CN116452992A - Method for extracting center line of tubular structure of minimum path - Google Patents
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Abstract
A method for extracting the central line of tubular structure with minimum path features that the tubular structure image data set is sent to the Attention U-Net network to complete the training of network, and the optimal weight and optimal bias are stored after the training is completed. Detecting feature points by using a Shi-Tomasit feature point detector to generate a feature point diagram, then sending the feature point diagram into a minimum path algorithm, and calculating a minimum path between the feature points by using the minimum path algorithm fused with an AttenionU-Net network, thereby completing the extraction of the central line of the tubular structure. The attribute U-Net network is fused into the minimum path algorithm, so that the minimum path algorithm overcomes the shortcuts and increases the accuracy of the minimum path. Meanwhile, the minimum path algorithm provides a strong geometric priori for the Attention U-Net network, and the performance of the Attention U-Net network is greatly improved. The minimum path algorithm and the Attention U-Net network are mutually fused, alternately executed and complementary in advantages, so that the central line of the tubular structure can be rapidly and accurately extracted from the complex tubular structure image.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to a method for extracting a central line of a tubular structure of a minimum path.
Background
Segmentation of the centerline of tubular structures is an important issue in the field of computer vision, applications including medical image processing, robotic navigation, three-dimensional modeling, and the like. Over the past decades, researchers have proposed a number of segmentation methods for the centerline of tubular structures, which can be categorized into graph theory-based methods, filtering-based methods, surface evolution-based methods, and deep learning-based methods.
The graph theory-based method is to represent a tubular structure in the form of a graph, and find a center line through a graph theory algorithm. One classical approach is to trace the min-cut, which represents the tubular structure as a weighted graph, looking for the centerline by the min-cut algorithm. Shi et al (ref: jianbo Shi and J. Malik, "Normalized cuts and image segmentation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.8, pp.888-905, aug.2000, doi: 10.1109/34.868688.) achieved good results in image segmentation by this method. The method has higher accuracy and robustness, but has higher computational complexity due to the need of building a complex graph model. The filtering-based method extracts the tubular structure by subjecting the image to a series of filters, wherein common filtering methods include gaussian filtering, hessian matrix, etc. The method has higher calculation efficiency, but has certain limitation on the need of manually adjusting the filtering parameters for tubular structures with different shapes and sizes. The method based on curved surface evolution is used for searching the center line through the curved surface evolution process by representing the tubular structure as a curved surface. Common methods include a Level Set model, an Active content model and the like. This approach can adaptively adapt to tubular structures of different shapes and sizes, but has higher computational complexity and requires longer computational time. As deep learning techniques develop, more and more researchers have begun to try to apply them to the segmentation problem of the tubular structure centerline. Common methods include Convolutional Neural Network (CNN) based methods and Recurrent Neural Network (RNN) based methods. The method has higher accuracy and robustness, but requires a large amount of data for training, and can have the problem of over fitting.
In summary, segmentation of the centerline of tubular structures is an important issue in the field of computer vision, and various techniques and methods have their own advantages and disadvantages.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a method capable of rapidly and accurately extracting the central line of the tubular structure from the complex tubular structure image.
The technical scheme adopted for overcoming the technical problems is as follows:
the extraction method of the center line of the tubular structure of the minimum path comprises the following steps:
a) Acquiring m suburb road images containing annotation information of suburb road center lines to form a training set D' 1 ，D′ 1 ＝{T 1 ,T 2 ,...,T i ,...,T m }, T therein i For the ith suburb road image, i epsilon {1,2,.. M }, obtaining n suburb road images containing annotation information of suburb road center lines, and forming a test set D 2 ′，D′ 2 ＝{T 1 ,T 2 ,...,T j ,...,T n }, T therein j J e {1,2,., n }; b) Test set D' 2 Splitting suburban road images, correcting one-fourth of the total number of the split images by a method that the central line of the road in the images is positioned at the center of the images and is perpendicular to the edges of the images, and forming a positive sample set D 'by the corrected images' 2+ The remaining three quarters of the split images form a negative set D' 2- Positive sample set D' 2+ And negative sample set D' 2- Performing addition operation to obtain a data set D, and dividing the data set D into a training set train and a test set test;
c) Inputting images in a training set train into an Attention U-Net network, training the Attention U-Net network, and obtaining a trained Attention U-Net network;
d) Test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer to obtain a feature point diagram H t And feature point set Q t Define a set of feature points Q t Disjoint point set S t ，Q t ∩S t Let phi be the empty set, set point S t Initializing to be an empty set;
e) Feature point diagram H t Inputting into a minimum path calculation layer to obtain a jth suburban road image T j Center line of the middle tubular structure.
