CN101976465A - Acceleration improvement algorithm based on cube edge sharing equivalent point - Google Patents
Acceleration improvement algorithm based on cube edge sharing equivalent point Download PDFInfo
- Publication number
- CN101976465A CN101976465A CN 201010522056 CN201010522056A CN101976465A CN 101976465 A CN101976465 A CN 101976465A CN 201010522056 CN201010522056 CN 201010522056 CN 201010522056 A CN201010522056 A CN 201010522056A CN 101976465 A CN101976465 A CN 101976465A
- Authority
- CN
- China
- Prior art keywords
- seamed edge
- equivalent point
- grid
- edge
- seamed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000001133 acceleration Effects 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 abstract description 9
- 238000012545 processing Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 11
- 238000011160 research Methods 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 4
- 238000012800 visualization Methods 0.000 description 4
- 238000002595 magnetic resonance imaging Methods 0.000 description 3
- 238000011960 computer-aided design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005481 NMR spectroscopy Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
Images
Landscapes
- Image Processing (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The invention relates to an acceleration improvement algorithm based on cube edge sharing an equivalent point which comprises the following steps of: (1) storing related information of each edge of a cube by using a global array variable; (2) calculating equivalent point coordinates: before the equivalent point is calculated, firstly judging whether the edge is provided with the equivalent point or not , if so, extracting data stored in an array, wherein the data are used for calculating a triangular patch; if not, calculating coordinates and a normal vector of the equivalent point through linear interpolation; and recording the equivalent point of the edge of a current voxel, and giving an assignment for the equivalent point of the edge of other voxels sharing the edge; and (3) calculating the triangular patch by using a Marching Cubes algorithm, and processing the generated triangular patch through GPU (Ground Power Unit) acceleration image processing software to obtain a three-dimensional graph. By utilizing the method of the cube edges sharing the equivalent point, the algorithm efficiency can be improved and the three-dimensional reconstruction problem of medical images is effectively solved.
Description
Technical field
The present invention relates to a kind of acceleration improvement algorithm of sharing equivalent point based on the cube seamed edge.
Background technology
Medical image is visual to be the important branch that medical science and computer science are calculated the visual research field, also is the popular point of scholar's research always.The medical image three-dimensional reconstruction is exactly the CT (Computed Tomography-computer tomography technology) that takes in hospital, MRI digitized image data such as (Nuclear Magnetic Resonance Imaging-Magnetic resonance imagings), by related algorithm, the mode with three-dimensional modeling represents intuitively in computing machine.
Visualization in scientific computing (be called for short visually, English is Visualization in Scientific Computing, is called for short ViSC) is an important research direction of computer graphics, is the frontier of figure science.In fact, can be called any abstract things, the expression that process becomes graph image visual.The basic meaning of visualization in scientific computing is the principle and the method for utilization computer graphics or general figures, and the large-scale data of generations such as scientific and engineering calculating is converted to figure, image, shows with form intuitively.It relates to a plurality of research fields such as computer graphics, Flame Image Process, computer vision, computer-aided design (CAD) and graphic user interface, has become the important directions of current computer graphics research.
Traditional medical image technology is just at first taken the image data of a certain tomography of human body with technology such as CT, the doctor observes, diagnoses by film or screen display then.But the observed just two dimensional image of fixed viewpoint of doctor, this causes the doctor mainly to be based on the analysis of the image of being seen when judging patient's the state of an illness, and need very big subjectivity be arranged in conjunction with doctor's practical experience.Along with the arrival of digitizing, network times, the medical image technology enters a brand-new era.In the medical image field, (as CT, appearance MRI) becomes the main flow of medical image technology to multiple new digitalized image technology.Utilize computer technology that two-dimensional slice image is analyzed and handled, reconstruct three-dimensional model, can allow the doctor carry out medical pathologies from various visual angles, at many levels and observe and analyze.Thereby can improve the accuracy and the correctness of medical diagnosis greatly.This has increased valuable value for the application of medical image.So from the nineties in 20th century, the medical image three-dimensional visualization technology is research both at home and abroad and the focus of using always.
Marching Cubes (mobile cube) algorithm is the classic algorithm that the 3 d data field contour surface generates.Existing 3 D medical image technology based on Marching Cubes technology because the complicacy of the algorithm of Marching Cubes own, handle big data quantity to influence the data age rate low.
