CN106296808B - Interactive brain fiber selection and visualization method - Google Patents
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- 239000000835 fiber Substances 0.000 title claims abstract description 211
- 210000004556 brain Anatomy 0.000 title claims abstract description 42
- 230000002452 interceptive effect Effects 0.000 title claims abstract description 12
- 238000010187 selection method Methods 0.000 title claims abstract description 8
- 238000007794 visualization technique Methods 0.000 title abstract 2
- 239000011159 matrix material Substances 0.000 claims abstract description 52
- 239000013598 vector Substances 0.000 claims abstract description 48
- 230000008447 perception Effects 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 22
- 210000003792 cranial nerve Anatomy 0.000 claims description 10
- 230000009977 dual effect Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000017105 transposition Effects 0.000 claims description 3
- 230000003321 amplification Effects 0.000 claims 1
- 238000003199 nucleic acid amplification method Methods 0.000 claims 1
- 238000012216 screening Methods 0.000 abstract description 3
- 239000006185 dispersion Substances 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 6
- 238000002597 diffusion-weighted imaging Methods 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 101000911390 Homo sapiens Coagulation factor VIII Proteins 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 102000057593 human F8 Human genes 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 210000005036 nerve Anatomy 0.000 description 2
- 229940047431 recombinate Drugs 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 210000004885 white matter Anatomy 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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Abstract
An interactive brain fiber selection and visualization method, comprising the steps of: 1) acquiring brain fiber data 2) calculating a direction vector of each fiber 3) counting the direction degree of the fiber or calculating a dispersion matrix of the fiber to obtain a judgment condition 4) screening the fiber by utilizing the judgment condition so as to obtain a local fiber graph with better spatial perception.
Description
Technical field
It is a kind of interactive brain fiber selection and method for visualizing present document relates to the research of cranial nerve fiber.
Background technique
The development of nmr imaging technique, diffusion-weighted imaging (DWI), diffusion tensor technology (DTI), height
The non-invasive imaging techniques such as angular resolution diffusion imaging technology (HARDI) come out one after another .DWI be a kind of measurement spin proton it is microcosmic with
The technology that seat in the plane is moved, by measurement biological tissue to the obstruction situation of hydrone Brownian movement, to explore biological properties
.DTI it is to introduce tensor on the basis of DWI, to there is directional information, it is fine becomes a kind of common detection cerebral white matter
The technology of structure is tieed up, however DTI model is limited by Gaussian hypothesis, and a machine direction can only be provided in each voxel
Information, can not disclose fiber intersection the case where .HARDI technology be to be developed by DTI technology, it is sampled using spherical surface, together
When assume that hydrone is Gauss disperse in tissue, can be used to describe the intersection of fiber, the states such as merge, by correlative study person
Pay close attention to
DTI and HARDI data can indicate one group of fibre bundle, this process is known as fiber tracking fiber tracking can be
Three-dimensional space shows the distribution of brain fibers and connection relationship is intensive line drawing process to the displaying of fiber, three-dimensional space
Brain fiber visually has that complicated confusion, fiber information obtain balance with hardly possible is blocked.People see brain fiber
When examining, since there are more fibers to block on its surface, the overall structure of brain fiber can not be often observed directly, therefore, it is difficult to
Full brain fiber explore and analyzes
In conclusion in order to reduce the visual confusion situation of brain fiber in three dimensions, need to brain fiber into
Row further screening and visualization.Reasonable screening mode, which can be removed, causes the part blocked to brain fibre structure, retains brain
Fiber overall structure.Good effect of visualization can preferably show the spatial relationship of brain fiber, and people is helped preferably to manage
The institutional framework for solving brain fiber, to preferably be explored to brain fiber.
Summary of the invention
In order to improve the space structure perceived effect to brain fiber, the present invention provides a kind of selections of the brain fiber of interactive mode
With method for visualizing.
