CN107689043A - A kind of acquisition methods of vessel segment terminal node and branch node - Google Patents

A kind of acquisition methods of vessel segment terminal node and branch node Download PDF

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CN107689043A
CN107689043A CN201710749812.XA CN201710749812A CN107689043A CN 107689043 A CN107689043 A CN 107689043A CN 201710749812 A CN201710749812 A CN 201710749812A CN 107689043 A CN107689043 A CN 107689043A
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skeleton
branch
msub
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branch node
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CN107689043B (en
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赖均
李伟生
赖涵
汪俊
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Chongqing University of Post and Telecommunications
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-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]
    • 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
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

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Abstract

The invention belongs to Medical Image Processing neighborhood, the acquisition methods of more particularly to a kind of vessel segment terminal node and branch node, methods described includes：Skeletal point neighborhood relationships are analyzed, obtain skeleton set of relationship；Cut down pseudo- skeleton branches；Skeleton branches point is analyzed, according to the connected relation of these skeleton branches nodes, when branch node has neighbouring relations, then forms branch's aggregation；Skeleton relation is rebuild by branch node and connection side, the connection Neighborhood Number of branch node is steps be repeated alternatively until more than 3 or branch node number is no longer reduced；Incidence relation between preferably structure skeleton branches node of the invention with other skeletal points, basis is provided for measurement blood vessel segment length and vessel radius.