Further, in step a), m suburban road images including annotation information for suburban road centerlines are acquired from the satellite image dataset EPFL, and n suburban road images including annotation information for suburban road centerlines are acquired.
Further, in step b), a PIL library is imported in python, an Image package is imported from the PIL library, and the crop () function test set D 'in the Image package is used' 2 Suburban road image splitting.
Preferably, in step b) the data set D is divided into training and test sets in a ratio of 7:3.
Further, step d) comprises the steps of:
d-1) the test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer, detecting feature points by using a Shi-Tomasit feature point detector to obtain a feature point diagram H t ；
d-2) mapping the feature points H t Generating a feature point set Q from each feature point in the set t 。
Further, step e) comprises the steps of:
e-1) mapping the feature points H t Input into a trained Attention U-Net network, and output to obtain an Attention weight coefficient diagram H att ；
e-2) mapping the feature points H t Building an undirected graph G t ，G t ＝(V t ,E t )，V t As undirected graph G t The collection of vertices is such that, as undirected graph G t I-th vertex of (a)>As undirected graph G t The j-th vertex of (a)>As undirected graph G t I e {1,2,., o }, j e {1,2,., o }, u e {1,2,., o }, o is an undirected graph G t Total number of middle vertexes, feature point set Q t For set V t Proper subset of (E) t As undirected graph G t Is provided with a set of edges of the (c),wherein->Is the ith vertex->And j' th vertex>Edges therebetween;
e-3) will be the ith vertexAs the initial vertex, the u-th vertex +.>As an ending vertex, the starting vertex +.>From a set of feature points Q t Culling and adding point set S t ；
e-4) calculating the initial vertexAnd end vertex->Minimum path->Minimum path +.>As an initial vertex->And end vertex->A centerline of the tubular structure therebetween;
e-5) repeating steps e-3) to e-4) until feature point set Q t Adding all vertexes in the set S t Obtaining the j suburban road image T j Center line of the middle tubular structure.
Further, step e-4) comprises the steps of:
e-4.1) set V t Divided into two disjoint sets Q 1 And S is 1 ，Q 1 ∩S 1 =φ, set Q 1 Contains set V t All vertices of (1), set S 1 Is an empty set;
e-4.2) is calculated by the formulaCalculating the ith vertex->And j' th vertex>Weights of the sides between->In->For Franagi filter tube metrics, both ε and λ are constants,is the ith vertex->And j' th vertex>Euclidean distance between them to obtain set E t Initial weight set of edges of (a)
Close->
e-4.3) initial set of weights on edgeIs selected from and i-th vertex->The connected edges are the vertices of minimum weight +.>The ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->Is to add the vertex->From set Q 1 Culling and adding set S 1 ；
e-4.4) from the attention weighting factor graph H att Obtaining vertices inAttention coefficient of->If attention coefficient->A threshold Th of greater than or equal to the vertex->Adjacent vertex set of adjacent vertices +.>Selecting an adjacent vertex->The ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->If the attention coefficient is +.>Less than threshold Th, finding vertex ++through pi function of Dijkstra algorithm>Precursor node->Calculate vertex->And precursor node->Euclidean distance L of (2) j At the apex +>And precursor node->Defining a length L therebetween j Is a local path of (a)Build with Euclidean distance L j Square tubular image block P of side length j Tubular image block P j Is a local path->Tubular image block P using a warp Perselected () function in an Opencv library j Rotated along the central lineThe transformation operation obtains regularized image block->e-4.5) regularizing the image block +.>Judging regularized image block in classifier of the trained Attention U-Net network>Whether it is a prospect;
e-4.6) verticesAnd start vertex->The sum of the weights of all sides on the path between is +.>If regularized image block->For foreground, then vertex->And adjacent vertex->The weight of the edge between the two edges is kept unchanged, and whether the vertex is updated or not is judged through Dijkstra algorithm>To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (2); e-4.7) if regularized image block +.>As background, vertex->And adjacent vertex->The weight of the edge between the two isBy the formula->Calculating updated weightsWeight of update ∈>Is carried into Dijkstra algorithm, and whether to update is judged through the Dijkstra algorithmVertex->To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (2);
e-4.8) repeating steps e-4.3) through e-4.7) until the vertex is finishedJoining set S 1 ；
e-4.9) querying the set S by pi-function of Dijkstra algorithm 1 Middle ending vertexPrecursor node->
e-4.10) node precursorSubstitution of the ending vertex in step e-4.9)>Repeating the steps
e-4.10) until the starting vertex is obtainedAnd end vertex->Minimum path->
The value of E is 0.01, and lambda E is 0, 1.
