CN104134205A - Fiber classification method based on spatial similarity and system realizing fiber classification method - Google Patents

Abstract

The invention provides a fiber classification method based on spatial similarity and a system realizing the fiber classification method. The fiber classification method comprises the following steps that: a data processing and fiber tracking module is adopted, and data importing, voxel modeling and probabilistic fiber outlet path tracking are carried out; a color coding, spatial mapping and fiber clustering module is adopted, and fiber beams are subjected to color coding in the spatial direction; and an interaction module is adopted, and the fiber is redrawn. The system realizing the fiber classification method comprises the data processing and fiber tracking module, the color coding, spatial mapping and fiber clustering module and the interaction module used for redrawing the fiber.

Description

A kind of Fibre sorting method and system thereof based on space similarity

Technical field

The present invention relates to brain fiber research, is a kind of Fibre sorting method and system thereof based on space similarity

Background technology

DW-MRI (Diffusion weighted MR imaging) is at present unique a kind of noninvasive diagnostic techniques in the structural research of live body brain white matter integrity and the connective exploration of brain field.By following the trail of the diffusion motion of hydrone in biosome, the method intuitively fiber between display brain function district connects, and is subject to paying close attention to of neuro-surgery researcher.

For tracer fiber direction, diffusion tensor imaging (DTI) method is suggested estimates that the spreading probability of hydrone distributes.In view of the defect of DTI method itself, in the voxel that has many fibers to intersect, DTI can not expressed intact go out plurality of fibers directional information.But fine angular resolution diffusion imaging (HARDI) technology has well solved and has had plurality of fibers cross-cutting issue in voxel.Due to brain fiber microstructure complexity, DW-MRI data are uncertain, accurately set up overall fiber optimization index Accurate Reconstruction nerve fibre and remain a difficult problem.The probability track algorithm of describing fiber orientation with probability density function is to solve at present the uncertain effective means of machine direction, expand as much as possible at random may moving towards of fiber by a large amount of particles according to probability density direction, with the credibility of probability description fiber.It has improved the problem that Deterministic Methods is brought to a certain extent, and what also easily produce that a large amount of " excessively " tracking causes does not expect fiber simultaneously.

On the other hand, in order better to show fiber path tracking, need to fiber is out visual.Probability tracking results is not all credible, and some fiber path does not meet actual fibers trend, is wrong.After fiber cluster, the low credible fiber of the rejecting of man-machine interactively, carries out credible height fiber visually to seem of crucial importance.

Comprehensively above-mentioned, how fiber to be classified, simultaneously underwriter's work interactive selection, obtains the fiber of high confidence level, and realizing the structure distribution that meets human brain fiber becomes major issue urgently to be resolved hurrily of current brain fiber art.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of novel Fibre sorting method, the low believable fiber of mutual rejecting, thus obtain high credible fiber.

A Fibre sorting method based on space similarity, comprises the steps:

Step 1, data importing, voxel modeling, probability is followed the tracks of out fiber path;

Step 2, carries out color coding by fibrous bundle according to direction in space; Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN (a kind of clustering algorithm based on density) method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Step 3, because the low probability fiber of following the tracks of out is insincere, rejects in the multifilament of need to comforming; Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be mutual, rejects lower than threshold value fiber, and again draws out high believable fiber.

As preferred a kind of scheme: described step 2) comprise the following steps:

Step2.1 color coding: fiber sampled point is used with vector represent, for ensure color map be on the occasion of, use represent Seed Points, therefore DUAL PROBLEMS OF VECTOR MAPPING is that RGB relation is as follows;

$\left\{\begin{array}{c}R=\frac{1}{2}(1+\frac{x}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})R_{\max }\\ G=\frac{1}{2}(1+\frac{y}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})G_{\max }\\ B=\frac{1}{2}(1+\frac{z}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})B_{\max }\end{array}\right.$

Wherein, R _max=G _max=B _max=255

Step2.2 spatial mappings: by fiber maps to pixelbar.According to Bayesian fiber tracking, try to achieve the mean value of fiber orientation with square error p _mSE:

$p_{\mathrm{MSE}}=\frac{1}{n}\sqrt{{\mathrm{\Σ}}_{k=1}^n{(p_i-\stackrel{\‾}{p})}^2}$

Wherein, p _ibe the overall probability of i root fiber, n is the sampling number of whole piece fiber.

