CN107689043B - Method for acquiring blood vessel section terminal node and branch node - Google Patents
Method for acquiring blood vessel section terminal node and branch node Download PDFInfo
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- 210000004204 blood vessel Anatomy 0.000 title claims abstract description 69
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- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 238000009966 trimming Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 3
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- 238000004458 analytical method Methods 0.000 description 7
- 238000011160 research Methods 0.000 description 5
- 238000003384 imaging method Methods 0.000 description 3
- 210000004072 lung Anatomy 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
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- 230000000694 effects Effects 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 238000013138 pruning Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000002600 positron emission tomography Methods 0.000 description 1
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- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06—COMPUTING; CALCULATING OR COUNTING
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- 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]
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- 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]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Abstract
The invention belongs to the field of medical image processing technology, and particularly relates to a method for acquiring a blood vessel section terminal node and a branch node, which comprises the following steps: analyzing the neighborhood relationship of the skeleton points to obtain a skeleton relationship set; reducing pseudo skeleton branches; analyzing the framework branch points, and forming branch aggregation when the branch nodes have adjacent relation according to the connection relation of the framework branch nodes; reconstructing a skeleton relationship through the branch nodes and the connecting edges, and repeating the steps until the number of the connecting neighborhoods of the branch nodes is more than 3 or the number of the branch nodes is not reduced; the invention better constructs the incidence relation between the framework branch nodes and other framework points, and provides a basis for measuring the length and the radius of the blood vessel segment.
Description
Technology neighborhood
The invention belongs to the technical field of medical image processing, and particularly relates to a method for acquiring a blood vessel section terminal node and a branch node.
Background
In recent years, with the rapid development and popularization of novel imaging technologies and devices such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), etc., a large amount of research images are generated every day around the world, which makes the analysis of organs, tissues and blood vessels by using the research images one of the current research hotspots. The acquisition of the blood vessels has important guiding significance for the research of analyzing the individual blood vessels, and the correctly segmented blood vessel structure can be used for analyzing the ambiguity of the lung tissue structure in the same region through the automatic detection of the nodules in the image.
In low dose imaging, in addition to the complexity of the tissue or organ condition, the vessel image is affected by a large amount of noise generated by imaging and partial volume effects. This makes the contrast of the blood vessel and other tissues low, so that it is difficult for the conventional image blood vessel segmentation method to obtain a good segmentation result.
At present, many scholars at home and abroad have been devoted to research on various medical image segmentation algorithms for years to segment blood vessels in images, although the existing methods can obtain a certain lung segmentation effect under specific conditions. However, because the blood vessel has a multi-level branch structure, the blood vessel tree structure segmented by the common segmentation method mostly loses many branches of the tiny blood vessels, and the contrast between the blood vessel and other lung tissues is usually low in a low-dose image; moreover, the segmented blood vessels are influenced by noise, and the blood vessel branches which should be communicated appear a large number of fractures or losses. Therefore, such a segmentation method has difficulty in obtaining a complete vessel tree structure, which lacks quantification capability and cannot provide parameter information of specific vessel length and corresponding diameter. The fundamental reasons are that basic means for processing and analyzing the segmented blood vessels are lacked, most methods only provide qualitative image display, accurate skeleton analysis means are lacked for specific blood vessel cases, and due to the discreteness of the blood vessel skeleton, the accurate direction of the blood vessel is difficult to accurately determine, so that a better analysis segmentation result is difficult to obtain.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for acquiring a blood vessel section terminal node and a branch node, and aims to provide a basis for improving an image to be effective in blood vessel analysis and measurement. The method provides a basis for the length measurement and the caliber measurement of the blood vessel based on the skeleton analysis of the graph analysis, and has wide application prospect due to certain mathematical basis and better implementation means.
