CN108830922B - Contour tree construction method based on multiple threads - Google Patents
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Abstract
The invention discloses a contour tree construction method based on multithreading, which comprises the steps of firstly, obtaining a slice image set generated by an image device, and forming a three-dimensional data set according to the sequence from top to bottom or from top to bottom; then, performing data equal division operation on the three-dimensional data set, and dividing the three-dimensional data set into a plurality of sub data sets according to the range interval; and then, respectively operating each sub data set, distributing 2 threads to each sub data set by utilizing a multithreading technology to calculate a split tree and a connection tree of the contour tree, further combining the split trees and the connection trees into sub contour trees, combining the sub contour trees formed by each sub data set to form a complete contour tree, and finally simplifying the topological structure of the formed contour tree to obtain the final required contour tree. The invention not only can obtain clear display effect, but also can flexibly carry out interaction.
Description
Technical Field
The invention relates to the technical field of medical image processing and application, in particular to a contour tree construction method based on multithreading.
Background
Volume rendering based on optical properties is the mainstream technique for visualization of medical data. The volume rendering simulates the transmission process of light rays in volume data, and further calculates the contribution of each point on a transmission path. The color and transparency attributes are distributed to the sampling points of the Volume data through the transfer function, and a GPU-based ray casting algorithm is combined, so that a three-dimensional image with an animated figure can be reconstructed, for example, in the document "efficiency Space cropping for Large-Scale Volume Rendering" (IEEE transactions on visualization and computer graphics,2018,24(1): 974-. Thus, the quality of the transfer function design determines the quality of the volume rendering.
Effective Volume Rendering techniques require well-designed Transfer Functions, and many scholars have already made good results, such as the document State of the Art in Transfer Functions for Direct Volume Rendering (Computer Graphics Forum,2016,35(3): 669-. The use of value-gradient histograms to guide the transfer function enables a fast and clear display of the volumetric data. Using 2D functions to classify data as well as features, the quality of the rendered image can be increased. One of the attributes that measure the local shape of data is curvature, and the document "Non-uniform sampling of geometry for the numerical simulation of head-related transfer functions" (Proceedings of the 21st International Congress of Sound and simulation, (Beijing, CN).2014) guides the design of transfer functions using curvature as a parameter. Image segmentation and feature measurement of two-dimensional histograms can improve volume rendering effects, as described in the document "Texture-based transfer functions for direct volume rendering" (IEEE Transactions on Visualization & Computer Graphics,2008,14(6): 1364).
The existing transfer function design can effectively guide the rendering of volume rendering. However, for volume data with a complex spatial structure, it is difficult to clearly express the boundary properties of a substance. Medical applications require finer structures for accurate diagnosis, which requires better differentiation of tissue structures. For the volume data of the complex structure, the topological structure can analyze the spatial topology of the complex structure, and further guide the design of a transfer function. There are many effective topological approaches that have been applied in volume rendering pipelines. For the topological analysis of volume data, algorithms such as Reeb-plot, Morse-Small complex, contour tree, etc. have been applied to many research fields, such as the documents "Direct Feature Visualization Using Morse-Small Complexes" (IEEE Transactions on Visualization & Computer Graphics,2012,18(9): 1549) -1562), "Measuring distance between Reeb graphs" (Proceedings of the third and fourth annual complex on Computational geometry ACM,2014: 464). The contour tree algorithm is more practical than other algorithms. However, as the resolution of the volume data continues to increase and the contour tree algorithm needs to traverse the data set multiple times, the technical challenges of data topology analysis, including computational overhead, increase. Especially, in order to perform interactive operation in the data analysis process, an efficient algorithm is needed to obtain a good experience. The parallel algorithm is introduced into the calculation of the contour tree, so that the calculation speed can be effectively improved. A Contour tree construction algorithm based on a Distributed environment is disclosed in the document Distributed content Trees (Topological Methods in Data Analysis and Visualization III Springer, Cham,2014:89-102), but the whole process needs multiple serial calculations, so that the operation performance is reduced.