Description

A kind of acquisition methods of vessel segment terminal node and branch node
Technology neighborhood
The invention belongs to technical field of medical image processing, more particularly to a kind of vessel segment terminal node and branch node Acquisition methods.
Background technology
In recent years, with computed tomography (computed tomography, CT), magnetic resonance imaging (magnetic Resonance imaging, MRI), positron emission tomography (positron emission tomography, PET) etc. The fast development and popularization of new imaging technique and equipment, extra large quantifier elimination image can be all produced daily all over the world, this causes Research on utilization image, which carries out organ, tissue and vessels analysis, turns into one of current research focus.And the acquisition of blood vessel is for analysis The research of individual blood vessel has important directive significance, by the automatic detection to tubercle in image, the blood vessel being correctly partitioned into Structure can be used for ambiguity of the parsing with region lung tissue structure.
In low dosage image, in addition to tissue or organ situation are complex, blood vessel image is imitated by imaging and partial volume Much noise caused by answering is influenceed.This causes the contrast step-down of blood vessel and other tissues, so that traditional image blood vessel Dividing method is difficult to the segmentation result obtained.
At present, many scholar is directed to the research of various medical image partitioning algorithms to implement always for many years both at home and abroad Segmentation to image medium vessels, although these existing methods can obtain certain lung segmentation effect under given conditions.But by There is multilevel branch structure in blood vessel, many minute blood vessels are lost by the vessel tree structures big city that common automatic Segmentation goes out Branch, in low dosage image, blood vessel and the contrast of other lung tissues are generally relatively low；And by noise being present in image Influence vascular branch that the blood vessel that is partitioned into can be made to occur connecting there is the phenomenon of a large amount of fractures or loss.Therefore, this The dividing method of sample is difficult to obtain complete vessel tree structures, make its lack quantization ability, it is impossible to provide specific length of vessel and The parameter information of respective diameters.Its basic reason is to lack the basic means for being handled and being analyzed to splitting blood vessel, and Most method only provides qualitatively image and shown, lacks accurate skeleton analysis means for specific blood vessel case, and Due to the discreteness of vascular skeleton in itself, it is difficult to which the accurate accurate direction for determining blood vessel, therefore, it is difficult to obtain preferably analysis point Cut result.
The content of the invention
In view of the shortcomings of the prior art, the present invention proposes a kind of acquisition methods of vessel segment terminal node and branch node, Purpose is to provide basis with measuring effective method to vessels analysis to improve image.It is blood for the skeleton analysis based on map analysis The linear measure longimetry of pipe and Calibration provide the foundation, because it has certain Fundamentals of Mathematics and preferable realization rate, institute It is with a wide range of applications with this method.
A kind of vessel segment of the present invention closes the acquisition methods of terminal node and branch node, as shown in figure 1, including：
S1, analysis skeletal point neighborhood relationships, obtain skeleton set of relationship；
S2, cut down pseudo- skeleton branches；
S3, analysis skeleton branches node Sb, according to the connected relation of these skeleton branches nodes, when branch node has phase During adjacent relation, then branch's aggregation is formed；
S4, skeleton relation rebuild by branch node and connection side, repeat S1, S2 and S3 until the connection of branch node is adjacent Domain number is more than 3 or branch node number is no longer reduced.
Preferably, step S0 is also included before step S1, as shown in Fig. 2 the blood vessel obtained will be split according to blood vessel Connectedness carries out noise reduction, including：
Connection voxel is found according to neighborhood relationships, blood vessel voxel is split into several parts；,
The size of each connected component is calculated, it is to connect blood vessel to select largest connected body；
Carried out using connection blood vessel as blood vessel mask and center framework voxel with multiplying, so as to remove skeleton noise；
Side's property interpolation such as carry out to the blood vessel for removing skeleton noise, and record the size of each voxel, use ripe skeleton Change method completes vascular skeleton.
Further, side's property interpolation such as described includes：
The ratio and maxima and minima of the maximum of the coordinate of skeletal point and median are used for Interpolation Proportions, etc. Square interpolation meets that each coordinate value of skeletal point is equal.
Preferably, the analysis skeletal point neighborhood relationships, obtaining skeleton set of relationship includes：
From obtained skeletal point, skeleton vertex neighborhood point or skeleton point value are obtained；