P _mSEthe opacity value of each pixelbar will be directly mapped as.A corresponding fiber of pixelbar, on fiber, all sampled points are all mapped on corresponding pixelbar one by one.On pixelbar, the directional information that color coding is fiber, pixelbar length is mapped to the length of fiber itself, and the opacity of pixelbar is mapped as the entire probability distribution of fiber.Like this, three-dimensional space fiber, through mapping, transfers the pixelbar on 2D to originally;

Step2.3 fiber cluster: set radius and density threshold, adopt DBSCAN to fiber cluster.We get radius is 2.5, and density threshold is 20, and space length is less than or equal to 2.5 pixelbar and will gathers for cluster, and fiber number is less than 20 the noise that is regarded as within radius is less than 2.5 scope, these data by not by cluster to other bunch in.Interfibrous distance B is defined as follows,

Suppose that two pixelbar up-sampling points are respectively M and N, fiber distance B:

$D=\frac{{\mathrm{\Σ}}_{i=1}^n{d}_i}{\frac{1}{2}(M+N)}$

Wherein: n=min (M, N), d _iit is the Euclidean distance between i sampled point on two bars of fibers;

Step2.4 is through above step, and path is similar, and the close fiber of direction by cluster together, is very clearly seen the fiber of high likelihood on pixelbar.

As preferred a kind of scheme: described step 3) comprise the following steps:

Step3.1 arranges opacity value, and mutual selects higher than the respective fiber that opacity value is set on the pixelbar generating, and weeds out the threshold value of the difference of two squares higher than definition;

Step3.2, according to above setting, regenerates the fibrous bundle of picking out, and obtains fiber result with a high credibility, and program end of run, logs off.

The system that realizes method of the present invention, comprises

Data processing and fiber track module: comprise data importing, voxel modeling, probability is followed the tracks of out fiber path;

Color coding, spatial mappings and fiber cluster module: fibrous bundle is carried out to color coding according to direction in space.Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, and the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Interactive module also redraws fiber: because the low probability fiber of following the tracks of out is insincere, in the multifilament of need to comforming, reject.Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be freely mutual, rejects lower than threshold value fiber, repaints out high believable fiber.

Compared with prior art, the invention has the beneficial effects as follows: adopt DBSCAN algorithm to realize direction close, the cluster of the similar fiber in path, has separated the believable fiber path of height according to space similarity, the low probability fiber of mutual rejecting, shows high believable fiber.

Brief description of the drawings

Fig. 1 is Fibre sorting method based on space similarity and the overall construction drawing of system thereof;

Fig. 2 is the Fibre sorting method program process flow diagram of space similarity;

Fig. 3 is the outline flowchart of Fibre sorting method;

Fig. 4 is pixelbar figure corresponding after classification;

Fig. 5 is the high credible fiber process schematic diagram of interactive selection.

Embodiment

Below in conjunction with accompanying drawing, the present invention is further described.

With reference to Fig. 1～Fig. 5, a kind of Fibre sorting method based on space similarity, specifically comprises the following steps:

Step 1, data importing, voxel modeling, probability is followed the tracks of out fiber path;

Step 2, carries out color coding by fibrous bundle according to direction in space; Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN (a kind of clustering algorithm based on density) method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Step 3, because the low probability fiber of following the tracks of out is insincere, rejects in the multifilament of need to comforming; Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be mutual, rejects lower than threshold value fiber, and again draws out high believable fiber.

The alleged DBSCAN method of the present invention is a kind of clustering algorithm based on density, refer to M.Ester, H.P.Kriegel, J.Sander, X.Xu.A density-based algorithm for discovering clusters in large spatial databases with noise.Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), 226 – 231,1996.

As preferred a kind of scheme: described step 2) comprise the following steps:

Step2.1 color coding: fiber sampled point is used with vector represent, for ensure color map be on the occasion of, use represent Seed Points, therefore DUAL PROBLEMS OF VECTOR MAPPING is that RGB relation is as follows;

$\left\{\begin{array}{c}R=\frac{1}{2}(1+\frac{x}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})R_{\max }\\ G=\frac{1}{2}(1+\frac{y}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})G_{\max }\\ B=\frac{1}{2}(1+\frac{z}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})B_{\max }\end{array}\right.$

Wherein, R _max=G _max=B _max=255

Step2.2 spatial mappings: by fiber maps to pixelbar.According to Bayesian (Bayes) fiber tracking, try to achieve the mean value of fiber orientation with square error pMSE:

$p_{\mathrm{MSE}}=\frac{1}{n}\sqrt{{\mathrm{\Σ}}_{k=1}^n{(p_i-\stackrel{\‾}{p})}^2}$

Wherein, p _ibe the overall probability of i root fiber, n is the sampling number of whole piece fiber.