The algorithms are all parallel in computation of partial stages, the construction speed of the contour tree is slow, and the interactive operation is not ideal because the contour tree rendering needs complex preprocessing. In order to overcome the defects, the invention is improved from two aspects: 1) by utilizing the multi-core capability of a CPU, a contour tree construction algorithm completely based on multithreading is provided; 2) based on the rapid contour tree algorithm, a new rendering mode is adopted to realize real-time interactive volume rendering. Through the improvement, the drawing effect of the volume data can be efficiently displayed, meanwhile, real-time interactive operation can be more conveniently carried out, and interesting features can be found.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art, and provides a contour tree construction method based on multiple threads, which can utilize a shared memory to complete parallel computation of a contour tree based on a range-driven partition strategy, particularly for complex data containing noise, the parallel implementation scheme can achieve speed balance on the loading and computation of redundant data, and meanwhile, the contour tree constructed by the method has good hierarchy and can clearly divide volume data, so that the design of a volume rendering transfer function can be guided, the rendering of the branch of the contour tree can be effectively accelerated by adopting a new rendering mode on the basis of the segmentation of the contour tree, the operation of the contour tree is flexible, the interactivity is strong, and the problem of fuzzy boundary of a medical volume rendering image can be solved.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a contour tree construction method based on multithreading comprises the steps of firstly, obtaining a slice image set generated by an image device, and forming a three-dimensional data set according to the sequence from top to bottom or from top to bottom; then, performing data equal division operation on the three-dimensional data set, and dividing the three-dimensional data set into a plurality of sub data sets according to the range interval; then, each subdata set is operated, 2 threads are distributed to each subdata set to calculate a split tree and a connection tree of the contour tree by utilizing a multithreading technology, the split tree and the connection tree are combined into a sub-contour tree, the sub-contour trees formed by each subdata set are combined to form a complete contour tree, and finally, the topological structure of the formed contour tree is simplified to obtain the final required contour tree; which comprises the following steps:
1) according to the size of the data set and the value range of the data, the data set is equally divided into n/2 sub-data sets by combining the number n of multiple threads supported by a computer;
2) allocating 2 threads for each subdata set for calculating a splitting tree and a connecting tree of the subdata set, and combining the splitting tree and the connecting tree into a sub-outline tree;
3) merging the sub-outline trees on all the sub-data sets into a complete outline tree;
4) simplifying the topological structure of the combined contour tree in the step 3) to obtain the final required contour tree.
In step 1), the data set is divided equally, the number of the threads is n, the data set is divided into n/2 sub-data sets, and each sub-data set contains the vertexes with the equal number:
where R represents a real number, μ represents an element of the dataset, i, j represent the order of the subdata set, and ξ representsiRepresents the ith sub-dataset, | δ0|iIs representative of xiiThe number of vertices in (1);
to accomplish this data equal division operation, the following steps are required:
1.1) arranging all vertexes on the data set in a small-to-large order by using a quick sorting algorithm, and storing elements by using an array;
1.2) equally dividing the ordered array into n/2 arraysi，θiThe corresponding subdata set is xii；
1.3) extension of θiExpansion into thetai'，θi' is formed by thetaiPlus with
And
connected vertices make up ofi +、fi -Is xiiEnd point of, i.e. ξi＝(fi -,fi +) Definition of
Is and fi +The edges of the connection are connected with each other,
is and fi -The edges of the connection.
In step 2), each subdata set is subjected to a local computation operation of the profile tree, and each data set xi is subjected to the computation of the profile tree of this stepiThe following operations are performed:
2.1) scanning the sub-data set ξ in increasing orderiExtended set of (theta)i' constructing a connection tree of the contour tree by using a classical union set algorithm;
2.2) scanning the sub data set ξ in descending orderiExtended set of (theta)i' constructing a split tree of the contour tree by using a classical parallel-searching algorithm;
and 2.3) combining the connection tree and the split tree obtained in the first two steps, and combining the nodes with the degree of 2 to form the contour tree.