The invention discloses a method for acquiring a vessel segment gateway terminal node and a branch node, which comprises the following steps of:
s1, analyzing the neighborhood relationship of the skeleton points to obtain a skeleton relationship set;
s2, cutting pseudo skeleton branches;
s3, analyzing framework branch node SbAccording to the connection relation of the framework branch nodes, when the branch nodes have adjacent relation, branch aggregation is formed;
s4, the skeleton relationship is rebuilt through the branch nodes and the connecting edges, and the S1, the S2 and the S3 are repeated until the number of the connection neighborhoods of the branch nodes is larger than 3 or the number of the branch nodes is not reduced any more.
Preferably, before the step S1, a step S0 is further included, as shown in fig. 2, the noise reduction of the segmented blood vessels according to the connectivity of the blood vessels includes:
searching connected voxels according to the neighborhood relationship, and dividing vessel voxels into a plurality of parts; ,
calculating the size of each communicating body, and selecting the largest communicating body as a communicated blood vessel;
taking the communicated blood vessel as a blood vessel mask to be multiplied with the central skeleton voxel, thereby removing skeleton noise;
and (3) performing isotropic interpolation on the blood vessel without skeleton noise, recording the size of each voxel, and finishing the blood vessel skeleton by using a mature skeletonization method.
Further, the isotropic interpolation includes:
and taking the ratio of the maximum value to the intermediate value and the ratio of the maximum value to the minimum value of the coordinates of the skeleton points as interpolation ratios, wherein the coordinates of the skeleton points obtained by the equi-square interpolation on the x axis, the y axis and the z axis are the same. .
Preferably, the analyzing the neighborhood relationship of the skeleton points to obtain a skeleton relationship set includes:
acquiring a framework point neighborhood point or a framework point value from the obtained framework points;
starting from any skeleton point, using neighborhood cutting ball to search skeleton point in neighborhood of the point, and counting the skeleton point as neighborhood point number Nb；
Number of current neighborhood points NbIf the number of the branch nodes is more than or equal to 3, the branch nodes of the framework are merged into a branch node set Sb(ii) a Number of current neighborhood points NbEqual to 2, then is a common connected skeleton point set Sc(ii) a Number of current neighborhood points NbEqual to 1, the terminal skeleton point set S ist。
Preferably, the calculating the data neighborhood number comprises: for any belonging to the branch node set SbP, number of neighborhood points NbIs N26The sum of the norms of all numbers in the neighborhood.
Preferably, the pruning of the pseudo skeleton branches comprises:
setting the radius R of each level of blood vessel according to the anatomical knowledge of the treated blood vesseliBranching skeleton B from candidate terminaltReducing erroneous skeletonized branches;
according to branch skeleton number N (B)t) And setting a trimming framework pseudo branch judgment threshold L according to the radius characteristicr(ii) a When N (B)t) Less than LrAnd if the frame is a pseudo branch, removing the pseudo branch and updating the neighborhood structure of the skeleton point.
Further, the branch aggregation includes:
analyzing the adjacency of the skeleton branch aggregation points, forming branch node aggregation when more than two branch nodes are adjacent, and distributing each aggregation number NcFinding the center of aggregation Sc；
Any of SbSkeleton points, provided that p belongs to the neighborhood of any other skeleton point therein, form an aggregate C with an aggregate center of Pc- (x)c,yc,zc)。
Preferably, characterized in that said concentration center Pc- (x)c,yc,zc) The calculation of (a) includes:
wherein n iscIs the number of branch aggregations, xi、yi、ziRespectively, coordinates of each neighborhood skeleton point in the aggregation C.
Preferably, the reconstructing the skeleton relationship through the branch nodes and the connecting edges, and repeating S1, S2 and S3 until the number of the connection neighborhoods of the branch nodes is greater than 3 or the number of the branch nodes is not reduced any more includes:
reconstructing a skeleton relationship through the branch nodes and the connecting edges;
after too short pseudo branches and wrong branches are removed, the neighborhood structure of the branch nodes is changed, and therefore the framework relationship needs to be updated;
and (4) reconstructing the number of skeletons according to the blood vessel segment set after updating the skeleton relationship, and re-analyzing the skeleton point neighborhood relationship, namely repeating S1, S2 and S3 until the number of connection neighborhoods of the branch nodes is more than 3 or the number of the branch nodes is not reduced any more.