Marching Cubes algorithm is more directly perceived, it is that each cube is handled, and calculates this cubical index value by 8 summits, and removing to find which seamed edge according to index value then has equivalent point, by linear interpolation arithmetic and obtain intersection point on the seamed edge, draw out tri patch.
Yet for one of them cubical seamed edge, it not only belongs to this current cube, and it also belongs to other three cubes adjacent with current cube simultaneously.Promptly a seamed edge is to be shared by 4 adjacent cubes.If calculate according to the Marching Cubes algorithm of simplicity, if 4 cubes all are at this seamed edge equivalent point to be arranged, calculate and can repeat 4 times, the processing expenditure of having wasted computing machine has greatly increased time of algorithm.
Summary of the invention
In view of the inefficient problem of above-mentioned existing Marching Cubes algorithm process, the objective of the invention is to propose a kind of acceleration based on the shared equivalent point of cube seamed edge and improve algorithm, it can solve medical image three-dimensional reconstruction problem more effective than prior art, more efficiently.
For realizing above-mentioned technical purpose, the present invention adopts following technical scheme:
A kind of acceleration based on the shared equivalent point of cube seamed edge improves algorithm, comprises the steps:
1) relevant information of preserving cubical every seamed edge with an overall array variable comprises whether this seamed edge has equivalent point, and the coordinate figure of equivalent point and normal vector;
2) coordinate figure of calculating equivalent point comprises the steps:
A) before calculating equivalent point, judge whether this seamed edge has equivalent point earlier, if this seamed edge has equivalent point, then directly existing the data extract in the array to come out to be used to calculate tri patch; If this seamed edge does not have equivalent point, jump to step b);
B) calculate the coordinate and the normal vector of equivalent point by linear interpolation, the equivalent point of the seamed edge of current voxel is noted, and give assignment the equivalent point of the seamed edge of other voxels of shared this seamed edge;
3) with Marching Cubes algorithm computation tri patch, and quicken image processing software by GPU and handle the tri patch that generates, draw and obtain three-dimensional picture.
Further, described step 3) is to adopt the computing simultaneously of two-wire journey Marching Cubes algorithm to generate tri patch, and gathers the tri patch of generation, quickens image processing software by GPU then and handles tri patch, draws and obtains three-dimensional picture.
Further, b described step 2)) substep comprises following three aspects:
1) current voxel is expressed as grid[i] [j] [k], represent that this cube is individual at the i of directions X, the j of Y direction, the k of Z direction; Wherein i, j, k are positive integer;
The equivalent point of the seamed edge of the current voxel of 2) noting only comprises x, y, the equivalent point of 9 seamed edges of three faces on the z direction, these three faces are expressed as face [1,10,5,9], face [2,10,6,11], face [6,5,4,7], each face is represented by 4 seamed edges respectively, have 9 seamed edges, be expressed as seamed edge 1, seamed edge 10, seamed edge 5, seamed edge 9, seamed edge 2, seamed edge 4, seamed edge 11, seamed edge 6, seamed edge 7 respectively;
3) described current cubical 9 seamed edges are corresponding with the equivalent point of the seamed edge of other voxels of sharing these 9 seamed edges, and its corresponding relation is as follows:
Seamed edge 10 and grid[i+1] seamed edge 11 and the grid[i of [j] [k]] seamed edge 9 of [j+1] [k] has identical equivalent point;
Seamed edge 9 and grid[i+1] seamed edge 8 of [j] [k] has identical equivalent point;
Seamed edge 2 and grid[i] seamed edge 0 of [j+1] [k] has identical equivalent point;
Seamed edge 4 and grid[i] seamed edge 0 of [j] [k+1] has identical equivalent point;
Seamed edge 11 and grid[i] seamed edge 8 of [j] [k+1] has identical equivalent point;
Seamed edge 6 and grid[i] seamed edge 4 and the grid[i of [j+1] [k]] seamed edge 2 of [j] [k+1] has identical equivalent point;
Seamed edge 7 and grid[i] seamed edge 3 of [j] [k+1] has identical equivalent point.
The present invention utilizes the cube seamed edge to share the method for equivalent point, improves efficiency of algorithm; And utilize the characteristic of present computer CPU multinuclear, propose the method for two-wire journey parallel computation iso-surface patch equivalent point, improve the speed of iso-surface patch greatly.Facts have proved, adopt the present invention to compare, efficiency of algorithm can be improved 20% to 30% with existing Marching Cubes algorithm.