In step 3), the sub-outline trees on each sub-data set are used as input, the sub-outline trees of all the sub-data sets are combined into a complete outline tree, and the sub-outline trees on the sub-data sets are recorded as gamma (f)iThe set of data vertices thereon is
The arc between the front and back sub-contour trees is recorded as
The set of all arcs is arc ═ arc0,arc1···arcn/2-1And as the sub data sets have one-to-many relationship, traversing the arc by adopting binary search, connecting the arcs meeting the relationship, and forming a complete contour tree after traversing all elements in the arc.
In step 4), the topological structure of the contour tree merged in step 3) is simplified, and the method comprises the following steps:
4.1) selecting the geometric characteristic attribute of the contour tree: persistence, volume, hyper-volume;
4.2) carrying out multi-resolution calculation on the selected geometric characteristics by using a branch decomposition method;
4.3) merging the branches with the resolution less than the set value to obtain the simplified contour tree.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention solves the problem of fuzzy object boundaries divided by the traditional transfer function, and the transfer function guided by the contour tree can clearly display the edge information of the object, thereby being beneficial to highlighting interested parts.
2. The method overcomes the defects of the traditional multi-thread contour tree method, is completely parallelized, and improves the construction speed of the method.
3. The invention eliminates the interaction among threads, fully exerts the parallel capability of multiple threads and achieves the real-time effect of the running speed.
4. The invention combines the contour tree method and the GPU-based ray projection volume rendering algorithm for the first time, and realizes high-efficiency medical volume rendering.
5. The invention adopts a new delay rendering mode, utilizes the segmentation characteristic of the contour tree, and has richer interactive operation and better rendering effect.
Drawings
FIG. 1 is a logic flow diagram of the method of the present invention.
FIG. 2 is a diagram of an example of the present invention constructed using a simple contour tree.
FIG. 3 is a graph comparing ICRM and DCRM patterns.
FIG. 4 is a graph comparing rendering results of a two-dimensional transfer function method using conventional density-gradient values with the method of the present invention.
FIG. 5 is a visual operating interface of the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments.
As shown in fig. 1, the method for constructing a multi-thread-based contour tree provided in this example includes the following specific processes: firstly, acquiring a slice image set generated by an imaging device, and forming a three-dimensional data set according to the sequence from top to bottom or from top to bottom; then, performing data equal division operation on the three-dimensional data set, and dividing the three-dimensional data set into a plurality of sub data sets according to the range interval; and then, operating each subdata set respectively, distributing 2 threads to each subdata set by utilizing a multithreading technology to calculate a split tree and a connection tree of the outline tree, and further combining the split trees and the connection trees into the sub-outline tree. And finally, combining the sub-outline trees formed by each sub-data set to form a complete outline tree, and finally simplifying the topological structure of the formed outline tree to obtain the finally required outline tree, so that the construction process of the outline tree is finished. Fig. 2 shows a schematic representation of the construction of the contour tree by a simple example, which specifically includes the following steps:
1) equally dividing the data set into subdata sets according to the size of the data set and the value range of the data, comprising the steps of:
1.1) sorting the data sets, with the height representing the value range of the data, and the sorted data sets being [ a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v ].
1.2) the sorted data are equally divided into 2 subsets [ a, b, c, d, e, f, g, h, i, j, k ] and [ l, m, n, o, p, q, r, s, t, u, v ].
1.3) expand the subdata set, m, o, l, k, n are the edge vertices of the adjacent subdata set, which are added to the 2 subdata sets of 1.2) to form new subdata sets [ a, b, c, d, e, f, g, h, i, j, k, l, m, n, o ] and [ l, m, n, o, p, q, r, s, t, u, v ].
2) Distributing two threads, respectively calculating the sub-outline trees on the sub-data sets, and executing the following steps:
2.1) scanning the subdata sets in an increasing order, and constructing a connection tree by using and searching the sets;
2.2) scanning the subdata sets in a reverse order, and constructing a split tree by using and searching the sets;
2.3) merging the split tree and the junction tree into a sub-contour tree.
To the right of (a) in FIG. 2 is a sub-profile tree formed from sub-data sets [ a, b, c, d, e, f, g, h, i, j, k, l, m, n, o ]. To the right of (b) in FIG. 2 is a sub-profile tree formed by the sub-data sets [ l, m, n, o, p, q, r, s, t, u, v ].