The selection of interactive brain fiber and method for visualizing that the present invention designs, comprising the following steps:
1) initial data, is imported, data are modeled, the path of fiber is tracked out；
2), due to the self attributes of fiber data, all fibres path is simultaneously non-isometric, for fiber spy different in size
Property, fiber selection mode is divided into two classes by us, and one kind is the fiber selection method based on scatter matrix, and this method focuses on list
The global direction attribute of root fiber, does not consider the length of fiber；It is another kind of, it is the fiber selecting party based on local direction vector
Method, this method focus on the local direction attribute of fiber, combine fibre length.
2.1) the fiber selection based on scatter matrix:
According to the direction vector of adjacent node on fiber path, the scatter matrix of every fiber path is calculated.Spread square
Battle array characteristic value can describe the directional information of the fiber, then construct machine direction parameter according to the characteristic value of scatter matrix, from
And the fiber of different directions is selected.
2.2) the fiber selection based on local direction vector:
Calculate fiber path on adjacent node direction vector, to direction vector carry out it is unitization, by the direction to
Three space coordinates of amount determine machine direction, and brain machine direction is divided into three classes；Direction initialization length threshold, passes through
The mode for adjusting threshold value, selects the fiber in some direction.
3), fiber is color coded, changes the color of fiber according to the variation in its direction, shows fiber
Direction tendency, so that nerve fibre be made to be easier in three-dimensional space to perception.
4) the integrally-built fiber of cranial nerve fiber can be described by, extracting, and then recombinate the fiber of different directions,
The fiber for repainting full brain field renders fiber by adjusting color and opacity, while passing through rotation, scaling
Small equal operation carries out virtual interactive interface to fiber, preferably shows the spatial relationship of brain fiber.
As a kind of preferred scheme: the step 2.1) includes following steps:
Step 1 calculates the local direction vector n between brain fiber adjacent nodei, formula are as follows:
ni=(xi-xi+1,yi-yi+1,zi-zi+1) (1)
Wherein, (xi,yi,zi) and (xi+1,yi+1,zi+1) respectively indicate the xyz space coordinate of i-th Yu i+1 node.
Meanwhile if a total of N number of node on a fiber, need to calculate the direction vector between an all above node of fiber
(n0,n1,n2,......nN-2,nN-1), and thus constitute the matrix of a direction vector.
Step 2 calculates the scatter matrix of every fiber.Its formula is as follows:
Wherein, niIndicate i-th point of direction vector,Indicate that the transposition of i-th point of direction vector, N indicate current
The number of nodes of fiber.
After Step 3 obtains scatter matrix, three characteristic value β of scatter matrix are calculated1,β2,β3, each brain fiber distribution
Three characteristic values of matrix can describe the xyz directional information of the fiber；Since the characteristic value of scatter matrix may be plural number, institute
With β herein1,β2,β3Respectively indicate the real part of three characteristic values.
Step 4 repeats step 1), step 2) and step 3), until calculating the corresponding characteristic value of all fibres.
Discriminant parameter C corresponding to scatter matrix is calculated in Step 5l, calculation formula is as follows:
Wherein, ClValue range be (- 1,1), for describing the general direction information of current fibre.
Step 6 is by ClValue falls within threshold interval (Clstart,Clend) fiber take out, wherein (Clstart,Clend) belong to (-
1,1), ClstartIndicate taken ClThe minimum value in section, ClendIndicate taken ClThe maximum value in section.The choosing of different directions fiber
It selects, it can be by adjusting section (Clstart,Clend) realize.
As a kind of preferred preferred embodiment: the step 2.2) the following steps are included:
Step 1 of the Step 1 with step 2.1).
The local direction vector n that Step 2 obtains formula (1)iIt is standardized, formula are as follows:
Wherein,Calculation formula are as follows:
Step 3 gives three variable (Uis,Uap,Ulr) x, the fiber information in tri- directions y, z, wherein U are stored respectivelyis
For being counted to x-axis direction fiber, UapFor being counted to y-axis direction fiber, UlrFor to z-axis direction fiber into
Row counts.In addition, giving two parameter w1, w2(w2Much larger than w1) for pairDirection determining is carried out, determines formula are as follows:
Step 4 traverses all fibers, using formula (7), carries out direction to all local direction vectors of every fiber and sentences
It is fixed.Finally use (Uis,Uap,Ulr) the direction that is referred to of maximum value, the Main way as current fibre.After traversal, entirely
Brain fiber will be divided into three classes with three directions.
