rigidity image registration and target rat tissue anatomical structure is demarcated.
Background technology
Current fluorescent molecular tomography (Fluorescence Molecular Tomography, FMT) the normally used uniform optical structural context of method will introduce the appreciable error in photon transport modeling, and the lifting of effective optical texture prior imformation to FMT reconstruction precision and sensitivity is significant.The foundation of optical texture and the acquisition of anatomical information closely related: on the one hand it is the physical geometry information imparting each region related optical parameter characteristic in anatomical structure, and it is the condition precedent that each area optical parameter obtains at body on the other hand
[1,2].
Common anatomy imaging mode is used for optical texture acquisition and all has some limitations.High-Field toy Magnetic resonance imaging (Micro Magnetic Resonance Imaging, μM RI) has high gray resolution image to soft tissue, and image method can be utilized to obtain each soft tissue organs region.The people such as Dhenain utilize this technology to carry out imaging to multiple mice embryonic, obtain the anatomical structure collection of illustrative plates (Atlas) at different development stage mice embryonic
[3].The people such as Segars utilize many group adult mice nuclear magnetic resonance datas to set up four-dimensional MOBY model, the dynamic dissection structural drawing spectrum of simulation mouse in the physiology courses such as heartbeat breathing
[4].But μM RI cost is high, and imaging is consuming time longer, limit its mouse FMT test in application.X ray computer fault imaging (X-ray Computed Tomography, XCT) is as a kind of conventional anatomy imaging pattern, and its image taking speed is very fast and cost is moderate.But the resolution of X ray to soft tissue is lower, utilize XCT Iamge Segmentation soft tissue to have larger difficulty, if be aided with other image mode, then can obtain biological tissue's body anatomical structure more accurately.The people such as Dogdas utilize XCT, positron emission computerized tomography and cold cut chip technology to carry out multi-modality imaging to mouse, and set up Digimouse model on this basis, obtain the precise anatomical structure collection of illustrative plates of mouse
[5].The method can obtain the anatomical information of mouse exactly, and for correlative study provides important reference significance, but the method imaging system used is complicated, and experimentation is loaded down with trivial details, and cost is higher, is unfavorable for equally adopting in the mouse FMT of routine tests.
[list of references]
[1]L.-H.Wu,W.-B.Wan and X.Wang et al,"Shape-parameterized diffuse opticaltomography holds promise for sensitivity enhancement of fluorescence molecular tomography,"Biomedical Optics Express 10,3640-3659(2014)。
[2]L.-H.Wu,H.-J.Zhao and X.Wang et al,"Enhancement of fluorescence moleculartomography with structural-prior-based diffuse optical tomography:combating opticalbackground uncertainty,"APPLIED OPTICS 53(30),6970-6982(2014)。
[3]M.Dhenain,S.W.Ruffins,and R.E.Jacobs,"Three-Dimensional Digital Mouse AtlasUsing High-Resolution MRI,"Developmental Biology 232,458-470(2001)。
[4]W.P.Segars,B.M.W.Tsui,and E.C.Frey et al,"Development of a 4-D Digital MousePhantom for Molecular Imaging Research,"Molecular Imaging and Biology 6(3),149–159(2004)。
[5]B.Dogdas,D.Stout and A.F.Chatziioannou et al,"Digimouse:a 3D whole body mouseatlas from CT and cryosection data,"PHYSICS IN MEDICINE AND BIOLOGY 52(3),577-587(2007)。
[6]H.Chui and A.Rangarajan,"A new point matching algorithm for non-rigid registration,"Computer Vision and Image Understanding 89,114-141(2003)。
[7]S.Lee,G.Wolberg,and S.Y.Shin,"Scattered Data Interpolation with MultilevelB-Splines,"IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS3(3),228-244(1997)。
Summary of the invention
For problems of the prior art, the present invention proposes a kind of target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT image, and the inventive method is by Digimouse model
[4]be chosen to be the standard anatomical structure of mouse, and by non-rigidity image registration algorithm, by XCT image registration corresponding for Digimouse model to target mouse XCT image, thus realize the demarcation to each histoorgan of target mouse.
In order to solve the problems of the technologies described above, a kind of target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT image that the present invention proposes is: first, be with reference to mouse anatomical structure collection of illustrative plates by Digimouse model specification, be reference picture by XCT image setting corresponding for Digimouse simultaneously; Secondly, carry out XCT imaging to target mouse obtain target image and carry out pre-service; Then, non-rigidity image registration techniques is utilized to build the registration mapping matrix of reference picture to target image; Finally, registration mapping matrix is acted on reference to mouse anatomical structure collection of illustrative plates, construct target mouse anatomical structure collection of illustrative plates.