3) Merging the sub-profile trees of each sub-data set to form a complete profile tree, comprising the steps of:
3.1) searching an arc formed by each subdata set and the edge vertex; the arc of [ a, b, c, d, e, f, g, h, i, j, k, l, m, n, o ] is (j, k) and the arc of [ l, m, n, o, p, q, r, s, t, u, v ] is (k, j).
3.2) connecting the corresponding arcs of the adjacent sub data sets to form a complete contour tree.
Fig. 2 (c) shows the complete contour tree after merging of 2 sub-data sets.
The volume rendering requires real-time communication between the CPU and the GPU, and the volume data is usually large in data volume, which causes a large load on the CPU to transmit data. In a traditional Immedia Context rendering mode (ICRM for short), interaction between a CPU and a GPU can be completed only through one CPU core, and other CPU cores cannot be utilized, so that great computing resource waste is caused. For this purpose, the latest Deferred Contexts rendering mode (DCRM for short) is selected to complete the communication scheduling. DCRM may utilize a multi-core CPU to complete the drawing commands of the GPU. As shown in fig. 3, the ICRM can only process the volume data using the single-core CPU, and then give it to the GPU to complete the actual rendering operation. The DCRM can fully utilize the segmentation characteristic of the contour tree, disperse the segmented data into each core of the CPU for execution, and finally, uniformly deliver the data to the GPU for rendering. Therefore, the rendering efficiency can be improved, each segment data can be independently operated, a desired part is selected to be highlighted, and the value of the method for the clinical application is high.
In order to test the practical application effect of the multithreading-based contour tree construction method, a visualization method is adopted to apply the method to real medical data, and the effectiveness and feasibility of the multithreading-based rapid contour tree construction method are verified.
The invention is realized by adopting C + + language and Directx graphic library, and the experimental platform is a Dell M4800 workstation, an I74810 processor, a 16G memory and a Quadro k2100 video card. The experimental platform processes the three-dimensional medical image data by using the method and the system of the invention, and finally obtains a rendering image.
1. To illustrate the effectiveness of the multi-thread-based outline tree construction method of the present invention, serial implementation (SITour) and parallel implementation (PITour) of the open source library libTourtre were used as a comparison with the method of the present invention. The data sets of the experiment are Tooth (256x256x161), Foot (256x256x256), Head (256x256x225), Skull (256x256x256), Engine (256x256x256), respectively, and the content in the parentheses indicates the size of the data set.
Table 1 shows the results of comparing five sets of data in the SITour, PITour methods and the method of the present invention, with n representing the number of passes. As can be seen from the table, the method of the present invention has a good speed increase. With the increase of the thread number, the method has more obvious advantages.
TABLE 1 construction speed results comparison (units: seconds)
Fig. 4 is a comparison of a two-dimensional transfer function method using conventional density-gradient values with rendering results using the method of the present invention. As can be seen from the figure, the image rendered by the method has better quality, clear details and relatively obvious division of material boundaries.
2. To verify the feasibility of the method of the present invention, the rendering frame rates of the two modes are compared. The visualization interface of the way is as shown in fig. 5. On the left side of the interface is a design panel of transfer functions, which is used primarily to specify the appropriate transfer function for the segmented data. And the right side is an illumination setting panel which designates illumination parameters and enhances the reality of rendering. The middle panel is used to display the rendered results. The panel at the lower left is a setting panel of the outline tree and a display panel of the outline tree, and any interested area can be selected by flexibly changing the branches of the outline tree.
Table 2 shows the comparison of the running frame rate of the DCRM mode used in the method of the present invention with the conventional ICRM mode. As can be seen from the results in the table, the mode adopted by the method of the invention has 40-50% speed improvement, and can achieve better visual effect.
TABLE 2 frame frequency comparison results
In conclusion, the invention provides a new method for clinical diagnosis and treatment of medical image processing. In auxiliary diagnosis, the outline tree is used for guiding the design of a transfer function, so that an image with a clear structure can be generated, and the method is an effective mode. The method of the invention fully utilizes the characteristics of the contour tree, can generate high-quality images, has practical popularization value and is worth popularizing.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.