Step 5 given threshold deg, deg belong to (0, maxN), wherein maxNIndicate maximum fiber nodes number.By to threshold
Value deg is adjusted, and screens to the fiber in some direction.
The reference direction DUAL PROBLEMS OF VECTOR MAPPING that Step 6 obtains formula (6) is to RGB color model, formula are as follows:
Wherein, Rmax=Gmax=Bmax=255.
Beneficial effects of the present invention: two different fiber selection methods are proposed.Start on the whole, by scatter matrix
It is dissolved into fiber selection, as a kind of foundation.Start with from part, it is right according to the direction vector of each node of every fiber
Fiber is selected.Two methods can be selected voluntarily according to demand, and unified target is the sense in order to improve user to fiber
Know.
Detailed description of the invention
Fig. 1 is the drafting effect picture of full brain fiber of the invention
Fig. 2 is the system diagram of the invention classified using scatter matrix calculated value
Fig. 3 is the effect picture of the invention screened using scatter matrix
Fig. 4 is flow chart of the invention
Fig. 5 is the effect picture of the invention screened using partial vector
Specific embodiment:
System interface of the invention is to carry out front end by Qt to write, and data processing is carried out by C++, and drafting passes through
OpenGL is completed.
The following further describes the present invention with reference to the drawings.
Referring to Fig.1~Fig. 5, it is a kind of interactive mode brain fiber selection and method for visualizing, specifically includes the following steps:
Fig. 1 is a kind of drafting carried out by OpenGL for full brain data.Thus it can be drawn on the basis of any given
Brain fiber represented by data.However, it is possible to significantly see that fiber in space, can not when quantity reaches certain quantity
The meeting avoided generates blocking, and the perception to it of user can sharply decline.Developer once uses illumination, and color coding resists
The operations such as sawtooth fail to get a desired effect.Therefore propose the method for visualizing for interacting selection and obtained compared with
Good effect, improves the spatial perception of fiber.
Fig. 2 is made based on above-mentioned method one, and classified based on space scatter matrix to fiber one is visual
The screenshot of change system.Fiber is pre-processed, the scatter matrix of every fiber in space is calculated, it, can be with according to formula
The value of an expression scatter matrix corresponding to every fiber is obtained, the value is among section [- 1.0,1.0], as classification
Foundation.Fiber data can be directed respectively into first into upper left and the view of lower-left 2, available 2 accessory fibers is drawn
Result.Although the data of this 2 views import simultaneously, but be independent from each other.User can be simultaneously to the two
View is interacted and is independent of each other.But also can freely be selected according to fiber scatter matrix obtained by calculating, from
And the result of different selections is obtained simultaneously.By merging button, the result that two select can be merged, obtain effect more
Good merging figure.Finally using opacity and the scheme of colour by selecting, final merging figure can be obtained.It is empty to merge figure
Between sensing capability obviously get a promotion, not only reduce blocking and by comparison, allow structure all the more clear of brain fiber.
Fig. 3 is the specific stage diagram that brain fiber is classified according to different scatter matrixes.Firstly, we utilize biography
The color matching of the scheme of colour of system, i.e. RGB represents direction and matches colors.By figure it is recognised that we are when fixed viewpoint, utilize
The available sorted data in the value section of different scatter matrixes.For example, fiber obtains whole when cl value is closer to 1
Specific gravity shared by front and back distribution (being based on screen direction) namely red of the body trend in space is more and more.Other
Situation is also similarly.