Compared with the prior art, the method for acquiring the terminal nodes and the branch nodes of the blood vessel section comprises the steps of removing noise by using blood vessel voxels segmented by an image, performing isotropic interpolation, skeletonizing, analyzing the terminal nodes and the branch nodes by using the neighborhood relation of the skeletonized points and the geometric relation between a blood vessel central path and a blood vessel, and better constructing the incidence relation between the skeleton branch nodes and other skeleton points by using the relation between an image space and a physical space of the segmented blood vessel.
Drawings
FIG. 1 is a flowchart of a preferred embodiment of a method for acquiring an end node and a branch node according to the present invention;
FIG. 2 is a flowchart of another preferred embodiment of a method for acquiring an end node and a branch node according to the present invention;
FIG. 3 is a schematic diagram of the framework point and framework analysis according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention are described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The invention provides a method for acquiring a blood vessel section terminal node and a branch node, which specifically comprises the following steps as shown in fig. 1 and fig. 2:
s0, reducing noise of the blood vessel obtained by segmentation according to the connectivity of the blood vessel
According to N26The neighborhood relation searches for connected voxels, and the vessel voxels are divided into a plurality of parts; ,
calculating the size of each communicating body, and selecting the largest communicating body as a communicating blood vessel;
taking the communicated blood vessel as a blood vessel mask and carrying out AND operation on the blood vessel mask and the central skeleton voxel so as to remove skeleton noise;
and (3) performing isotropic interpolation on the blood vessel without skeleton noise, recording the size of each voxel, and finishing the blood vessel skeleton by using a mature skeletonization method.
Further, the blood vessel mask is used for binarizing the acquired blood vessel image, namely, a part containing the connected blood vessels is marked as '1', and a part without the connected blood vessels is marked as '0'.
Furthermore, the method for removing the skeleton noise comprises the steps of performing AND operation on a blood vessel mask and a central skeleton voxel, and removing a part without a blood vessel connected voxel, namely removing the skeleton noise; the method specifically comprises the following steps:
Vvessels＝Vs&Maskv
wherein VvesselsMask for removing noisy vessel segmentsvFor blood vessel masking, VsIs the central skeleton voxel.
Further, the isotropic interpolation includes:
the vessel voxel after segmentation and de-noising is recorded as S (S)x,Sy,Sz)，Sx、Sy、SzIs the scale of a blood vessel, where Sx,Sy,SzThe ratio of the maximum value to the intermediate value and the ratio of the maximum value to the minimum value of (a) are taken as interpolation ratios, and the isotropic interpolation satisfies that the isotropic scales of the skeleton points are equal, for example: selection of Smax＝max(Sx,Sy,Sz)、Smin＝min(Sx,Sy,Sz)、Smed＝med(Sx,Sy,Sz) The interpolation ratio is: smax/Smed、Smax/SminAnd the result of the isotropic interpolation satisfies: s'x＝S’y＝S’zThe isotropic interpolation is to ensure the isotropy of the segmented and denoised blood vessel and reduce the pseudo branches generated by skeletonization.
S1, analyzing the neighborhood relationship of the skeleton points to obtain a skeleton relationship set
Preferably, the analyzing the skeleton point neighborhood relationship, as shown in fig. 3, obtaining the skeleton relationship set includes:
acquiring a framework point neighborhood point or a framework point value from the obtained framework points;
starting from any skeleton point, using neighborhood cutting ball to search skeleton point in neighborhood of the point, and counting the skeleton point as neighborhood point number Nb；
Number of current neighborhood points NbIf the number of the branch nodes is more than or equal to 3, the branch nodes of the framework are merged into a branch node set Sb(ii) a Number of current neighborhood points NbEqual to 2, then is a common skeleton connected point set Sc(ii) a Number of current neighborhood points NbEqual to 1, the terminal skeleton point set S ist。
Wherein v isbIs a skeleton point set.