Claims (4)
1. A contour tree construction method based on multithreading is characterized in that: firstly, acquiring a slice image set generated by an imaging device, and forming a three-dimensional data set according to the sequence from top to bottom or from top to bottom; then, performing data equal division operation on the three-dimensional data set, and dividing the three-dimensional data set into a plurality of sub data sets according to the range interval; then, each subdata set is operated, 2 threads are distributed to each subdata set to calculate a split tree and a connection tree of the contour tree by utilizing a multithreading technology, the split tree and the connection tree are combined into a sub-contour tree, the sub-contour trees formed by each subdata set are combined to form a complete contour tree, and finally, the topological structure of the formed contour tree is simplified to obtain the final required contour tree; which comprises the following steps:
1) according to the size of the data set and the value range of the data, the data set is equally divided into n/2 sub-data sets by combining the number n of multiple threads supported by a computer;
2) allocating 2 threads for each subdata set for calculating a splitting tree and a connecting tree of the subdata set, and combining the splitting tree and the connecting tree into a sub-outline tree; wherein each subdata set is subjected to local calculation operation of the contour tree, and each data set xi is subjected to the calculation of the contour tree at the stepiThe following operations are performed:
2.1) scanning the sub-data set ξ in increasing orderiExtended set of θ'iConstructing a connection tree of the contour tree by using a classical parallel-searching algorithm;
2.2) scanning the sub data set ξ in descending orderiExtended set of θ'iConstructing a split tree of the contour tree by using a classical parallel-searching algorithm;
2.3) combining the connection tree and the split tree obtained in the first two steps, and combining the nodes with the degree of 2 to form a contour tree;
3) merging the sub-outline trees on all the sub-data sets into a complete outline tree;
4) simplifying the topological structure of the combined contour tree in the step 3) to obtain the final required contour tree.
2. The multithread-based contour tree construction method of claim 1, wherein: in step 1), the data set is divided equally, the number of the threads is n, the data set is divided into n/2 sub-data sets, and each sub-data set contains the vertexes with the equal number:
where R represents a real number, μ represents an element of the dataset, i, j represent the order of the subdata set, ξiRepresents the ith sub-dataset, | δ0|iIs representative of xiiThe number of vertices in (1);
to accomplish this data equal division operation, the following steps are required:
1.1) arranging all vertexes on the data set in a small-to-large order by using a quick sorting algorithm, and storing elements by using an array;
1.2) equally dividing the ordered array into n/2 arraysi，θiThe corresponding subdata set is xii；
1.3) extension of θiExtended to theta'i，θ′iIs formed by thetaiPlus with
And
connected vertices make up ofi +、fi -Is xiiEnd point of, i.e. ξi＝(fi -，fi +) Definition of
Is and fi +The edges of the connection are connected with each other,
is and fi -The edges of the connection.
3. The multithread-based contour tree construction method of claim 1, wherein: in step 3), the sub-outline trees on each sub-data set are used as input, the sub-outline trees of all the sub-data sets are combined into a complete outline tree, and the sub-outline trees on the sub-data sets are recorded as gamma (f)iThe set of data vertices thereon is [ f ]i -，fi +]The arc between the front and rear sub-contour trees is recorded as
The set of all arcs is arc ═ arc0，arc1…arcn/2-1And as the sub data sets have one-to-many relationship, traversing the arc by adopting binary search, connecting the arcs meeting the relationship, and forming a complete contour tree after traversing all elements in the arc.
4. The multithread-based contour tree construction method of claim 1, wherein: in step 4), the topological structure of the contour tree merged in step 3) is simplified, and the method comprises the following steps:
4.1) selecting the geometric characteristic attribute of the contour tree: persistence, volume, hyper-volume;
4.2) carrying out multi-resolution calculation on the selected geometric characteristics by using a branch decomposition method;
4.3) merging the branches with the resolution less than the set value to obtain the simplified contour tree.
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