Further, the concrete steps that the present invention is based on the target mouse anatomical structure collection of illustrative plates acquisition methods of XCT image non-rigidity registration are as follows:
Step one, setting are with reference to mouse anatomical structure collection of illustrative plates and reference picture:
Be with reference to mouse anatomical structure collection of illustrative plates A by Digimouse configuration settings
r, be reference picture I by the XCT image setting of described Digimouse
r;
The acquisition of step 2, target mouse XCT image and pre-service:
Utilize XCT equipment to carry out whole body imaging to target mouse, obtain the XCT image of target mouse; Affined transformation is carried out to target mouse XCT image, by the head of target mouse XCT image small mouse and back towards being converted into and reference picture I
ridentical; Using the XCT image after conversion as target image I
t;
Step 3, structure preliminary registration mapping matrix M
cwith preliminary registration image I
c:
Image segmentation algorithm is utilized to be partitioned into reference picture I respectively
rin mouse bony areas and cuticle region, and target image I
tin mouse bony areas and cuticle region;
Edge detection algorithm is utilized to extract above-mentioned reference picture I respectively
rsmall mouse bony areas and cuticle region and target image I
tin mouse bony areas and cuticle region amount to the boundary profile in four regions, equiprobability sampling is carried out to the boundary profile in described four regions, thus calculates with reference to mouse skeleton character point set L
rb, with reference to mouse epidermis characteristic point set L
rs, target mouse skeleton character point set L
tband target mouse epidermis characteristic point set L
ts;
Utilize TPS-RPM (Thin-plate Spline Robust Point Matching, thin plate spline robust point registration) algorithm
[6]program of increasing income, computing reference mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
b; When calculating, by target mouse skeleton character point set L
tbbe set as target point set, with reference to mouse skeleton character point set L
rbbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, go out skeleton character point homography C by iterative computation
b; :
Utilize the program of increasing income of TPS-RPM algorithm, computing reference mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
s; When calculating, by target mouse epidermis characteristic point set L
tsbe set as target point set, with reference to mouse epidermis characteristic point set L
rsbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, go out epidermis characteristic point homography C by iterative computation
s;
The skeleton character point homography C will tried to achieve respectively
band epidermis characteristic point homography C
sact on reference to mouse skeleton character point set L
rband with reference to mouse epidermis characteristic point set L
rs, obtain the skeleton character point set L after preliminary registration
cband the epidermis characteristic point set L after preliminary registration
cs;
Utilize the result of above-mentioned preliminary Characteristic points match of trying to achieve, build preliminary registration local displacement matrix P:
Above-mentioned matrix P is converted into three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) }, is considered as the functional value of point (x, y, z), i.e. △ x=G respectively by Δ x, Δ y and Δ z
1(x, y, z), △ y=G
2(x, y, z), △ z=G
3(x, y, z);
Adopt Multilevel B-splines fitting algorithm respectively to described three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) } carries out matching, for calculating described reference picture I
rin each pixel along the displacement on x, y, z three directions;
Utilize described Multilevel B-splines approximating method fitting data point set P
xthe process of={ (x, y, z, △ x) } is described in detail as follows:
Describedly to refer at many levels, utilize and be overlying on reference picture I
ron one group of cube control mesh Φ encrypted gradually
0, Φ
1..., Φ
k..., Φ
hsuccessively to the four-dimensional data point set that iteration upgrades
carry out B-spline matching, and by required h layer fitting function sum
as final Multilevel B-splines fitting function; Wherein, △
0ξ=△ x, △
k+1ξ=△
kξ-g
k(x, y, z), g
k(x, y, z) is kth layer B-spline fitting result;
Described kth layer B-spline fit procedure is described below:
Suppose kth layer control mesh Φ
kbe of a size of K
x× K
y× K
z, then kth layer B-spline fitting function is shown below:
In formula (4),
φ
k, (l+i, m+j, n+k)for being positioned at control mesh Φ
kmiddle coordinate is the Controlling vertex value of (l+i, m+j, n+k); L, m, n ∈ { 0,1,2,3}; B
l, B
mand B
nbe respectively l, m, n rank B-spline basis function, wherein the expression formula of 0 to 3 rank B-spline function is described below:
In described formula (4), control mesh Φ
kin each Controlling vertex value by following two steps calculate:
A () calculates
in each data point to control mesh Φ
kin the influence amount of each Controlling vertex value:
With
in a data point p=(x
p, y
p, z
p, △
kξ
p) be described as follows for example:
Data point p=(x
p, y
p, z
p, △
kξ
p) to control mesh Φ
kin the influence matrix of each Controlling vertex show as one and be of a size of K
x× K
y× K
zmatrix Ψ
p; Easy for calculating, definition and matrix Ψ
ptwo measure-alike matrix Γ
pwith Ω
p; Described matrix Ψ
p, Γ
pwith Ω
pmiddle coordinate is that the element of (l+i, m+j, n+k) calculates respectively by formula (6):
In formula (6), l, m, n ∈ { 0,1,2,3};
At matrix Ψ
p, Γ
pwith Ω
pin, except described coordinate is that (l+i, m+j, n+k) amounts to all the other positions beyond 64 elements, ψ
p, γ
pwith ω
pbe 0;
B () asks for control mesh Φ
kin the value of each Controlling vertex
Comprehensively
in each data point to control mesh Φ
kin the impact of each Controlling vertex value, ask for grid Φ processed
kin the value of each Controlling vertex; Control mesh Φ
kmiddle coordinate is the Controlling vertex φ of (a, b, c)
k, (a, b, c)value is:
In formula (7), γ
p, (a, b, c), ω
p, (a, b, c), ψ
p, (a, b, c)be respectively Ψ
p, Γ
pwith Ω
pmiddle coordinate is the element value of (a, b, c);
So far, kth layer B-spline fitting function g
k(x, y, z) establishes; Comprehensive each level fitting function, calculates Multilevel B-splines fitting function
the Multilevel B-splines fitting function g (x, y, z) asked for described in utilization, calculates reference picture I
rin each pixel along the displacement of x-axis;
In like manner, the four-dimensional data point set P of Multilevel B-splines matching is utilized
y={ (x, y, z, △ y) } and P
z={ (x, y, z, △ z) }, thus calculate reference picture I
rin each pixel along y, z-axis in displacement, build reference picture I thus
rpreliminary registration mapping matrix M
c;
Utilize described preliminary registration mapping matrix M
c, oppositely solve preliminary registration image I
c; When building preliminary registration image, the assignment of gray scale adopts tri-linear interpolation methods;
Step 4, build meticulous registration mapping matrix M
f:
Utilize image segmentation algorithm, extract preliminary registration image I
cin mouse skin region and bony areas, and utilize edge detection algorithm to extract the profile of described mouse skin region and bony areas respectively; The profile of mouse skin and bone is superposed mutually, and uses rectangular node to sample, obtain one group of preliminary registration characteristics of image point set L' thus
c;
Block matching method is utilized to ask for preliminary registration characteristics of image point set L'
cin the correspondence position of each unique point on target image, with L'
cin any point p
l=(x
p, y
p, z
p) be example, by as follows for described process prescription:
At preliminary registration image I
cin with coordinate (x
p, y
p, z
p) centered by choose and be of a size of N
1× N
1× N
1cube neighborhood T, at target image I
tin with coordinate (x
p, y
p, z
p) centered by choose N
2× N
2× N
2cube neighborhood S, wherein N
2>N
1; Using T as template, S is as region of search, and in S region, search and T have the subregion s of maximum similarity
1, and by s
1central point p
l' as putting p
lcorrespondence position;
By that analogy, preliminary registration characteristics of image point set L' is found out successively
cmiddle each point is at target image I
ton correspondence position, construct meticulous registration features point set L thus
f;
Utilize preliminary registration characteristics of image point set L'
cand meticulous registration features point set L
fbuild meticulous registration local displacement matrix Q:
Q=[L'
c,L'
c-L
f]=[x,y,z,Δx,Δy,Δz] (8)
Above-mentioned matrix Q is converted into three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) }, Q
z={ (x, y, z, △ z) }; Consistent with Multilevel B-splines fit procedure in step 3, respectively to described three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) } carries out Multilevel B-splines matching, thus calculates preliminary registration image I respectively
cin each pixel along the displacement of x, y, z three axis, build preliminary registration image I thus
cmeticulous registration mapping matrix M
f;
Step 5, establishing target mouse anatomical structure collection of illustrative plates:
By described with reference to mouse anatomical structure collection of illustrative plates A
rbe projected to pixel coordinate system, obtain the reference mouse anatomical structure collection of illustrative plates under pixel coordinate system
in order successively by preliminary registration mapping matrix M
cand meticulous registration mapping matrix M
fact on the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
make the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
produce the distortion identical with step 3 preliminary registration and the meticulous registration process of step 4, obtain the registration mouse anatomical structure collection of illustrative plates under pixel coordinate system
by the registration mouse anatomical structure collection of illustrative plates under described pixel coordinate system
under being projected to physical coordinates system, obtain the registration mouse anatomical structure collection of illustrative plates A under physical coordinates system
f, the registration mouse anatomical structure collection of illustrative plates A under described physical coordinates system
fbe target mouse anatomical structure collection of illustrative plates.