Ball is cut since any skeletal point, and with neighborhood, searches the skeletal point in the neighborhood of a point, and it is counted Number is neighborhood points Nb；
When neighborhood points NbMore than or equal to 3, then branch node collection S is incorporated to for skeleton branches nodeb；When neighborhood points Nb Then it is common connection skeleton point set S equal to 2c；When neighborhood points NbThen it is terminal skeleton point set S equal to 1t。
Preferably, the calculating data neighborhood number includes：To arbitrarily belonging to branch node collection SbPoint p, neighborhood points NbFor N26The norm sum of all numbers in neighborhood relationships.
Preferably, the pseudo- skeleton branches of reduction include：
The radius R of blood vessels at different levels is set according to handled vascular anatomy knowledgei, from candidate terminal branch skeleton BtCut down mistake Skeletonizing branch；
According to branched backbone points N (Bt) and radius characteristic setting trimming skeleton puppet branch decision threshold Lr；
As N (Bt) it is less than Lr, then it is assumed that it is pseudo- branch, removes pseudo- branch and update the neighbour structure of skeletal point.
Further, the branch settlement includes：
It is adjacent to skeleton branches accumulation point to analyze, when adjacent more than two or more branch node, then form branch's section Point aggregation, distribute each aggregation numbering Nc, find aggregation center Sc；
It is any to belong to SbSkeletal point, if p belongs to therein another skeleton neighborhood of a point, formed and assemble C, in its settlement The heart is Pc- (xc,yc,zc)。
Preferably, it is characterised in that the settlement center Pc- (xc,yc,zc) calculating include：
Wherein, ncFor branch's settlement number, xi、yi、ziThe coordinate of respectively each branch settlement number.
Preferably, it is described that skeleton relation is rebuild by branch node and connection side, S1, S2 and S3 are repeated until branch node Connection Neighborhood Number be more than 3 or branch node number no longer reduce including：
Skeleton relation is rebuild by branch node and connection side；
After removing too short pseudo- branch and erroneous branch, the neighbour structure of branch node can be changed, it is therefore desirable to update skeleton Relation；
Skeleton number is rebuild according to vessel segment collection after renewal skeleton relation, reanalyses skeletal point neighborhood relationships, i.e., repeatedly S1, S2 and S3 until branch node connection Neighborhood Number be more than 3 or branch node number no longer reduce.
Compared with prior art, the acquisition methods of a kind of vessel segment terminal node of the present invention and branch node, utilize image The advanced row noise remove of blood vessel voxel being partitioned into, then side's property interpolation such as carry out, skeletonizing is then carried out, utilizes skeletonizing point Neighborhood relationships and blood vessel center path and the geometrical relationship of blood vessel, analyze terminal node and branch node, due to make use of point The image space of blood vessel and the relation of physical space are cut, so between being allowed to preferably to build skeleton branches node and other skeletons The incidence relation of point.
Brief description of the drawings
Fig. 1 is the acquisition methods preferred embodiment flow chart of end node of the present invention and branch node；
Fig. 2 is another preferred embodiment flow chart of acquisition methods of end node of the present invention and branch node；
Fig. 3 is skeletal point of the present invention and skeleton analysis schematic diagram.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with accompanying drawing to of the invention real The technical scheme applied in example is clearly and completely described, it is clear that described embodiment is only that a present invention part is implemented Example, rather than whole embodiments.
The acquisition methods of a kind of the vessel segment terminal node and branch node that propose are invented, as depicted in figs. 1 and 2, specifically Comprise the following steps：
S0, the blood vessel of acquisition will be split according to the connective progress noise reduction of blood vessel
According to N26Neighborhood relationships find connection voxel, and blood vessel voxel is split into several parts；,
The size of each connected component is calculated, selects largest connected body to connect blood vessel；
Carry out and operate using connection blood vessel as blood vessel mask and center framework voxel, so as to remove skeleton noise；
Side's property interpolation such as carry out to the blood vessel for removing skeleton noise, and record the size of each voxel, use ripe skeleton Change method completes vascular skeleton.
Further, the blood vessel mask refers to the blood-vessel image binaryzation of acquisition, will contain the portion of connection blood vessel Minute mark is " 1 ", and the part without connection blood vessel is designated as " 0 ".
Further, the method for removing skeleton noise is that blood vessel mask and center framework voxel are carried out and operated, The part without blood vessel connection voxel is removed, that is, removes skeleton noise；Specially：
Vvessels=Vs&Maskv
Wherein VvesselsTo remove the vessel segment of noise, MaskvFor blood vessel mask, VsCentered on skeleton voxel.
Further, side's property interpolation such as described includes：