The point, edge and neighborhood relationships obtained by the present step are as follows:
s2, reducing false skeleton branch
Preferably, the pruning of the pseudo skeleton branches comprises:
setting the radius R of each level of blood vessel according to the anatomical knowledge of the treated blood vesseliBranching skeleton B from candidate terminaltReducing erroneous skeletonized branches;
according to branch skeleton number N (B)t) And setting a trimming framework pseudo branch judgment threshold L according to the radius characteristicr；
When N (B)t) Less than LrAnd if the frame is a pseudo branch, removing the pseudo branch and updating the neighborhood structure of the skeleton point.
Further, a pseudo branch decision threshold LrLength tR, L being the ratio of vessel diameter to vessel voxel length of the current branchrA ratio of 1.5-2 times the current rough estimate radius R.
E belongs to the terminal edge set EtProvided that L (e) ≦ tLrRemoving the terminal edge, and for the branch node set S belonging to eb，N(sb)＝N(sb) -1, wherein N(s)b) As a set of branch nodes SbNeighborhood points of.
S3, analyzing framework branch node SbAccording to the connection relation of the branch nodes of the frameworks, when the branch nodes have adjacent relation, branch aggregation is formed
Preferably, the branch aggregation comprises:
analyzing the adjacency of the skeleton branch aggregation points, forming branch node aggregation when more than two branch nodes are adjacent, and distributing each aggregation number NcSearching for an aggregation center;
any of SbIf p belongs to the neighborhood of any of the skeleton points, an aggregate C is formed, the aggregate center of which is Pc- (x)c,yc,zc) Using the centre of convergence Pc- (x)c,yc,zc) To refine the branch points and remove the dummy branch points.
Preferably, characterized in that said concentration center Pc- (x)c,yc,zc) The calculation of (a) includes:
wherein n iscIs the number of branch aggregations, xi、yi、ziRespectively, coordinates of each neighborhood skeleton point in the aggregation C.
S4, reconstructing the skeleton relationship through the branch nodes and the connecting edges, and repeating S1, S2 and S3 until the number of the connection neighborhoods of the branch nodes is more than 3 or the number of the branch nodes is not reduced any more
Preferably, the calculating the data neighborhood number comprises: for any belonging to the branch node set SbP, number of neighborhood points NbIs N26The sum of all numbers in the neighborhood, i.e.
Preferably, the reconstructing the skeleton relationship through the branch nodes and the connecting edges, and repeating S1, S2 and S3 until the number of the connection neighborhoods of the branch nodes is greater than 3 or the number of the branch nodes is not reduced any more includes:
reconstructing a skeleton relationship through the branch nodes and the connecting edges;
after too short pseudo branches and wrong branches are removed, the neighborhood structure of the branch nodes is changed, and therefore the framework relationship needs to be updated;
and (4) reconstructing the number of skeletons according to the blood vessel segment set after updating the skeleton relationship, and re-analyzing the skeleton point neighborhood relationship, namely repeating S1, S2 and S3 until the number of connection neighborhoods of the branch nodes is more than 3 or the number of the branch nodes is not reduced any more.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, and the like.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for acquiring a blood vessel section terminal node and a branch node is characterized by comprising the following steps:
s0: the noise reduction of the segmented blood vessels according to the connectivity of the blood vessels comprises the following steps:
searching connected voxels according to the neighborhood relationship, and dividing vessel voxels into a plurality of parts;
calculating the size of each communicating body, and selecting the largest communicating body as a communicated blood vessel;
taking the communicated blood vessel as a blood vessel mask to be multiplied with the central skeleton voxel, thereby removing skeleton noise;
performing isotropic interpolation on the blood vessel without skeleton noise, recording the size of each voxel, and finishing the blood vessel skeleton by using a skeletonization method;
s1, analyzing the neighborhood relationship of the skeleton points to obtain a skeleton relationship set;
s2, cutting pseudo skeleton branches;
s3, analyzing framework branch node SbAccording to the connection relation of the framework branch nodes, when the branch nodes have adjacent relation, branch aggregation is formed;
s4, the skeleton relationship is rebuilt through the branch nodes and the connecting edges, and the S1, the S2 and the S3 are repeated until the number of the connection neighborhoods of the branch nodes is larger than 3 or the number of the branch nodes is not reduced any more.