P _mSEthe opacity value of each pixelbar will be directly mapped as.A corresponding fiber of pixelbar, on fiber, all sampled points are all mapped on corresponding pixelbar one by one.On pixelbar, the directional information that color coding is fiber, pixelbar length is mapped to the length of fiber itself, and the opacity of pixelbar is mapped as the entire probability distribution of fiber.Like this, three-dimensional space fiber, through mapping, transfers the pixelbar on 2D to originally;

Step2.3 fiber cluster: set radius and density threshold, adopt DBSCAN to fiber cluster.We get radius is 2.5, and density threshold is 20, and space length is less than or equal to 2.5 pixelbar and will gathers for cluster, and fiber number is less than 20 the noise that is regarded as within radius is less than 2.5 scope, these data by not by cluster to other bunch in.Interfibrous distance B is defined as follows,

Suppose that two pixelbar up-sampling points are respectively M and N, fiber distance B:

$D=\frac{{\mathrm{\Σ}}_{i=1}^n{d}_i}{\frac{1}{2}(M+N)}$

Wherein: n=min (M, N), d _iit is the Euclidean distance between i sampled point on two bars of fibers;

Step2.4 is through above step, and path is similar, and the close fiber of direction by cluster together, is very clearly seen the fiber of high likelihood on pixelbar.

As preferred a kind of scheme: described step 3) comprise the following steps:

Step3.1 arranges opacity value, and mutual selects higher than the respective fiber that opacity value is set on the pixelbar generating, and weeds out the threshold value of the difference of two squares higher than definition;

Step3.2, according to above setting, regenerates the fibrous bundle of picking out, and obtains fiber result with a high credibility, and program end of run, logs off.

Bayesian fiber tracking of the present invention, it is Bayes's fiber tracking, refer to O.Friman, G.Farneback, C.Westin.A Bayesian Approach for Stochastic White Matter Tractograph.IEEE Transactions on Medical Imaging.25 (8): 965-978,2006.

The system of method of the present invention, comprises

Data processing and fiber track module: comprise data importing, voxel modeling, probability is followed the tracks of out fiber path;

Color coding, spatial mappings and fiber cluster module: fibrous bundle is carried out to color coding according to direction in space.Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, and the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Interactive module also redraws fiber: because the low probability fiber of following the tracks of out is insincere, in the multifilament of need to comforming, reject.Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be freely mutual, rejects lower than threshold value fiber, repaints out high believable fiber.

In Fig. 1, Fibre sorting method system global structure figure based on space similarity is the overall plan of this method, data processing section uses C Plus Plus to realize, and Fibre sorting method is utilized GLSL language compilation, and whole program adopts Qt to build and realize at VS2008 platform.

In Fig. 2, the Fibre sorting method overall construction drawing based on space similarity.For DW-MRI data, by high angular resolution technology (HARDI) modeling voxel, can overcome the shortcoming that fiber intersection crotch fiber can not make a distinction, utilize uncertain fiber tracking algorithm, follow the tracks of out possible fiber path from Seed Points.Then the fiber of these tentatively being followed the tracks of out carries out cluster, makes to separate according to similarity between them.For this reason, it is upper that three-dimensional fibrous structure is mapped to two-dimentional pixelbar by we, carries out DBSCAN cluster according to interfibrous space length.Afterwards, mean variance is set, the mutual corresponding fiber of selection on pixelbar, weeds out undesirable fiber, obtains high credible fiber path.

In Fig. 3, the step of Fibre sorting method has: first, choose Seed Points, the color of Seed Points is set.The fiber of following the tracks of out from Seed Points extends to surrounding, calculates the direction between neighbouring sample point on fiber, and according to the direction of fiber, the color space that contrast is set, carries out color coding to fiber.

Then, three-dimensional fiber is mapped on two-dimentional pixelbar.Fiber is one-to-one relationship with pixelbar, each sampled point will be mapped on pixelbar, thereby the length of pixelbar has represented the length of respective fiber, the opacity of pixelbar is mapped as the entire probability distribution of fiber, certainly, color is also mapped to pixelbar above one by one accordingly.According to the pixelbar after mapping, set radius and density threshold, adopt DBSCAN method to carry out cluster to fiber.We get radius is 2.5, and density threshold is 20, and space length is less than or equal to 2.5 pixelbar and will gathers for cluster, and fiber number is less than 20 the noise that is regarded as within radius is less than 2.5 scope, these data by not by cluster to other bunch in.Interfibrous distance B is defined as follows,

Suppose that two pixelbar up-sampling points are respectively M and N, fiber distance B:

$D=\frac{{\mathrm{\Σ}}_{i=1}^n{d}_i}{\frac{1}{2}(M+N)}$

Wherein: n=min (M, N), d _iit is the Euclidean distance between i sampled point on two bars of fibers;

So far, path is similar, and the fiber that direction is close will be polymerized to a pile, very clearly sees high believable fiber on pixelbar.