Preferably, threshold Th e (0, 1).
The beneficial effects of the invention are as follows: and sending the tubular structure image data set into an attribute U-Net network to complete the training of the network, and storing the optimal weight and the optimal bias after the training is completed. Detecting feature points by using a Shi-Tomasit feature point detector to generate a feature point diagram, then sending the feature point diagram into a minimum path algorithm, and calculating a minimum path between the feature points by using the minimum path algorithm fused with an AttenionU-Net network, thereby completing the extraction of the central line of the tubular structure. The attribute U-Net network is fused into the minimum path algorithm, so that the minimum path algorithm overcomes the shortcuts and increases the accuracy of the minimum path. Meanwhile, the minimum path algorithm provides a strong geometric priori for the Attention U-Net network, and the performance of the Attention U-Net network is greatly improved. The minimum path algorithm and the Attention U-Net network are mutually fused, alternately executed and complementary in advantages, so that the central line of the tubular structure can be rapidly and accurately extracted from the complex tubular structure image.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to fig. 1.
The extraction method of the center line of the tubular structure of the minimum path comprises the following steps:
a) Acquiring m suburb road images containing annotation information of suburb road center lines to form a training set D' 1 ，D′ 1 ＝{T 1 ,T 2 ,...,T i ,...,T m }, T therein i For the ith suburban road image, i epsilon {1,2,.. M }, obtaining n suburban road images containing annotation information of suburban road center lines, and forming a test set D '' 2 ，D′ 2 ＝{T 1 ,T 2 ,...,T j ,…,T n }, T therein j J e {1,2, …, n } is the j suburban road image. In one embodiment of the present invention, the value of m may be 5 and the value of n may be 9.
b) Test set D' 2 Splitting suburban road images, correcting one-fourth of the total number of the split images by a method that the central line of the road in the images is positioned at the center of the images and is perpendicular to the edges of the images, and forming a positive sample set D 'by the corrected images' 2+ The remaining three quarters of the split images form a negative set D' 2- Positive sample set D' 2+ And negative sample set D' 2- And performing addition operation to obtain a data set D, and dividing the data set D into a training set train and a test set test.
c) And inputting the images in the training set train into the Attention U-Net network, and training the Attention U-Net network to obtain the trained Attention U-Net network.
d) Test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer to obtain a feature point diagram H t And feature point set Q t Define a set of feature points Q t Disjoint point set S t ，Q t ∩S t Let phi be the empty set, set point S t Initialized to an empty set.
e) Feature point diagram H t Inputting into a minimum path calculation layer to obtain a jth suburban road image T j Middle tube shapeThe centerline of the structure.
The method for extracting the central line of the tubular structure by embedding a progressive minimum path method into an attribute U-Net network. The centerline of the tubular structure is intended to be extracted from a high-complexity, poor-quality tubular image. The method is divided into two stages. In the first stage, the Attention U-Net network is trained. And in the second stage, fusing the AttenionU-Net network trained in the first stage into a Dijkstra algorithm to finish the extraction of the central line of the tubular structure. The attribute U-Net network is fused into the minimum path algorithm, so that the minimum path algorithm overcomes the shortcuts and increases the accuracy of the minimum path. Meanwhile, the minimum path algorithm provides a strong geometric priori for the Attention U-Net network, and the performance of the Attention U-Net network is greatly improved. The minimum path algorithm and the Attention U-Net network are mutually fused, alternately executed and complementary in advantages, so that the central line of the tubular structure can be rapidly and accurately extracted from the complex tubular structure image.