Compared with prior art, the invention has the beneficial effects as follows:
1. the present invention only uses XCT single mode imaging mode to mouse imaging, and experiment is simple, and cost is lower;
2. the soft tissue identification difficulty that method proposed by the invention can effectively avoid XCT image single mode formation method to cause, simple realizes the identified problems of mouse tissue organ on XCT image;
3. the two step registrations that the present invention uses can realize good registration accuracy, thus provide condition for the correct mark of mouse anatomical structure;
4. the present invention also can use other mouse anatomical structure collection of illustrative plates as a reference in specific implementation process, obtains to the anatomical structure collection of illustrative plates realizing being in target mouse under different figure or developmental stage;
5. main thought of the present invention is equally applicable to other field of medical applications as the research of human brain structure, namely utilizes standard human brain anatomical images and the method for the invention, the acquisition of the anatomical structure of realize target human brain.
Accompanying drawing explanation
Fig. 1 is the target mouse anatomical structure collection of illustrative plates acquisition methods block diagram based on the non-rigidity registration of XCT image;
Fig. 2 is the concrete implementing procedure figure of the target mouse anatomical structure collection of illustrative plates acquisition methods based on XCT image non-rigidity registration that the present invention proposes;
Fig. 3 is the data flow diagram of the present invention in specific implementation process.
Embodiment
Be described in further detail technical solution of the present invention below in conjunction with the drawings and specific embodiments, described specific embodiment only explains the present invention, not in order to limit the present invention.
The basic step that Fig. 1 shows the target mouse anatomical structure collection of illustrative plates acquisition methods that the present invention is based on the non-rigidity registration of XCT image is:
First, be with reference to mouse anatomical structure collection of illustrative plates by Digimouse configuration settings, be reference picture by XCT image setting corresponding for Digimouse simultaneously;
Secondly, carry out XCT imaging to target mouse obtain target image and carry out pre-service;
Then, non-rigidity image registration techniques is utilized to build the registration mapping matrix of reference picture to target image;
Finally, registration mapping matrix is acted on reference to mouse anatomical structure collection of illustrative plates, construct target mouse anatomical structure collection of illustrative plates; Registration mapping matrix can be selected to act on reference picture, construct registering images, in order to evaluation image registration accuracy.
Because different mouse exists larger difference when imaging, first described method for registering carries out preliminary registration adjustment mouse general configuration, secondly uses meticulous registration adjustment image detail part.Therefore, the target mouse anatomical structure collection of illustrative plates acquisition methods that the present invention is based on the non-rigidity registration of XCT image can be refined as 5 steps in implementation process.As shown in Figure 2, the data stream in implementation process as shown in Figure 3 for the process flow diagram of described implementation process.5 steps that the present invention is based in the target mouse anatomical structure collection of illustrative plates acquisition methods specific implementation process of the non-rigidity registration of XCT image are described in detail as follows:
Step one, setting are with reference to mouse anatomical structure collection of illustrative plates and reference picture:
By the Digimouse model proposed in people's documents such as Dogdas
[5]be set as with reference to mouse anatomical structure collection of illustrative plates A
r, be reference picture I by the XCT image setting of described Digimouse
r;
So far, digitized mouse reference model specification is complete;
The acquisition of step 2, target mouse XCT image and pre-service:
Utilize XCT equipment to carry out whole body imaging to target mouse, obtain the XCT image of target mouse; Affined transformation is carried out to target mouse XCT image, by the head of target mouse XCT image small mouse and back towards being converted into and reference picture I
ridentical; Using the XCT image after conversion as target image I
t;
So far, the target image I of target mouse is come from
tobtain complete;
Step 3, structure preliminary registration mapping matrix M
cwith preliminary registration image I
c:
Because target mouse also exists larger difference with reference to the posture of mouse, size and body internal cavity position, therefore first the present invention carries out preliminary images registration, to adjust the difference of two width image small mouse sizes and figure.The main process of described step 3 is reference picture I
rand target image I
tcarry out preliminary registration, construct and control reference picture I
rproduce the preliminary registration mapping matrix M of non-rigidity deformation
c, by M
cact on reference picture I
r, construct preliminary registration image I
c.Specific implementation process is described in detail as follows:
(3-1) reference picture I is extracted respectively
rand target image I
tfeature point set
Image segmentation algorithm is utilized to be partitioned into reference picture I respectively
rin mouse bony areas and cuticle region, and target image I
tin mouse bony areas and cuticle region;
Edge detection algorithm is utilized to extract above-mentioned reference picture I respectively
rsmall mouse bony areas and cuticle region and target image I
tin mouse bony areas and cuticle region amount to the boundary profile in four regions, equiprobability sampling is carried out to the boundary profile in described four regions, thus calculates with reference to mouse skeleton character point set L
rb, with reference to mouse epidermis characteristic point set L
rs, target mouse skeleton character point set L
tband target mouse epidermis characteristic point set L
ts;
(3-2) based on the Characteristic points match of TPS-RPM algorithm
Utilize the TPS-RPM algorithm that Haili Chui proposes
[6]program of increasing income, computing reference mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
b, with reference to mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
s;
Described with reference to mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
band with reference to mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
sbe fuzzy homography, in fuzzy homography, each element is the floating number between [0,1], in order to describe the power of degree of correspondence between 2;
Wherein, at computing reference mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