2. The method according to claim 1, wherein the isotropic interpolation comprises:
and taking the ratio of the maximum value to the intermediate value and the ratio of the maximum value to the minimum value of the coordinates of the skeleton points as interpolation ratios, wherein the coordinates of the skeleton points obtained by the equi-square interpolation on the x axis, the y axis and the z axis are the same.
3. The method according to claim 1, wherein the analyzing the neighborhood relationship of the skeleton points to obtain the skeleton relationship set comprises:
obtaining framework point neighborhood points from the obtained framework points;
starting from any skeleton point, using neighborhood cutting ball to search skeleton point in neighborhood of the point, and counting the skeleton point as neighborhood point number Nb；
Number of current neighborhood points NbIf the number of the branch nodes is more than or equal to 3, the branch nodes of the framework are merged into a branch node set Sb(ii) a Number of current neighborhood points NbEqual to 2, then is a common connected skeleton point set Sc(ii) a Number of current neighborhood points NbEqual to 1, the terminal skeleton point set S ist。
4. The method according to claim 3, wherein the calculating of the number of neighborhood points comprises: for any belonging to the branch node set SbP, number of neighborhood points NbIs N26The sum of the norms of all numbers in the neighborhood.
5. The method according to claim 1, wherein the reducing of the pseudo skeleton branches comprises:
setting the radius R of each level of blood vessel according to the anatomical knowledge of the treated blood vesseliBranching skeleton B from candidate terminaltReducing erroneous skeletonized branches;
according to branch skeleton number N (B)t) And setting a trimming framework pseudo branch judgment threshold L according to the radius characteristicr；
When N (B)t) Less than LrAnd if the frame is a pseudo branch, removing the pseudo branch and updating the neighborhood structure of the skeleton point.
6. The method according to claim 1, wherein the branch aggregation comprises:
analyzing the adjacency of the skeleton branch aggregation points, forming branch node aggregation when more than two branch nodes are adjacent, and distributing each aggregation number NcFinding the center of aggregation Sc；
Belonging to a set of branch nodes SbThe neighborhood skeleton points of any skeleton point of (a) form an aggregate C with an aggregate center of Pc.
7. The method of claim 6, wherein the calculating the coordinates of the c + Pc center comprises:
wherein x isc,yc,zcX-axis coordinate, Y-axis coordinate, and Z-axis coordinate, n, respectively, of the focus center PccIs the number of branch aggregations, xi、yi、ziRespectively, coordinates of each neighborhood skeleton point in the aggregation C.
8. The method of claim 1, wherein the reconstructing the skeleton relationship by the branch nodes and the connecting edges and repeating the steps S1, S2 and S3 until the number of connection neighborhoods of the branch nodes is greater than 3 or the number of branch nodes is not reduced further comprises:
reconstructing a skeleton relationship through the branch nodes and the connecting edges;
updating the skeleton relationship;
and reconstructing the skeleton number according to the blood vessel segment set, and re-analyzing the skeleton point neighborhood relationship, namely repeating S1, S2 and S3 until the connection neighborhood number of the branch nodes is more than 3 or the branch node number is not reduced any more.
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