Blood vessel voxel after segmentation denoising is denoted as S (Sx,Sy,Sz), Sx、Sy、SzFor the yardstick of blood vessel, wherein Sx,Sy,Sz Maximum and the ratio and maxima and minima of median be used for Interpolation Proportions, property interpolation in the side's of grade meets each of skeletal point It is equal to yardstick, such as：Select Smax=max (Sx,Sy,Sz), Smin=min (Sx,Sy,Sz)、Smed=med (Sx,Sy,Sz), insert Value ratio is：Smax/Smed、Smax/Smin, the side's of grade property interpolation result satisfaction：S’x=S 'y=S 'z, i.e. isotropy interpolation be in order to Ensure the isotropism of the blood vessel after segmentation denoising, reduce pseudo- branch caused by skeletonizing.
S1, analysis skeletal point neighborhood relationships, obtain skeleton set of relationship
Preferably, the analysis skeletal point neighborhood relationships, as shown in figure 3, obtaining skeleton set of relationship includes：
From obtained skeletal point, skeleton vertex neighborhood point or skeleton point value are obtained；
Ball is cut since any skeletal point, and with neighborhood, searches the skeletal point in the neighborhood of a point, and it is counted Number is neighborhood points Nb；
When neighborhood points NbMore than or equal to 3, then branch node collection S is incorporated to for skeleton branches nodeb；When neighborhood points Nb Then it is common skeleton connected set of points S equal to 2c；When neighborhood points NbThen it is terminal skeleton point set S equal to 1t。
Wherein, vbFor skeleton point set.
By available point, side and the neighborhood relationships such as following table of this step：
S2, cut down pseudo- skeleton branches
Preferably, the pseudo- skeleton branches of reduction include：
The radius R of blood vessels at different levels is set according to handled vascular anatomy knowledgei, from candidate terminal branch skeleton BtCut down mistake Skeletonizing branch；
According to branched backbone points N (Bt) and radius characteristic setting trimming skeleton puppet branch decision threshold Lr；
As N (Bt) it is less than Lr, then it is assumed that it is pseudo- branch, removes pseudo- branch and update the neighbour structure of skeletal point.
Further, pseudo- branch's decision threshold LrFor the scale length of the blood vessel diameter and blood vessel voxel length of current branch TR, LrCan be the 1.5-2 times of ratio that current coarse estimates radius R.
For example, e belongs to terminal edge collection EtIf L (e)≤tLr, remove the terminal edge, the branch node collection for belonging to e Sb, N (sb)=N (sb) -1, wherein N (sb) it is branch node collection SbNeighborhood points.
S3, analysis skeleton branches node Sb, according to the connected relation of these skeleton branches nodes, when branch node has phase During adjacent relation, then branch's aggregation is formed
Preferably, the branch settlement includes：
It is adjacent to skeleton branches accumulation point to analyze, when adjacent more than two or more branch node, then form branch's section Point aggregation, distribute each aggregation numbering Nc, find aggregation center；
It is any to belong to SbSkeletal point, if p belongs to any skeleton neighborhood of a point therein, aggregation C is formed, in its settlement The heart is Pc- (xc,yc,zc), utilize settlement center Pc- (xc,yc,zc) come branch point of refining, remove pseudo- branch point.
Preferably, it is characterised in that the settlement center Pc- (xc,yc,zc) calculating include：
Wherein, ncFor branch's settlement number, xi、yi、ziThe coordinate of respectively each branch settlement number.
S4, skeleton relation rebuild by branch node and connection side, repeat S1, S2 and S3 until the connection of branch node is adjacent Domain number is more than 3 or branch node number is no longer reduced
Preferably, the calculating data neighborhood number includes：To arbitrarily belonging to branch node collection SbPoint p, neighborhood points NbFor N26All number sums in neighborhood relationships, i.e.,
Preferably, it is described that skeleton relation is rebuild by branch node and connection side, S1, S2 and S3 are repeated until branch node Connection Neighborhood Number be more than 3 or branch node number no longer reduce including：
Skeleton relation is rebuild by branch node and connection side；
After removing too short pseudo- branch and erroneous branch, the neighbour structure of branch node can be changed, it is therefore desirable to update skeleton Relation；
Skeleton number is rebuild according to vessel segment collection after renewal skeleton relation, reanalyses skeletal point neighborhood relationships, i.e., repeatedly S1, S2 and S3 until branch node connection Neighborhood Number be more than 3 or branch node number no longer reduce.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can To instruct the hardware of correlation to complete by program, the program can be stored in a computer-readable recording medium, storage Medium can include：ROM, RAM, disk or CD etc..
Embodiment provided above, the object, technical solutions and advantages of the present invention are carried out with further detailed description, institute It should be understood that embodiment provided above is only the preferred embodiment of the present invention, be not intended to limit the invention, it is all Any modification, equivalent substitution and improvements made for the present invention etc., should be included in the present invention within the spirit and principles in the present invention Protection domain within.