Example 1:
in step a), m suburban road images containing annotation information of suburban road centerlines are acquired from the satellite image data set EPFL, and n suburban road images containing annotation information of suburban road centerlines are acquired.
Example 2:
step b) of importing a PIL library in python, importing an Image package from the PIL library, and using a crop () function test set D 'in the Image package' 2 Suburban road image splitting.
Example 3:
in step b), the data set D is divided into a training set and a testing set according to the ratio of 7:3.
Example 4:
step d) comprises the steps of:
d-1) the test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer, detecting feature points by using a Shi-Tomasit feature point detector to obtain a feature point diagram H t 。
d-2) mapping the feature points H t Generating a feature point set Q from each feature point in the set t 。
Example 5:
step e) comprises the steps of:
e-1) mapping the feature points H t Input into a trained Attention U-Net network, and output to obtain an Attention weight coefficient diagram H att 。
e-2) mapping the feature points H t Building an undirected graph G t ，G t ＝(V t ,E t )，V t As undirected graph G t The collection of vertices is such that,as undirected graph G t I-th vertex of (a)>As undirected graph G t The j-th vertex of (a)>As undirected graph G t I e {1,2, …, o }, j e {1,2, …, o }, u e {1,2, …, o }, o is the undirected graph G t Total number of middle vertexes, feature point set Q t For set V t Proper subset of (E) t As undirected graph G t Is provided with a set of edges of the (c),wherein->Is the ith vertex->And j' th vertex>And a border therebetween. Set V t Each vertex in (a) corresponds to a feature point diagram H t Through set E between pixels as vertices t The edges of (a) are connected.
e-3) will be the ith vertexAs the initial vertex, the u-th vertex +.>As an ending vertex, the starting vertex +.>From a set of feature points Q t Culling and adding point set S t 。
e-4) calculating the initial vertexAnd end vertex->Minimum path->Minimum path +.>As an initial vertex->And end vertex->A centerline of the tubular structure.
e-5) repeating steps e-3) to e-4) until feature point set Q t Adding all vertexes in the set S t Obtaining the j suburban road image T j Center line of the middle tubular structure.
Example 6:
step e-4) comprises the steps of:
e-4.1) set V t Divided into two disjoint sets Q 1 And S is 1 ，Q 1 ∩S 1 =φ, set Q 1 Contains set V t All vertices of (1), set S 1 Is an empty set.
e-4.2) is calculated by the formulaCalculating the ith vertex->And j' th vertex>Weights of the sides between->In->For Franagi filter tube measure, both ε and λ are constants, +.>Is the ith vertex->And j' th vertex>Euclidean distance between them to obtain set E t Initial weight set of edges of (a)
Closing devicee-4.3) initial set of weights on edges +.>Is selected fromIth vertex->The connected edges are the vertices of minimum weight +.>The ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->Is to add the vertex->From set Q 1 Culling and adding set S 1 。
e-4.4) from the attention weighting factor graph H att Obtaining vertices inAttention coefficient of->If the attention coefficientA threshold Th of greater than or equal to the vertex->Adjacent vertex set of adjacent vertices +.>Selecting an adjacent vertex->The ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->If the attention coefficient is +.>Less than threshold Th, finding vertex ++through pi function of Dijkstra algorithm>Precursor node->Calculate vertex->And precursor node->Euclidean distance L of (2) j At the apex +>And precursor node->Defining a length L therebetween j Is->Build with Euclidean distance L j Square tubular image block P of side length j Tubular image block P j Is a local pathTubular image block P using a warp Perselected () function in an Opencv library j Rotation operation along the center line to get regularized image block +.>e-4.5) regularizing the image block +.>Judging regularized image block in classifier of the trained Attention U-Net network>Whether it is a prospect.