Description of drawings
Fig. 1 is the cubical summit and the seamed edge numbering synoptic diagram of the embodiment of the invention;
Fig. 2 is the shared face and the shared limit synoptic diagram of two cube voxels of the embodiment of the invention;
Fig. 3 is the single-threaded parallel computation accelerating algorithm schematic flow sheet of the embodiment of the invention;
Fig. 4 is the two-wire journey parallel computation accelerating algorithm schematic flow sheet of the embodiment of the invention.
Embodiment
Below, come in conjunction with the accompanying drawings and embodiments the specific embodiment of the present invention is elaborated.
The acceleration of present embodiment improves algorithm and comprises following two aspects:
1, shares the acceleration improvement of equivalent point based on the cube seamed edge
After finding the seamed edge that equivalent point is arranged, it or not the coordinate that calculates equivalent point immediately by linear interpolation, but the relevant information of preserving every seamed edge in the cube with an overall array variable, comprise whether this seamed edge has equivalent point, if equivalent point is arranged, how many coordinate figures of equivalent point is, also notes the normal vector of equivalent point simultaneously.
Before calculating equivalent point, judge whether this seamed edge has equivalent point earlier, if having, then directly existing the data extract in the array to come out to be used for ensuing flow process.
If this seamed edge of query display does not have equivalent point, just calculate the coordinate and the normal vector of equivalent point by linear interpolation.This step is the most key step of algorithm of the present invention, calculate the coordinate of equivalent point and the step of normal vector at this, not only just current voxel grid[i] equivalent point of the seamed edge edge of [j] [k] is to being noted, also to give assignment to other the equivalent point of seamed edge edge of voxel of sharing this edge, so just can inquire this seamed edge in ensuing algorithm has had equivalent point, without double counting.
The cube of supposing Fig. 1 is individual at the i of directions X, and the j of Y direction, the k of Z direction is individual, i.e. grid[i] [j] [k], i, j, k are positive integer.Because our algorithm computation direction is to advance according to x, y, z positive dirction, so inevitable 9 seamed edges that only need to consider three faces get final product, [1,10,5,9], [2,10,6,11], [6,5,4,7] these three faces are shared seamed edge equivalent point not to be arranged with also the cube of not handling, total 1,10,5,9,2,4,11,6,79 seamed edges that are numbered.
Fig. 2 shows is the shared face of being made up of seamed edge 1,10,5,9 [1,10,5,9] that stretches to the x direction.The cube on the left side has marked the numbering of summit and seamed edge, and is corresponding, though the cubical summit on the right and the numbering and the left side is different, corresponding relation arranged.For example the cubical seamed edge 1 on the left side is the cubical seamed edge 3 on the right.
Be the information of 9 pairing other cube seamed edge equivalent points of seamed edge below:
Seamed edge 10 and grid[i+1] seamed edge 11 of [j] [k] has identical equivalent point, and with grid[i] seamed edge 9 of [j+1] [k] has identical equivalent point;
Seamed edge 9 and grid[i+1] seamed edge 8 of [j] [k] has identical equivalent point;
Seamed edge 2 and grid[i] seamed edge 0 of [j+1] [k] has identical equivalent point;
Seamed edge 4 and grid[i] seamed edge 0 of [j] [k+1] has identical equivalent point;
Seamed edge 11 and grid[i] seamed edge 8 of [j+1] [k] has identical equivalent point;
Seamed edge 6 and grid[i] seamed edge 4 of [j+1] [k] has identical equivalent point, and with grid[i] seamed edge 2 of [j] [k+1] has identical equivalent point;
Seamed edge 7 and grid[i] seamed edge 3 of [j] [k+1] has identical equivalent point.
The algorithm computation equivalent point that utilization is above can be so that need not repeat the equivalent point of interpolation calculation seamed edge, and 4 calculating can be reduced to 1 calculating, has improved the efficient of algorithm greatly.
2, the acceleration based on the parallel computation of two-wire journey improves
For Marching Cubes algorithm, because the minimum treat unit of algorithm is a voxel, it only calculates the equivalent tri patch in the voxel, and the computation process of this algorithm need not rely on other cubes (certainly, the front is mentioned utilizing and shared the seamed edge equivalent point).So Marching Cubes can carry out the algorithm of parallel computation with the two-wire journey.