Claims (9)

  1. A kind of 1. acquisition methods of vessel segment terminal node and branch node, it is characterised in that including：
    S1, analysis skeletal point neighborhood relationships, obtain skeleton set of relationship；
    S2, cut down pseudo- skeleton branches；
    S3, analysis skeleton branches node Sb, according to the connected relation of these skeleton branches nodes, when branch node has adjacent pass When being, then branch's aggregation is formed；
    S4, by branch node and connection side rebuild skeleton relation, repeat S1, S2 and S3 until the connection Neighborhood Number of branch node More than 3 or branch node number is no longer reduced.
  2. 2. the acquisition methods of a kind of vessel segment terminal node according to claim 1 and branch node, it is characterised in that Also include S0 before step S1：The blood vessel for splitting acquisition is included according to the connective noise reduction that carries out of blood vessel：
    Connection voxel is found according to neighborhood relationships, blood vessel voxel is split into several parts；
    The size of each connected component is calculated, it is to connect blood vessel to select largest connected body；
    Carried out using connection blood vessel as blood vessel mask and center framework voxel with multiplying, so as to remove skeleton noise；
    Side's property interpolation such as carry out to the blood vessel for removing skeleton noise, and record the size of each voxel, use ripe skeletonizing side Method completes vascular skeleton.
  3. 3. the acquisition methods of a kind of vessel segment terminal node according to claim 2 and branch node, it is characterised in that institute Side's property interpolation such as stating includes：
    By the Interpolation Proportions that are used for of the ratio and maxima and minima of the maximum of the coordinate of skeletal point and median, the side of grade inserts Value meets that each coordinate value of skeletal point is equal.
  4. 4. the acquisition methods of a kind of vessel segment terminal node according to claim 1 and branch node, it is characterised in that institute Analysis skeletal point neighborhood relationships are stated, obtaining skeleton set of relationship includes：
    From obtained skeletal point, skeleton vertex neighborhood point or skeleton point value are obtained；
    Ball is cut since any skeletal point, and with neighborhood, searches the skeletal point in the neighborhood of a point, and it is counted as Neighborhood points Nb；
    When neighborhood points NbMore than or equal to 3, then branch node collection S is incorporated to for skeleton branches nodeb；When neighborhood points NbIt is equal to 2, then it is common connection skeleton point set Sc；When neighborhood points NbThen it is terminal skeleton point set S equal to 1t。
  5. 5. the acquisition methods of a kind of vessel segment terminal node according to claim 4 and branch node, it is characterised in that institute Stating the calculating of neighborhood points includes：To arbitrarily belonging to branch node collection SbPoint p, neighborhood points NbFor N26Own in neighborhood relationships Number norm sum.
  6. 6. the acquisition methods of a kind of vessel segment terminal node according to claim 1 and branch node, it is characterised in that institute Stating the pseudo- skeleton branches of reduction includes：
    The radius R of blood vessels at different levels is set according to handled vascular anatomy knowledgei, from candidate terminal branch skeleton BtCut down wrong skeleton Change branch；
    According to branched backbone points N (Bt) and radius characteristic setting trimming skeleton puppet branch decision threshold Lr；
    As N (Bt) it is less than Lr, then it is assumed that it is pseudo- branch, removes pseudo- branch and update the neighbour structure of skeletal point.
  7. 7. the acquisition methods of a kind of vessel segment terminal node according to claim 1 and branch node, it is characterised in that institute Stating branch settlement includes：
    It is adjacent to skeleton branches accumulation point to analyze, when adjacent more than two or more branch node, then form branch node and gather Collection, distribute each aggregation numbering Nc, find aggregation center Sc；
    Belong to SbAny skeleton neighborhood of a point skeletal point formed aggregation C, its settlement center is Pc.
  8. 8. the acquisition methods of a kind of vessel segment terminal node according to claim 7 and branch node, it is characterised in that poly- Falling center Pc coordinate calculating includes：
    <mrow> <msub> <mi>x</mi> <mi>c</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <msub> <mi>n</mi> <mi>c</mi> </msub> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>c</mi> </msub> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mi>c</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <msub> <mi>n</mi> <mi>c</mi> </msub> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>c</mi> </msub> </munderover> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>z</mi> <mi>c</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <msub> <mi>n</mi> <mi>c</mi> </msub> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>n</mi> <mi>c</mi> </msub> </munderover> <msub> <mi>z</mi> <mi>i</mi> </msub> </mrow>
    Wherein, xc,yc,zcSettlement center Pc X-axis coordinate, Y-axis coordinate and Z axis coordinate, n are represented respectivelycFor branch's settlement number, xi、yi、ziThe coordinate of respectively each branch settlement number.
  9. 9. the acquisition methods of a kind of vessel segment terminal node according to claim 1 and branch node, it is characterised in that institute State and skeleton relation is rebuild by branch node and connection side, repeat S1, S2 and S3 until the connection Neighborhood Number of branch node is more than 3 Or branch node number no longer reduce including：
    Skeleton relation is rebuild by branch node and connection side；
    Update skeleton relation；
    According to vessel segment collection rebuild skeleton number, reanalyse skeletal point neighborhood relationships, i.e., repeatedly S1, S2 and S3 until branch node Connection Neighborhood Number be more than 3 or branch node number no longer reduce.
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Publication number Priority date Publication date Assignee Title
CN113421218A (en) * 2021-04-16 2021-09-21 深圳大学 Method for extracting branch point of vascular network
CN113421218B (en) * 2021-04-16 2024-02-23 深圳大学 Extraction method of vascular network branch point

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