e-4.6) verticesAnd start vertex->The sum of the weights of all sides on the path between is +.>If regularized image block->For foreground, then vertex->And adjacent vertex->The weight of the edge between the two edges is kept unchanged, and whether the vertex is updated or not is judged through Dijkstra algorithm>To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (c). e-4.7) if regularized image block +.>As background, vertex->And adjacent vertex->The weight of the edge between the two isBy the formula->Calculating updated weightsWeight of update ∈>Is carried into Dijkstra algorithm, and whether the vertex is updated or not is judged through Dijkstra algorithm>To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (c).
e-4.8) repeating steps e-4.3) through e-4.7) until the vertex is finishedJoining set S 1 。
e-4.9) querying the set S by pi-function of Dijkstra algorithm 1 Middle ending vertexPrecursor node->
e-4.10) node precursorInstead of step e-4.9) Ending vertex of->Repeating step e-4.10) until the initial vertex +.>And end vertex->Minimum path->
Example 7:
preferably, the value of E is 0.01, and lambda E is (0, 1). Threshold Th e (0, 1).
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The extraction method of the central line of the tubular structure of the minimum path is characterized by comprising the following steps:
a) Acquiring m suburb road images containing annotation information of suburb road center lines to form a training set D' 1 ，D′ 1 ＝{T 1 ,T 2 ,...,T i ,...,T m }, T therein i For the ith suburban road image, i epsilon {1,2,.. M }, obtaining n suburban road images containing annotation information of suburban road center lines, and forming a test set D '' 2 ，D′ 2 ＝{T 1 ,T 2 ,...,T j ,...,T n }, T therein j For the j suburban roadImage j e {1,2,., n };
b) Test set D' 2 Splitting suburban road images, correcting one-fourth of the total number of the split images by a method that the central line of the road in the images is positioned at the center of the images and is perpendicular to the edges of the images, and forming a positive sample set D 'by the corrected images' 2+ The remaining three quarters of the split images form a negative set D' 2- Positive sample set D' 2+ And negative sample set D' 2- Performing addition operation to obtain a data set D, and dividing the data set D into a training set train and a test set test;
c) Inputting images in a training set train into an Attention U-Net network, training the Attention U-Net network, and obtaining a trained Attention U-Net network;
d) Test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer to obtain a feature point diagram H t And feature point set Q t Define a set of feature points Q t Disjoint point set S t ，Q t ∩S t Let phi be the empty set, set point S t Initializing to be an empty set;
e) Feature point diagram H t Inputting into a minimum path calculation layer to obtain a jth suburban road image T j Center line of the middle tubular structure.
2. The method for extracting a centerline of a tubular structure of minimum path according to claim 1, wherein: in step a), m suburban road images containing annotation information of suburban road centerlines are acquired from the satellite image data set EPFL, and n suburban road images containing annotation information of suburban road centerlines are acquired.
3. The method for extracting a centerline of a tubular structure of minimum path according to claim 1, wherein: step b) of importing a PIL library in python, importing an Image package from the PIL library, and using a crop () function test set D 'in the Image package' 2 Suburban road image splitting.
4. The method for extracting a centerline of a tubular structure of minimum path according to claim 1, wherein: in step b), the data set D is divided into a training set and a testing set according to the ratio of 7:3.