btime, by target mouse skeleton character point set L
tbbe set as target point set, with reference to mouse skeleton character point set L
rbbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, the extreme point by the following energy equation of iterative computation:
In formula (1), c
b, ijfor homography C
bin element; N
rbfor reference mouse skeleton character point set L
rbcomprise the number of unique point, N
tbfor target mouse skeleton character point set L
tbcomprise the number of point patterns; l
rb, ifor reference mouse skeleton character point set L
rbin the coordinate of i-th unique point, l
tb, jfor target mouse skeleton character point set L
tbin the coordinate of a jth unique point; F is thin plate spline function; || Lf||
2for the smoothness constraint to f, wherein L is differentiating operator; T is for controlling the temperature coefficient of fuzzy degree of correspondence in simulated annealing process; λ is priori smoothness weights; γ is Lu Bang Control Sampled-Data weights; Along with temperature coefficient T reduces gradually, homography C
bwith thin plate spline function f alternating iteration; Homography C
bfinal iteration result be required skeleton character point homography C
b;
At computing reference mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
stime, by target mouse epidermis characteristic point set L
tsbe set as target point set, with reference to mouse epidermis characteristic point set L
rsbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, the extreme point by the following energy equation of iterative computation:
In formula (2), c
s, ijfor homography C
sin element; N
rsfor reference mouse epidermis characteristic point set L
rscomprise the number of unique point, N
tsfor target mouse epidermis characteristic point set L
tscomprise the number of point patterns; l
rs, ifor reference mouse epidermis characteristic point set L
rsin the coordinate of i-th point, l
ts, jfor target mouse epidermis characteristic point set L
tsin the coordinate of jth point; Remaining variables implication is identical with formula (1); Along with temperature coefficient T reduces gradually, homography C
swith thin plate spline function f alternating iteration; Homography C
sfinal iteration result be required epidermis characteristic point homography C
s;
The skeleton character point homography C will tried to achieve respectively
band epidermis characteristic point homography C
sact on reference to mouse skeleton character point set L
rband with reference to mouse epidermis characteristic point set L
rs, obtain the skeleton character point set L after preliminary registration
cband the epidermis characteristic point set L after preliminary registration
cs;
(3-3) structure of preliminary registration local displacement matrix P
Utilize the result of above-mentioned preliminary Characteristic points match of trying to achieve, build preliminary registration local displacement matrix P:
(3-4) preliminary registration mapping matrix M
cstructure
Above-mentioned matrix P is converted into three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) }, is considered as the functional value of point (x, y, z), i.e. △ x=G respectively by Δ x, Δ y and Δ z
1(x, y, z), △ y=G
2(x, y, z), △ z=G
3(x, y, z);
Adopt Multilevel B-splines fitting algorithm respectively to described three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) } carries out matching, for calculating described reference picture I
rin each pixel along the displacement on x, y, z three directions;
In the present invention, Multilevel B-splines fitting algorithm is the three-dimensional data fitting algorithm that Seungyong Lee proposes
[7]expansion on four-dimentional space.Utilize described Multilevel B-splines approximating method fitting data point set P
xthe process of={ (x, y, z, △ x) } is described in detail as follows:
Describedly to refer at many levels, utilize and be overlying on reference picture I
ron one group of cube control mesh Φ encrypted gradually
0, Φ
1..., Φ
k..., Φ
hsuccessively to the four-dimensional data point set that iteration upgrades
carry out B-spline matching, and by required h layer fitting function sum
as final Multilevel B-splines fitting function; Wherein, △
0ξ=△ x, △
k+1ξ=△
kξ-g
k(x, y, z), g
k(x, y, z) is kth layer B-spline fitting result;
Described kth layer B-spline fit procedure is described below:
Suppose kth layer control mesh Φ
kbe of a size of K
x× K
y× K
z, then kth layer B-spline fitting function is shown below:
In formula (4),
φ
k, (l+i, m+j, n+k)for being positioned at control mesh Φ
kmiddle coordinate is the Controlling vertex value of (l+i, m+j, n+k); L, m, n ∈ { 0,1,2,3}; B
l, B
mand B
nbe respectively l, m, n rank B-spline basis function, wherein the expression formula of 0 to 3 rank B-spline function is described below:
In described formula (4), control mesh Φ
kin each Controlling vertex value by following two steps calculate:
A () calculates
in each data point to control mesh Φ
kin the influence amount of each Controlling vertex value:
With
in a data point p=(x
p, y
p, z
p, △
kξ
p) be described as follows for example:
Data point p=(x
p, y
p, z
p, △
kξ
p) to control mesh Φ
kin the influence matrix of each Controlling vertex show as one and be of a size of K
x× K
y× K
zmatrix Ψ
p; Easy for calculating, definition and matrix Ψ
ptwo measure-alike matrix Γ
pwith Ω
p; Described matrix Ψ
p, Γ
pwith Ω
pmiddle coordinate is that the element of (l+i, m+j, n+k) calculates respectively by formula (6):
In formula (6), l, m, n ∈ { 0,1,2,3};
At matrix Ψ
p, Γ
pwith Ω
pin, except described coordinate is that (l+i, m+j, n+k) amounts to all the other positions beyond 64 elements, ψ
p, γ
pwith ω
pbe 0;
B () asks for control mesh Φ
kin the value of each Controlling vertex
Comprehensively
in each data point to control mesh Φ
kin the impact of each Controlling vertex value, ask for grid Φ processed
kin the value of each Controlling vertex; Control mesh Φ
kmiddle coordinate is the Controlling vertex φ of (a, b, c)
k, (a, b, c)value is:
In formula (7), γ
p, (a, b, c), ω
p, (a, b, c), ψ
p, (a, b, c)be respectively Ψ
p, Γ
pwith Ω
pmiddle coordinate is the element value of (a, b, c);
So far, kth layer B-spline fitting function g
k(x, y, z) establishes; Comprehensive each level fitting function, calculates Multilevel B-splines fitting function
the Multilevel B-splines fitting function g (x, y, z) asked for described in utilization, calculates reference picture I
rin each pixel along the displacement of x-axis;
In like manner, the four-dimensional data point set P of Multilevel B-splines matching is utilized
y={ (x, y, z, △ y) } and P
z={ (x, y, z, △ z) }, thus calculate reference picture I
rin each pixel along y, z-axis in displacement, build reference picture I thus
rpreliminary registration mapping matrix M
c;
(3-5) preliminary registration image I
cstructure
Utilize described preliminary registration mapping matrix M
c, oppositely solve preliminary registration image I
c; When building preliminary registration image, the assignment of gray scale adopts tri-linear interpolation methods;
So far, preliminary registration mapping matrix M
c, with preliminary registration image I
cobtain complete.