As shown in Figure 4, Fig. 4 is a two-wire journey parallel computation accelerating algorithm embodiment schematic flow sheet of the present invention, computing when present embodiment can be realized two-wire journey Marching Cubes, then the tri patch that calculates is gathered, quicken image processing software by GPU and handle the tri patch that generates, thereby draw out three-dimensional picture.
The triggering of described thread need be called the AfxBeginThread function, function prototype is as follows: CWinThread*AfxBeginThread (AFX_THREADPROC pfnThreadProc, LPVOID pParam, int nPriority=THREAD_PRIORITY_NORMAL, UINT nStackSize=0, DWORD dwCreateFlags=0, LPSECURITY_ATTRIBUTES lpSecurityAttrs=NULL);
By specifying 2 thread function thread1, thread2 starts the two-wire journey:
AfxBeginThread(thread1，this，THREAD_PRIORITY_ABOVE_NORMAL)；
AfxBeginThread(thread2，this，THREAD_PRIORITY_ABOVE_NORMAL)。
Fig. 3 is the single-threaded parallel computation accelerating algorithm of a present invention embodiment schematic flow sheet, and the difference of Fig. 3 and Fig. 4 just is: Fig. 4 adopts the two-wire journey to handle raw data, carries out the generation of tri patch simultaneously, gathers the tri patch of generation at last.
Claims (3)
1. the acceleration based on the shared equivalent point of cube seamed edge improves algorithm, it is characterized in that comprising the steps:
1) relevant information of preserving cubical every seamed edge with an overall array variable comprises whether this seamed edge has equivalent point, and the coordinate figure of equivalent point and normal vector;
2) coordinate figure of calculating equivalent point comprises the steps:
A) before calculating equivalent point, judge whether this seamed edge has equivalent point earlier, if this seamed edge has equivalent point, then directly existing the data extract in the array to come out to be used to calculate tri patch; If this seamed edge does not have equivalent point, jump to step b);
B) calculate the coordinate and the normal vector of equivalent point by linear interpolation, the equivalent point of the seamed edge of current voxel is noted, and give assignment the equivalent point of the seamed edge of other voxels of shared this seamed edge;
3) with Marching Cubes algorithm computation tri patch, and quicken image processing software by GPU and handle the tri patch that generates, draw and obtain three-dimensional picture.
2. the acceleration improvement algorithm of sharing equivalent point based on the cube seamed edge as claimed in claim 1, it is characterized in that: step 3) is to adopt the computing simultaneously of two-wire journey Marching Cubes algorithm to generate tri patch, and gather the tri patch of generation, quicken image processing software by GPU then and handle tri patch, draw and obtain three-dimensional picture.
3. the acceleration of sharing equivalent point based on the cube seamed edge as claimed in claim 1 improves algorithm, it is characterized in that described step 2) b) substep comprises following three aspects:
1) current voxel is expressed as grid[i] [j] [k], represent that this cube is individual at the i of directions X, the j of Y direction, the k of Z direction; Wherein i, j, k are positive integer;
The equivalent point of the seamed edge of the current voxel of 2) noting only comprises x, y, the equivalent point of 9 seamed edges of three faces on the z direction, these three faces are expressed as face [1,10,5,9], face [2,10,6,11], face [6,5,4,7], each face is represented by 4 seamed edges respectively, have 9 seamed edges, be expressed as seamed edge 1, seamed edge 10, seamed edge 5, seamed edge 9, seamed edge 2, seamed edge 4, seamed edge 11, seamed edge 6, seamed edge 7 respectively;
3) described current cubical 9 seamed edges are corresponding with the equivalent point of the seamed edge of other voxels of sharing these 9 seamed edges, and its corresponding relation is as follows:
Seamed edge 1 and grid[i+1] seamed edge 3 of [j] [k] has identical equivalent point;
Seamed edge 10 and grid[i+1] seamed edge 11 and the grid[i of [j] [k]] seamed edge 9 of [j+1] [k] has identical equivalent point;