5. The method of extracting a centerline of a tubular structure of minimum path according to claim 1, wherein step d) comprises the steps of:
d-1) the test set D' 2 The j-th suburban road image T j Inputting into a feature point extraction layer, detecting feature points by using a Shi-Tomasit feature point detector to obtain a feature point diagram H t ；
d-2) mapping the feature points H t Generating a feature point set Q from each feature point in the set t 。
6. The method of extracting a centerline of a minimum path tubular structure according to claim 1, wherein step e) comprises the steps of:
e-1) mapping the feature points H t Input into a trained Attention U-Net network, and output to obtain an Attention weight coefficient diagram H att ；
e-2) mapping the feature points H t Building an undirected graph G t ，G t ＝(V t ,E t )，V t As undirected graph G t The collection of vertices is such that, as undirected graph G t I-th vertex of (a)>As undirected graph G t The j-th vertex of (a)>As undirected graphG t I e {1,2,., o }, j e {1,2,., o }, u e {1,2,., o }, o is an undirected graph G t Total number of middle vertexes, feature point set Q t For set V t Proper subset of (E) t As undirected graph G t Is provided with a set of edges of the (c),wherein->Is the ith vertex->And j' th vertex>Edges therebetween;
e-3) will be the ith vertexAs the initial vertex, the u-th vertex +.>As an ending vertex, the starting vertex +.>From a set of feature points Q t Culling and adding point set S t ；
e-4) calculating the initial vertexAnd end vertex->Minimum path->Minimum path +.>As an initial vertex->And end vertex->A centerline of the tubular structure therebetween;
e-5) repeating steps e-3) to e-4) until feature point set Q t Adding all vertexes in the set S t Obtaining the j suburban road image T j Center line of the middle tubular structure.
7. The method of extracting a centerline of a tubular structure of minimum path according to claim 6, wherein step e-4) comprises the steps of:
e-4.1) set V t Divided into two disjoint sets Q 1 And S is 1 ，Q 1 ∩S 1 =φ, set Q 1 Contains set V t All vertices of (1), set S 1 Is an empty set;
e-4.2) is calculated by the formulaCalculating the ith vertex->And j' th vertex>Weights of the sides between->In->For Franagi filter tube measure, both ε and λ are constants, +.>Is the ith vertex->And j' th vertex>Euclidean distance between them to obtain set E t An initial set of weights for edges of (2)>
e-4.3) initial set of weights on edgeIs selected from and i-th vertex->The connected edges are the vertices of minimum weight +.>The ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->Is to add the vertex->From set Q 1 Culling and adding set S 1 ；
e-4.4) from the attention weighting factor graph H att Obtaining vertices inAttention coefficient of->If attention coefficient->A threshold Th of greater than or equal to the vertex->Adjacent vertex set of adjacent vertices +.>Selecting an adjacent vertexThe ith vertex is ++by pi-function of Dijkstra algorithm>Set as vertex->If the attention coefficient isLess than threshold Th, finding vertex ++through pi function of Dijkstra algorithm>Precursor node->Calculate vertex->And precursor node->Euclidean distance L of (2) j At the apex +>And precursor node->Defining a length L therebetween j Is->Build with Euclidean distance L j Square tubular image block P of side length j Tubular image block P j Is a local pathTubular image block P using a warp Perselected () function in an Opencv library j Rotation operation along the center line to get regularized image block +.>
e-4.5) regularizing the image blocksJudging regularized image block in classifier of the trained Attention U-Net network>Whether it is a prospect;
e-4.6) verticesAnd start vertex->The sum of the weights of all sides on the path between is +.>If regularized image block->For foreground, then vertex->And adjacent vertex->The weight of the edge between the two edges is kept unchanged, and whether the vertex is updated or not is judged through Dijkstra algorithm>To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (2); e-4.7) if regularized image block +.>As background, vertex->And adjacent vertex->The weight of the edge between the two isBy the formula->Calculating updated weightsWeight of update ∈>Is carried into Dijkstra algorithm, and whether the vertex is updated or not is judged through Dijkstra algorithm>To the initial vertex->Path weight sum between ∈>If updated, vertex->To the initial vertex->The sum of the path weights between is updated to +.>Vertex ++through Dijkstra algorithm>Set as adjacent vertex->Is a precursor node of (2);
e-4.8) repeating steps e-4.3) through e-4.7) until the vertex is finishedJoining set S 1 ；
e-4.9) querying the set S by pi-function of Dijkstra algorithm 1 Middle ending vertexPrecursor node->
e-4.10) node precursorSubstitution of the ending vertex in step e-4.9)>Repeating the steps
e-4.10) until the starting vertex is obtainedAnd end vertex->Minimum path->
8. The method for extracting a centerline of a tubular structure of minimum path according to claim 7, wherein: the value of E is 0.01, and lambda E is 0, 1.
9. The method for extracting a centerline of a tubular structure of minimum path according to claim 7, wherein: threshold Th e (0, 1).
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