Step 4, build meticulous registration mapping matrix M
fand meticulous registering images I
f:
After preliminary registration, preliminary registration image I
cwith target image I
tstill there is bigger difference, in order to improve registration accuracy, needing to carry out more meticulous registration.The main process of described step 4 is preliminary registration image I
cand target image I
tcarry out meticulous registration, construct and control preliminary registration image I
cproduce the meticulous registration mapping matrix M of non-rigidity deformation
f, by M
fact on preliminary registration image I
c, construct meticulous registering images I
cf.Specific implementation process is described in detail as follows:
(4-1) preliminary registration characteristics of image point set is again extracted
Utilize image segmentation algorithm, extract preliminary registration image I
cin mouse skin region and bony areas, and utilize edge detection algorithm to extract the profile of described mouse skin region and bony areas respectively; The profile of mouse skin and bone is superposed mutually, and uses rectangular node to sample, obtain one group of preliminary registration characteristics of image point set L' thus
c;
(4-2) based on the Characteristic points match of block matching method
Block matching method is utilized to ask for preliminary registration characteristics of image point set L'
cin the correspondence position of each unique point on target image, with L'
cin any point p
l=(x
p, y
p, z
p) be example, by as follows for described process prescription:
At preliminary registration image I
cin with coordinate (x
p, y
p, z
p) centered by choose and be of a size of N
1× N
1× N
1cube neighborhood T, at target image I
tin with coordinate (x
p, y
p, z
p) centered by choose N
2× N
2× N
2cube neighborhood S, wherein N
2>N
1; Using T as template, S is as region of search, and in S region, search and T have the subregion s of maximum similarity
1, and by s
1central point p
l' as putting p
lcorrespondence position;
By that analogy, preliminary registration characteristics of image point set L' is found out successively
cmiddle each point is at target image I
ton correspondence position, construct meticulous registration features point set L thus
f;
(4-3) structure of meticulous registration local displacement matrix Q
Utilize preliminary registration characteristics of image point set L'
cand meticulous registration features point set L
fbuild meticulous registration local displacement matrix Q:
Q=[L'
c,L'
c-L
f]=[x,y,z,Δx,Δy,Δz] (8)
(4-4) meticulous registration mapping matrix M
fstructure
Above-mentioned matrix Q is converted into three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) }, Q
z={ (x, y, z, △ z) }; Consistent with Multilevel B-splines fit procedure in step 3, respectively to described three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) } carries out Multilevel B-splines matching, thus calculates preliminary registration image I respectively
cin each pixel along the displacement of x, y, z three axis, build preliminary registration image I thus
cmeticulous registration mapping matrix M
f;
So far, meticulous registration mapping matrix M
fobtain complete.
(4-5) meticulous registering images I
fstructure (optional)
After this process, can select meticulous registration mapping matrix M
fact on preliminary registration image I
c, construct meticulous registering images I
f; When building preliminary registration image, the assignment of gray scale adopts tri-linear interpolation methods equally; By judging final registering images (i.e. meticulous registering images I
f) and target image I
tsimilarity to evaluate the degree of functioning of registration;
Step 5, establishing target mouse anatomical structure collection of illustrative plates:
In non-rigidity process of image registration, by preliminary registration mapping matrix M
cand meticulous registration mapping matrix M
fcontrol action, reference picture I
rbe deformed into meticulous registering images I
f, meticulous registering images I
fwith target image I
tthere is higher similarity.Therefore, by preliminary registration mapping matrix M
cand meticulous registration mapping matrix M
fact on the anatomical structure collection of illustrative plates A with reference to mouse successively
r, then the anatomical structure collection of illustrative plates of establishing target mouse can be similar to.But, under building on physical coordinates system with reference to anatomical structure collection of illustrative plates (in units of mm), and above-mentioned two registration mapping matrixes build on pixel coordinate system under (in units of pixel), therefore need in the process A
rcarry out coordinate transform.Specific implementation process is described in detail as follows:
(5-1) by described reference mouse anatomical structure collection of illustrative plates A
rbe projected to pixel coordinate system, obtain the reference mouse anatomical structure collection of illustrative plates under pixel coordinate system
(5-2) in order successively by preliminary registration mapping matrix M
cand meticulous registration mapping matrix M
fact on the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
make the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
produce the distortion identical with step 3 preliminary registration and the meticulous registration process of step 4, obtain the registration mouse anatomical structure collection of illustrative plates under pixel coordinate system
(5-3) by the registration mouse anatomical structure collection of illustrative plates under described pixel coordinate system
under being projected to physical coordinates system, obtain the registration mouse anatomical structure collection of illustrative plates A under physical coordinates system
f, the registration mouse anatomical structure collection of illustrative plates A under described physical coordinates system
fbe target mouse anatomical structure collection of illustrative plates.
Although invention has been described by reference to the accompanying drawings above; but the present invention is not limited to above-mentioned embodiment; above-mentioned embodiment is only schematic; instead of it is restrictive; those of ordinary skill in the art is under enlightenment of the present invention; when not departing from present inventive concept, can also make a lot of distortion, these all belong within protection of the present invention.
Claims (3)
1. the target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT image, it is characterized in that, its basic step is: first, is with reference to mouse anatomical structure collection of illustrative plates by Digimouse model specification, is reference picture by XCT image setting corresponding for Digimouse simultaneously; Secondly, carry out XCT imaging to target mouse obtain target image and carry out pre-service; Then, non-rigidity image registration techniques is utilized to build the registration mapping matrix of reference picture to target image; Finally, registration mapping matrix is acted on reference to mouse anatomical structure collection of illustrative plates, construct target mouse anatomical structure collection of illustrative plates.