Fig. 4 illustrates the realization process of the fiber selection method based on scatter matrix by way of flow chart.It obtains first
After obtaining data, every fiber is made of several nodes, so needing to calculate the direction between each node of every fiber
One direction vector matrix of vector and composition.After this, it is calculated based on obtained direction vector matrix and scatter matrix
The available scatter matrix of formula.Since direction vector possesses 3 values, so obtained scatter matrix is the matrix of 3*3, also
It is to have said 3 characteristic values.Utilize the available foundation cl that we are used to be judged later of this 3 characteristic values.Cl be by
A specific properties being calculated, are added in the middle of original data.It, can be to original fiber again by given threshold
It is selected.
Fig. 5 carries out the effect picture of selection by local direction vector.Maximum feature is exactly easy compared with scatter matrix,
It does not need to calculate fiber data.Because its effect is a summation according to local direction, effect is more coarse,
As a result relatively simple.User can should reach according to the x of formulation, the required fiber selected of tri- direction definitions of y, z
Degree, be defined as deg.The bigger feature in this direction just represented of the value of deg is all the more obvious.
A kind of interactive brain fiber selection of the present invention and method for visualizing, include the following steps:
1) initial data, is imported, data are modeled, the path of fiber is tracked out；
2), due to the self attributes of fiber data, all fibres path is simultaneously non-isometric, for fiber spy different in size
Property, fiber selection mode is divided into two classes by us, and one kind is the fiber selection method based on scatter matrix, and this method focuses on list
The global direction attribute of root fiber, does not consider the length of fiber；It is another kind of, it is the fiber selecting party based on local direction vector
Method, this method focus on the local direction attribute of fiber, combine fibre length；
2.1) the fiber selection based on scatter matrix；
According to the direction vector of adjacent node on fiber path, the scatter matrix S of every fiber path is calculated；Spread square
Battle array characteristic value can describe the directional information of the fiber, then construct machine direction parameter according to the characteristic value of scatter matrix, from
And the fiber of different directions is selected；；
Step 21 calculates the local direction vector n between brain fiber adjacent nodei, formula are as follows:
ni=(xi-xi+1,yi-yi+1,zi-zi+1) (1)
Wherein, (xi,yi,zi) and (xi+1,yi+1,zi+1) respectively indicate the xyz space coordinate of i-th Yu i+1 node；
Meanwhile if a total of N number of node on a fiber, need to calculate the direction vector between an all above node of fiber
(n0,n1,n2,......nN-2,nN-1), and thus constitute the matrix n of a direction vector；
Step2 2 calculates the scatter matrix S of every fiber；Its formula is as follows:
Wherein, niIndicate i-th point of direction vector,Indicate that the transposition of i-th point of direction vector, N indicate current
The number of nodes of fiber；
After Step 23 obtains scatter matrix, three characteristic value β of scatter matrix are calculated1,β2,β3, each brain fiber dissipate
Three characteristic values of cloth matrix can describe the xyz directional information of the fiber；Since the characteristic value of scatter matrix may be plural number,
So β herein1,β2,β3Respectively indicate the real part that each fiber corresponds to three characteristic values of scatter matrix S；
Step 24 repeats step 1), step 2) and step 3), until calculating the corresponding characteristic value of all fibres；
Discriminant parameter C corresponding to scatter matrix is calculated in Step2 5l, calculation formula is as follows:
Wherein, ClValue range be (- 1,1), for describing the general direction information of current fibre；
Step 26 is by ClValue falls within threshold interval (Clstart,Clend) fiber take out, wherein (Clstart,Clend) belong to
(- 1,1), ClstartIndicate taken ClThe minimum value in section, ClendIndicate taken ClThe maximum value in section；The choosing of different directions fiber
It selects, it can be by adjusting section (Clstart,Clend) realize；
2.2) the fiber selection based on local direction vector:
Calculate fiber path on adjacent node direction vector, to direction vector carry out it is unitization, by the direction to
Three space coordinates of amount determine machine direction, and brain machine direction is divided into three classes；Direction initialization length threshold, passes through
The mode for adjusting threshold value, selects the fiber in some direction；
3), fiber is color coded, changes the color of fiber according to the variation in its direction, shows fiber
Direction tendency, so that nerve fibre be made to be easier in three-dimensional space to perception；
4) the integrally-built fiber of cranial nerve fiber can be described by, extracting, and then recombinate the fiber of different directions,
The fiber for repainting full brain field renders fiber by adjusting color and opacity, while passing through rotation, scaling
Small equal operation carries out virtual interactive interface to fiber, preferably shows the spatial relationship of brain fiber；
Step4 1 is the same as step step2 1；
The local direction vector n that Step4 2 obtains formula (1)iIt is standardized, formula are as follows:
Wherein,Calculation formula are as follows:
Step 43 gives three variable (Uis,Uap,Ulr) x, the fiber information in tri- directions y, z, wherein U are stored respectivelyis
For being counted to x-axis direction fiber, UapFor being counted to y-axis direction fiber, UlrFor to z-axis direction fiber into
Row counts；In addition, giving two parameter w1, w2(w2Much larger than w1) for pairDirection determining is carried out, determines formula are as follows:
Step44 traverses all fibers, using formula (7), carries out direction to all local direction vectors of every fiber and sentences
It is fixed；Finally use (Uis,Uap,Ulr) the direction that is referred to of maximum value, the Main way as current fibre；After traversal, entirely
Brain fiber will be divided into three classes with three directions；
Step 45 given threshold deg, deg belong to (0, maxN), wherein maxNIndicate maximum fiber nodes number；By right
Threshold value deg is adjusted, and screens to the fiber in some direction；
The reference direction DUAL PROBLEMS OF VECTOR MAPPING that Step 46 obtains formula (6) is to RGB color model, formula are as follows:
Wherein, Rmax=Gmax=Bmax=255.
Claims (1)
1. a kind of interactive mode brain fiber selection and method for visualizing, include the following steps:
1) initial data, is imported, data are modeled, the path of fiber is tracked out；
2), due to the self attributes of fiber data, all fibres path is simultaneously non-isometric, will for fiber characteristic different in size
Fiber selection mode is divided into two classes, and one kind is the fiber selection method based on scatter matrix, and this method focuses on single fiber
Global direction attribute, does not consider the length of fiber；It is another kind of, it is the fiber selection method based on local direction vector, this method
The local direction attribute for focusing on fiber, combines fibre length；
2.1) the fiber selection based on scatter matrix；
According to the direction vector of adjacent node on fiber path, the scatter matrix S of every fiber path is calculated；Scatter matrix S
Characteristic value can describe the directional information of the fiber, then construct machine direction parameter according to the characteristic value of scatter matrix S, thus
The fiber of different directions is selected；
Step 21 calculates the local direction vector n between cranial nerve fiber adjacent nodei, formula are as follows:
ni=(xi-xi+1,yi-yi+1,zi-zi+1) (1)
Wherein, (xi,yi,zi) and (xi+1,yi+1,zi+1) respectively indicate the xyz space coordinate of i-th Yu i+1 node；Together
When, if a total of N number of node on a fiber, needs to calculate the direction vector (n between an all above node of fiber0,
n1,n2,......nN-2,nN-1), and thus constitute the matrix n of a direction vector；
Step 22 calculates the scatter matrix S of every fiber；Its formula is as follows:
Wherein, niIndicate i-th point of direction vector,Indicate that the transposition of i-th point of direction vector, N indicate current fibre
Number of nodes；
After Step 23 obtains scatter matrix S, three characteristic value β of scatter matrix S are calculated1,β2,β3, every cranial nerve fiber
Three characteristic values of scatter matrix S can describe the xyz directional information of the fiber；Since the characteristic value of scatter matrix S may be