In Fig. 4, show the distribution of fiber after cluster.According to separating three fibers in fibre space similarity figure, what in first box, represent is the fiber that left and right is moved towards, and what in second frame, represent is the fiber moving towards up and down, and what in the 3rd frame, represent is the fiber of front and back trend.

In Fig. 5, according to classification results, the selected a certain specific difference of two squares, the mutual satisfactory fiber of selection on pixelbar, thus filter out those high credible fibers.

Claims (4)

1. the Fibre sorting method based on space similarity, is characterized in that:

Step 1, data importing, voxel modeling, probability is followed the tracks of out fiber path;

Step 2, carries out color coding by fibrous bundle according to direction in space; Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN (a kind of clustering algorithm based on density) method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Step 3, because the low probability fiber of following the tracks of out is insincere, rejects in the multifilament of need to comforming; Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be mutual, rejects lower than threshold value fiber, and again draws out high believable fiber.

2. the Fibre sorting method based on space similarity according to claim 1, is characterized in that: described step 2) comprise the following steps,

Step2.1 color coding: fiber sampled point is used with vector represent, for ensure color map be on the occasion of, use represent Seed Points, therefore DUAL PROBLEMS OF VECTOR MAPPING is that RGB relation is as follows;

$\left\{\begin{array}{c}R=\frac{1}{2}(1+\frac{x}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})R_{\max }\\ G=\frac{1}{2}(1+\frac{y}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})G_{\max }\\ B=\frac{1}{2}(1+\frac{z}{{(x^2+y^2+z^2)}^{\frac{1}{2}}})B_{\max }\end{array}\right.---\left(1\right)$

Wherein, R _max=G _max=B _max=255

Step2.2 spatial mappings: by fiber maps to pixelbar.According to Bayesian fiber tracking, try to achieve the mean value of fiber orientation with square error p _mSE:

$p_{\mathrm{MSE}}=\frac{1}{n}\sqrt{{\mathrm{\Σ}}_{k=1}^n{(p_i-\stackrel{\‾}{p})}^2}---\left(2\right)$

Wherein, p _ibe the overall probability of i root fiber, n is the sampling number of whole piece fiber.

P _mSEthe opacity value of each pixelbar will be directly mapped as.A corresponding fiber of pixelbar, on fiber, all sampled points are all mapped on corresponding pixelbar one by one.On pixelbar, the directional information that color coding is fiber, pixelbar length is mapped to the length of fiber itself, and the opacity of pixelbar is mapped as the entire probability distribution of fiber.Like this, three-dimensional space fiber, through mapping, transfers the pixelbar on 2D to originally;

Step2.3 fiber cluster: set radius and density threshold, adopt DBSCAN to fiber cluster.We get radius is 2.5, and density threshold is 20, and space length is less than or equal to 2.5 pixelbar and will gathers for cluster, and fiber number is less than 20 the noise that is regarded as within radius is less than 2.5 scope, these data by not by cluster to other bunch in.Interfibrous distance B is defined as follows,

Suppose that two pixelbar up-sampling points are respectively M and N, fiber distance B:

$D=\frac{{\mathrm{\Σ}}_{i=1}^n{d}_i}{\frac{1}{2}(M+N)}---\left(3\right)$

Wherein: n=min (M, N), d _iit is the Euclidean distance between i sampled point on two bars of fibers;

Step2.4 is through above step, and path is similar, and the close fiber of direction by cluster together, is very clearly seen the fiber of high likelihood on pixelbar.

3. the Fibre sorting method based on space similarity according to claim 1, is characterized in that: described step 3)

Comprise the following steps,

Step3.1 arranges opacity value, and mutual selects higher than the respective fiber that opacity value is set on the pixelbar generating, and weeds out the threshold value of the difference of two squares higher than definition;

Step3.2, according to above setting, regenerates the fibrous bundle of picking out, and obtains fiber result with a high credibility, and program end of run, logs off.

4. the system that realizes method claimed in claim 1, is characterized in that: comprise

Data processing and fiber track module: comprise data importing, voxel modeling, probability is followed the tracks of out fiber path;

Color coding, spatial mappings and fiber cluster module: fibrous bundle is carried out to color coding according to direction in space.Then,, according to the parameters of fiber, 3D fiber is mapped on 2D pixelbar (pixel bars) one by one.According to DBSCAN method, set corresponding radius and density threshold, high-density fiber data are carried out to automatic cluster, direction is close, and the similar fiber in path gathers for a bundle fibrous bundle, thereby sorts out clearly the similar some fibrous bundles in path;

Interactive module also redraws fiber: because the low probability fiber of following the tracks of out is insincere, in the multifilament of need to comforming, reject.Be mapped as after 2D pixelbar, all fibres on the self-defining fiber probability threshold value of selection that can be freely mutual, rejects lower than threshold value fiber, repaints out high believable fiber.