2., according to claim 1 based on the target mouse anatomical structure collection of illustrative plates acquisition methods of the non-rigidity registration of XCT image, it is characterized in that, concrete steps are as follows:
Step one, setting are with reference to mouse anatomical structure collection of illustrative plates and reference picture:
Be with reference to mouse anatomical structure collection of illustrative plates A by Digimouse configuration settings
r, be reference picture I by the XCT image setting of described Digimouse
r;
The acquisition of step 2, target mouse XCT image and pre-service:
Utilize XCT equipment to carry out whole body imaging to target mouse, obtain the XCT image of target mouse; Affined transformation is carried out to target mouse XCT image, by the head of target mouse XCT image small mouse and back towards being converted into and reference picture I
ridentical; Using the XCT image after conversion as target image I
t;
Step 3, structure preliminary registration mapping matrix M
cwith preliminary registration image I
c:
Image segmentation algorithm is utilized to be partitioned into reference picture I respectively
rin mouse bony areas and cuticle region, and target image I
tin mouse bony areas and cuticle region;
Edge detection algorithm is utilized to extract above-mentioned reference picture I respectively
rsmall mouse bony areas and cuticle region and target image I
tin mouse bony areas and cuticle region amount to the boundary profile in four regions, equiprobability sampling is carried out to the boundary profile in described four regions, thus calculates with reference to mouse skeleton character point set L
rb, with reference to mouse epidermis characteristic point set L
rs, target mouse skeleton character point set L
tband target mouse epidermis characteristic point set L
ts;
Utilize the program of increasing income of TPS-RPM algorithm, computing reference mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
b; When calculating, by target mouse skeleton character point set L
tbbe set as target point set, with reference to mouse skeleton character point set L
rbbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, go out skeleton character point homography C by iterative computation
b;
Utilize the program of increasing income of TPS-RPM algorithm, computing reference mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
s; When calculating, by target mouse epidermis characteristic point set L
tsbe set as target point set, with reference to mouse epidermis characteristic point set L
rsbe set as point set subject to registration, and the initial temperature coefficient set in TPS-RPM simulated annealing used and stopping criterion for iteration, go out epidermis characteristic point homography C by iterative computation
s;
The skeleton character point homography C will tried to achieve respectively
band epidermis characteristic point homography C
sact on reference to mouse skeleton character point set L
rband with reference to mouse epidermis characteristic point set L
rs, obtain the skeleton character point set L after preliminary registration
cband the epidermis characteristic point set L after preliminary registration
cs;
Utilize the result of above-mentioned preliminary Characteristic points match of trying to achieve, build preliminary registration local displacement matrix P:
Above-mentioned matrix P is converted into three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) }, is considered as the functional value of point (x, y, z), i.e. △ x=G respectively by Δ x, Δ y and Δ z
1(x, y, z), △ y=G
2(x, y, z), △ z=G
3(x, y, z);
Adopt Multilevel B-splines fitting algorithm respectively to described three groups of four-dimensional data point set P
x={ (x, y, z, △ x) }, P
y={ (x, y, z, △ y) }, P
z={ (x, y, z, △ z) } carries out matching, for calculating described reference picture I
rin each pixel along the displacement on x, y, z three directions;
Utilize described Multilevel B-splines approximating method fitting data point set P
xthe process of={ (x, y, z, △ x) } is described in detail as follows:
Describedly to refer at many levels, utilize and be overlying on reference picture I
ron one group of cube control mesh Φ encrypted gradually
0, Φ
1..., Φ
k..., Φ
hsuccessively to the four-dimensional data point set that iteration upgrades
carry out B-spline matching, and by required h layer fitting function sum
as final Multilevel B-splines fitting function; Wherein, △
0ξ=△ x, △
k+1ξ=△
kξ-g
k(x, y, z), g
k(x, y, z) is kth layer B-spline fitting result;
Described kth layer B-spline fit procedure is described below:
Suppose kth layer control mesh Φ
kbe of a size of K
x× K
y× K
z, then kth layer B-spline fitting function is shown below:
In formula (4),
φ
k, (l+i, m+j, n+k)for being positioned at control mesh Φ
kmiddle coordinate is the Controlling vertex value of (l+i, m+j, n+k); L, m, n ∈ { 0,1,2,3}; B
l, B
mand B
nbe respectively l, m, n rank B-spline basis function, wherein the expression formula of 0 to 3 rank B-spline function is described below:
In described formula (4), control mesh Φ
kin each Controlling vertex value by following two steps calculate:
A () calculates
in each data point to control mesh Φ
kin the influence amount of each Controlling vertex value:
With
in a data point p=(x
p, y
p, z
p, △
kξ
p) be described as follows for example:
Data point p=(x
p, y
p, z
p, △
kξ
p) to control mesh Φ
kin the influence matrix of each Controlling vertex show as one and be of a size of K
x× K
y× K
zmatrix Ψ
p; Easy for calculating, definition and matrix Ψ
ptwo measure-alike matrix Γ
pwith Ω
p; Described matrix Ψ
p, Γ
pwith Ω
pmiddle coordinate is that the element of (l+i, m+j, n+k) calculates respectively by formula (6):
In formula (6), l, m, n ∈ { 0,1,2,3};
At matrix Ψ
p, Γ
pwith Ω
pin, except described coordinate is that (l+i, m+j, n+k) amounts to all the other positions beyond 64 elements, ψ
p, γ
pwith ω
pbe 0;
B () asks for control mesh Φ
kin the value of each Controlling vertex
Comprehensively
in each data point to control mesh Φ
kin the impact of each Controlling vertex value, ask for grid Φ processed
kin the value of each Controlling vertex; Control mesh Φ
kmiddle coordinate is the Controlling vertex φ of (a, b, c)
k, (a, b, c)value is:
In formula (7), γ
p, (a, b, c), ω
p, (a, b, c), ψ
p, (a, b, c)be respectively Ψ
p, Γ
pwith Ω
pmiddle coordinate is the element value of (a, b, c);
So far, kth layer B-spline fitting function g
k(x, y, z) establishes; Comprehensive each level fitting function, calculates Multilevel B-splines fitting function
the Multilevel B-splines fitting function g (x, y, z) asked for described in utilization, calculates reference picture I
rin each pixel along the displacement of x-axis;
In like manner, the four-dimensional data point set P of Multilevel B-splines matching is utilized
y={ (x, y, z, △ y) } and P
z={ (x, y, z, △ z) }, thus calculate reference picture I
rin each pixel along y, z-axis in displacement, build reference picture I thus
rpreliminary registration mapping matrix M
c;
Utilize described preliminary registration mapping matrix M
c, oppositely solve preliminary registration image I
c; When building preliminary registration image, the assignment of gray scale adopts tri-linear interpolation methods;
Step 4, build meticulous registration mapping matrix M
f:
Utilize image segmentation algorithm, extract preliminary registration image I
cin mouse skin region and bony areas, and utilize edge detection algorithm to extract the profile of described mouse skin region and bony areas respectively; The profile of mouse skin and bone is superposed mutually, and uses rectangular node to sample, obtain one group of preliminary registration characteristics of image point set L' thus
c;
Block matching method is utilized to ask for preliminary registration characteristics of image point set L'
cin the correspondence position of each unique point on target image, with L'
cin any point p
l=(x
p, y
p, z
p) be example, by as follows for described process prescription:
At preliminary registration image I
cin with coordinate (x
p, y
p, z
p) centered by choose and be of a size of N
1× N
1× N
1cube neighborhood T, at target image I
tin with coordinate (x
p, y
p, z
p) centered by choose N
2× N
2× N
2cube neighborhood S, wherein N
2>N
1; Using T as template, S is as region of search, and in S region, search and T have the subregion s of maximum similarity
1, and by s
1central point p '
las a p
lcorrespondence position;
By that analogy, preliminary registration characteristics of image point set L' is found out successively
cmiddle each point is at target image I
ton correspondence position, construct meticulous registration features point set L thus
f;
Utilize preliminary registration characteristics of image point set L'
cand meticulous registration features point set L
fbuild meticulous registration local displacement matrix Q:
Q=[L'
c,L'
c-L
f]=[x,y,z,Δx,Δy,Δz] (8)
Above-mentioned matrix Q is converted into three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) }, Q
z={ (x, y, z, △ z) }; Consistent with Multilevel B-splines fit procedure in step 3, respectively to described three groups of four-dimensional data point set Q
x={ (x, y, z, △ x) }, Q
y={ (x, y, z, △ y) } carries out Multilevel B-splines matching, thus calculates preliminary registration image I respectively
cin each pixel along the displacement of x, y, z three axis, build preliminary registration image I thus
cmeticulous registration mapping matrix M
f;
Step 5, establishing target mouse anatomical structure collection of illustrative plates:
By described with reference to mouse anatomical structure collection of illustrative plates A
rbe projected to pixel coordinate system, obtain the reference mouse anatomical structure collection of illustrative plates under pixel coordinate system
in order successively by preliminary registration mapping matrix M
cand meticulous registration mapping matrix M
fact on the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
make the reference mouse anatomical structure collection of illustrative plates under described pixel coordinate system
produce the distortion identical with step 3 preliminary registration and the meticulous registration process of step 4, obtain the registration mouse anatomical structure collection of illustrative plates under pixel coordinate system
by the registration mouse anatomical structure collection of illustrative plates under described pixel coordinate system
under being projected to physical coordinates system, obtain the registration mouse anatomical structure collection of illustrative plates A under physical coordinates system
f, the registration mouse anatomical structure collection of illustrative plates A under described physical coordinates system
fbe target mouse anatomical structure collection of illustrative plates.