Plural number, so, β herein1,β2,β3Respectively indicate the real part that every fiber corresponds to three characteristic values of scatter matrix S；
Step 24 repeats step 1), step 2) and step 3), until calculating the corresponding characteristic value of all fibres；
Discriminant parameter C corresponding to scatter matrix is calculated in Step 25l, calculation formula is as follows:
Wherein, ClValue range be (- 1,1), for describing the general direction information of current fibre；
Step 26 is by ClValue falls within threshold interval (Clstart,Clend) fiber take out, wherein (Clstart,Clend) belong to (- 1,
1), ClstartIndicate taken ClThe minimum value in section, ClendIndicate taken ClThe maximum value in section；The selection of different directions fiber,
It can be by adjusting section (Clstart,Clend) realize；
2.2) the fiber selection based on local direction vector:
The direction vector for calculating adjacent node on fiber path, is standardized direction vector, passes through direction vector
Three space coordinates determine machine direction, and cranial nerve machine direction is divided into three classes；Direction initialization length threshold, passes through
The mode for adjusting threshold value, selects the fiber in some direction；
3), fiber is color coded, changes the color of fiber according to the variation in its direction, shows the direction of fiber
Tendency, so that cranial nerve fiber be made to be easier in three-dimensional space to perception；
4) the integrally-built fiber of cranial nerve fiber can be described by, extracting, and then be recombinated the fiber of different directions, again
The fiber for drawing full brain field renders fiber by adjusting color and opacity, while passing through rotation, amplification, contracting
Small operation carries out virtual interactive interface to fiber, preferably shows the spatial relationship of cranial nerve fiber；
Step 41 is the same as step step 21；
The local direction vector n that Step 42 obtains formula (1)iIt is standardized, formula are as follows:
Wherein,Calculation formula are as follows:
Step 43 gives three variable (Uis,Uap,Ulr) x, the fiber information in tri- directions y, z, wherein U are stored respectivelyisWith
It is counted in x-axis direction fiber, UapFor being counted to y-axis direction fiber, UlrFor being carried out to z-axis direction fiber
It counts；In addition, giving two parameter w1, w2For rightCarry out direction determining, w2Much larger than w1, determine formula are as follows:
Step 44 traverses all fibers, using formula (7), carries out direction to all local direction vectors of every fiber and sentences
It is fixed；Finally use (Uis,Uap,Ulr) the direction that is referred to of maximum value, the Main way as current fibre；After traversal, entirely
The cranial nerve fiber of brain will be divided into three classes with three directions；
Step 45 given threshold deg, deg belong to (0, maxN), wherein maxNIndicate maximum fiber nodes number；By to threshold value
Deg is adjusted, and screens to the fiber in some direction；
The reference direction DUAL PROBLEMS OF VECTOR MAPPING that Step 46 obtains formula (6) is to RGB color model, formula are as follows:
Wherein, Rmax=Gmax=Bmax=255.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102204819A (en) * | 2011-04-06 | 2011-10-05 | 浙江工业大学 | Swarm optimization-based alba fiber tracking method |
| CN104740781A (en) * | 2015-04-10 | 2015-07-01 | 中国医学科学院生物医学工程研究所 | Vector transcranial magnetic stimulation method on basis of trend of nerve fibers |
| JP2015165367A (en) * | 2014-03-03 | 2015-09-17 | 国立大学法人九州工業大学 | Biological tissue simulation method and apparatus thereof |
| CN104933759A (en) * | 2015-06-03 | 2015-09-23 | 浙江工业大学 | Human brain tissue high-dimension visualization method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102204819A (en) * | 2011-04-06 | 2011-10-05 | 浙江工业大学 | Swarm optimization-based alba fiber tracking method |
| JP2015165367A (en) * | 2014-03-03 | 2015-09-17 | 国立大学法人九州工業大学 | Biological tissue simulation method and apparatus thereof |
| CN104740781A (en) * | 2015-04-10 | 2015-07-01 | 中国医学科学院生物医学工程研究所 | Vector transcranial magnetic stimulation method on basis of trend of nerve fibers |
| CN104933759A (en) * | 2015-06-03 | 2015-09-23 | 浙江工业大学 | Human brain tissue high-dimension visualization method |
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