3. according to claim 2 based on the target mouse anatomical structure collection of illustrative plates acquisition methods of the non-rigidity registration of XCT image, it is characterized in that, in step 3, utilize the program of increasing income of TPS-RPM algorithm to ask for skeleton character point homography C
bwith epidermis characteristic point homography C
sdetailed process as follows:
Described with reference to mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
band with reference to mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
sbe fuzzy homography, in fuzzy homography, each element is the floating number between [0,1], in order to describe the power of degree of correspondence between 2;
Described TPS-RPM algorithm is at computing reference mouse skeleton character point set L
rbwith target mouse skeleton character point set L
tbbetween skeleton character point homography C
btime, utilize simulated annealing to be calculated as follows the minimum point of energy equation:
In formula (1), c
b, ijfor homography C
bin element; N
rbfor reference mouse skeleton character point set L
rbcomprise the number of unique point, N
tbfor target mouse skeleton character point set L
tbcomprise the number of point patterns; l
rb, ifor reference mouse skeleton character point set L
rbin the coordinate of i-th unique point, l
tb, jfor target mouse skeleton character point set L
tbin the coordinate of a jth unique point; F is thin plate spline function; || Lf||
2for the smoothness constraint to f, wherein L is differentiating operator; T is for controlling the temperature coefficient of fuzzy degree of correspondence in simulated annealing process; λ is priori smoothness weights; γ is Lu Bang Control Sampled-Data weights; Along with temperature coefficient T reduces gradually, homography C
bwith thin plate spline function f alternating iteration; Homography C
bfinal iteration result be required skeleton character point homography C
b;
Described TPS-RPM algorithm is at computing reference mouse epidermis characteristic point set L
rswith target mouse epidermis characteristic point set L
tsbetween epidermis characteristic point homography C
stime, utilize simulated annealing to be calculated as follows the minimum point of energy equation:
In formula (2), c
s, ijfor homography C
sin element; N
rsfor reference mouse epidermis characteristic point set L
rscomprise the number of unique point, N
tsfor target mouse epidermis characteristic point set L
tscomprise the number of point patterns; l
rs, ifor reference mouse epidermis characteristic point set L
rsin the coordinate of i-th point, l
ts, jfor target mouse epidermis characteristic point set L
tsin the coordinate of jth point; Remaining variables implication is identical with formula (1); Along with temperature coefficient T reduces gradually, homography C
swith thin plate spline function f alternating iteration; Homography C
sfinal iteration result be required epidermis characteristic point homography C
s.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510259870.5A CN104867104B (en) | 2015-05-20 | 2015-05-20 | Target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT images |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510259870.5A CN104867104B (en) | 2015-05-20 | 2015-05-20 | Target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT images |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104867104A true CN104867104A (en) | 2015-08-26 |
CN104867104B CN104867104B (en) | 2017-12-15 |
Family
ID=53912921
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510259870.5A Expired - Fee Related CN104867104B (en) | 2015-05-20 | 2015-05-20 | Target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT images |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104867104B (en) |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105212936A (en) * | 2015-09-09 | 2016-01-06 | 首都医科大学宣武医院 | Brain template generation method |
CN105427237A (en) * | 2015-11-25 | 2016-03-23 | 长春乙天科技有限公司 | Steel mesh image registration and detection method for large-format optical measurement system |
CN110322491A (en) * | 2019-06-11 | 2019-10-11 | 大连理工大学 | A kind of algorithm of deformable mouse systemic map and mouse Image registration |
CN111091567A (en) * | 2020-03-23 | 2020-05-01 | 南京景三医疗科技有限公司 | Medical image registration method, medical device and storage medium |
CN112116642A (en) * | 2020-09-27 | 2020-12-22 | 上海联影医疗科技股份有限公司 | Registration method of medical image, electronic device and storage medium |
CN112967236A (en) * | 2018-12-29 | 2021-06-15 | 上海联影智能医疗科技有限公司 | Image registration method and device, computer equipment and storage medium |
CN112995528A (en) * | 2021-05-06 | 2021-06-18 | 中国工程物理研究院流体物理研究所 | Device, system and method for image registration between channels of photoelectric framing camera |
CN113298856A (en) * | 2021-05-28 | 2021-08-24 | 上海联影医疗科技股份有限公司 | Image registration method, device, equipment and medium |
CN114742869A (en) * | 2022-06-15 | 2022-07-12 | 西安交通大学医学院第一附属医院 | Brain neurosurgery registration method based on pattern recognition and electronic equipment |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2013019985A2 (en) * | 2011-08-03 | 2013-02-07 | The Regents Of The University Of California | 2d/3d registration of a digital mouse atlas with x-ray projection images and optical camera photos |
CN103077522A (en) * | 2013-01-22 | 2013-05-01 | 南方医科大学 | Improved registration method for image with obvious local deformation |
CN104599268A (en) * | 2015-01-06 | 2015-05-06 | 广州医科大学附属肿瘤医院 | Local area accurate deformation registration algorithm combining point registration |
-
2015
- 2015-05-20 CN CN201510259870.5A patent/CN104867104B/en not_active Expired - Fee Related
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2013019985A2 (en) * | 2011-08-03 | 2013-02-07 | The Regents Of The University Of California | 2d/3d registration of a digital mouse atlas with x-ray projection images and optical camera photos |
CN103077522A (en) * | 2013-01-22 | 2013-05-01 | 南方医科大学 | Improved registration method for image with obvious local deformation |
CN104599268A (en) * | 2015-01-06 | 2015-05-06 | 广州医科大学附属肿瘤医院 | Local area accurate deformation registration algorithm combining point registration |
Non-Patent Citations (3)
Title |
---|
赵会娟、高峰等: ""基于区域标识的‘粗粒度’扩散光学层析成像方法稳态测量模式"", 《光学学报》 * |
赵会娟等: ""小鼠荧光层析成像系统及数据提取扩展方法"", 《光子学报》 * |
高峰等: ""一种多模态医学图像数据融合方法与应用"", 《中国医疗设备》 * |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105212936A (en) * | 2015-09-09 | 2016-01-06 | 首都医科大学宣武医院 | Brain template generation method |
CN105427237A (en) * | 2015-11-25 | 2016-03-23 | 长春乙天科技有限公司 | Steel mesh image registration and detection method for large-format optical measurement system |
CN105427237B (en) * | 2015-11-25 | 2018-04-24 | 长春乙天科技有限公司 | A kind of steel mesh image registration of large format optical measuring system and detection method |
CN112967236B (en) * | 2018-12-29 | 2024-02-27 | 上海联影智能医疗科技有限公司 | Image registration method, device, computer equipment and storage medium |
CN112967236A (en) * | 2018-12-29 | 2021-06-15 | 上海联影智能医疗科技有限公司 | Image registration method and device, computer equipment and storage medium |
CN110322491A (en) * | 2019-06-11 | 2019-10-11 | 大连理工大学 | A kind of algorithm of deformable mouse systemic map and mouse Image registration |
CN111091567A (en) * | 2020-03-23 | 2020-05-01 | 南京景三医疗科技有限公司 | Medical image registration method, medical device and storage medium |
CN111091567B (en) * | 2020-03-23 | 2020-06-23 | 南京景三医疗科技有限公司 | Medical image registration method, medical device and storage medium |
CN112116642A (en) * | 2020-09-27 | 2020-12-22 | 上海联影医疗科技股份有限公司 | Registration method of medical image, electronic device and storage medium |
CN112995528B (en) * | 2021-05-06 | 2021-09-21 | 中国工程物理研究院流体物理研究所 | Method for registering images among channels of photoelectric framing camera |
CN112995528A (en) * | 2021-05-06 | 2021-06-18 | 中国工程物理研究院流体物理研究所 | Device, system and method for image registration between channels of photoelectric framing camera |
CN113298856A (en) * | 2021-05-28 | 2021-08-24 | 上海联影医疗科技股份有限公司 | Image registration method, device, equipment and medium |
CN113298856B (en) * | 2021-05-28 | 2023-10-20 | 上海联影医疗科技股份有限公司 | Image registration method, device, equipment and medium |
CN114742869A (en) * | 2022-06-15 | 2022-07-12 | 西安交通大学医学院第一附属医院 | Brain neurosurgery registration method based on pattern recognition and electronic equipment |
CN114742869B (en) * | 2022-06-15 | 2022-08-16 | 西安交通大学医学院第一附属医院 | Brain neurosurgery registration method based on pattern recognition and electronic equipment |
Also Published As
Publication number | Publication date |
---|---|
CN104867104B (en) | 2017-12-15 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104867104B (en) | Target mouse anatomical structure collection of illustrative plates acquisition methods based on the non-rigidity registration of XCT images | |
CN106920234B (en) | Combined automatic radiotherapy planning method | |
CN106934821B (en) | Conical beam CT and CT image registration method based on ICP algorithm and B spline | |
CN104392019B (en) | High-order diffusion tensor mixed sparse imaging method for brain white matter fiber tracking | |
CN105719278B (en) | A kind of medical image cutting method based on statistics deformation model | |
CN109166133A (en) | Soft tissue organs image partition method based on critical point detection and deep learning | |
CN107680107B (en) | Automatic segmentation method of diffusion tensor magnetic resonance image based on multiple maps | |
CN110459301A (en) | Brain neuroblastoma surgical navigation method for registering based on thermodynamic chart and facial key point | |
CN109509193B (en) | Liver CT atlas segmentation method and system based on high-precision registration | |
Saleh et al. | The distance discordance metric—a novel approach to quantifying spatial uncertainties in intra-and inter-patient deformable image registration | |
CN107507195A (en) | The multi-modal nasopharyngeal carcinoma image partition methods of PET CT based on hypergraph model | |
Xie et al. | Feature‐based rectal contour propagation from planning CT to cone beam CT | |
CN109994188A (en) | Neurosurgery navigation registration test method and system based on NDI | |
CN107507189A (en) | Mouse CT image kidney dividing methods based on random forest and statistical model | |
Dong et al. | Accelerated nonrigid image registration using improved Levenberg–Marquardt method | |
CN109118455B (en) | Ancient human skull craniofacial interactive restoration method based on modern soft tissue distribution | |
CN111080658A (en) | Cervical MRI image segmentation method based on deformable registration and DCNN | |
CN114792326A (en) | Surgical navigation point cloud segmentation and registration method based on structured light | |
CN107093190A (en) | A kind of Medical Image Registration Algorithm based on multichannel chromatogram tag fusion | |
CN104166994B (en) | A kind of bone suppressing method optimized based on training sample | |
CN116650115A (en) | Orthopedic surgery navigation registration method based on UWB mark points | |
CN108564607A (en) | Three-dimensional brain map data set space calibration method and system | |
CN111127488A (en) | Method for automatically constructing patient anatomical structure model based on statistical shape model | |
Wang et al. | Using optimal transport to improve spherical harmonic quantification of complex biological shapes | |
CN110322491B (en) | Algorithm for registering deformable mouse whole-body atlas and mouse image |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
EXSB | Decision made by sipo to initiate substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171215 Termination date: 20200520 |