Seamed edge 5 and grid[i+1] seamed edge 7 and the grid[i of [j] [k]] seamed edge 1 of [j] [k+1] has identical equivalent point;
Seamed edge 9 and grid[i+1] seamed edge 8 of [j] [k] has identical equivalent point;
Seamed edge 2 and grid[i] seamed edge 0 of [j+1] [k] has identical equivalent point;
Seamed edge 4 and grid[i] seamed edge 0 of [j] [k+1] has identical equivalent point;
Seamed edge 11 and grid[i] seamed edge 8 of [j] [k+1] has identical equivalent point;
Seamed edge 6 and grid[i] seamed edge 4 and the grid[i of [j+1] [k]] seamed edge 2 of [j] [k+1] has identical equivalent point;
Seamed edge 7 and grid[i] seamed edge 3 of [j] [k+1] has identical equivalent point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010522056 CN101976465A (en) | 2010-10-27 | 2010-10-27 | Acceleration improvement algorithm based on cube edge sharing equivalent point |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010522056 CN101976465A (en) | 2010-10-27 | 2010-10-27 | Acceleration improvement algorithm based on cube edge sharing equivalent point |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101976465A true CN101976465A (en) | 2011-02-16 |
Family
ID=43576347
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010522056 Pending CN101976465A (en) | 2010-10-27 | 2010-10-27 | Acceleration improvement algorithm based on cube edge sharing equivalent point |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101976465A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103093503A (en) * | 2013-02-08 | 2013-05-08 | 河北大学 | Lung parenchyma area surface model establishment method based on computed tomography (CT) image |
| CN109492069A (en) * | 2018-11-02 | 2019-03-19 | 中国地质大学（武汉） | A kind of marching cube parallel calculating method and system based on ray computing unit |
| CN109934922A (en) * | 2019-03-14 | 2019-06-25 | 哈尔滨理工大学 | A kind of three-dimensional rebuilding method based on improvement MC algorithm |
| CN110021059A (en) * | 2019-04-11 | 2019-07-16 | 中国人民解放军国防科技大学 | Efficient Marching cube isosurface extraction method and system without redundant computation |
| US10366534B2 (en) | 2015-06-10 | 2019-07-30 | Microsoft Technology Licensing, Llc | Selective surface mesh regeneration for 3-dimensional renderings |
| CN112016572A (en) * | 2020-09-09 | 2020-12-01 | 北京推想科技有限公司 | Method and device for extracting isosurface and method and device for drawing image |
| CN113888700A (en) * | 2021-10-20 | 2022-01-04 | 哈尔滨理工大学 | Medical image three-dimensional reconstruction method based on voxel growth |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1430185A (en) * | 2001-12-29 | 2003-07-16 | 田捷 | Ultralarge scale medical image surface reconstruction method based on single-layer surface tracking |
| CN101266691A (en) * | 2008-04-24 | 2008-09-17 | 浙江大学 | A polygonal grid model amalgamation method for any topology |
| US20090295803A1 (en) * | 2008-05-27 | 2009-12-03 | Simpleware Limited | Image processing method |
| CN101599181A (en) * | 2009-07-01 | 2009-12-09 | 浙江大学 | A kind of real-time drawing method of algebra B-spline surface |
| US8000941B2 (en) * | 2007-12-30 | 2011-08-16 | St. Jude Medical, Atrial Fibrillation Division, Inc. | System and method for surface reconstruction from an unstructured point set |
-
2010
- 2010-10-27 CN CN 201010522056 patent/CN101976465A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1430185A (en) * | 2001-12-29 | 2003-07-16 | 田捷 | Ultralarge scale medical image surface reconstruction method based on single-layer surface tracking |
| US8000941B2 (en) * | 2007-12-30 | 2011-08-16 | St. Jude Medical, Atrial Fibrillation Division, Inc. | System and method for surface reconstruction from an unstructured point set |
| CN101266691A (en) * | 2008-04-24 | 2008-09-17 | 浙江大学 | A polygonal grid model amalgamation method for any topology |
| US20090295803A1 (en) * | 2008-05-27 | 2009-12-03 | Simpleware Limited | Image processing method |
| CN101599181A (en) * | 2009-07-01 | 2009-12-09 | 浙江大学 | A kind of real-time drawing method of algebra B-spline surface |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103093503A (en) * | 2013-02-08 | 2013-05-08 | 河北大学 | Lung parenchyma area surface model establishment method based on computed tomography (CT) image |
| CN103093503B (en) * | 2013-02-08 | 2015-08-05 | 河北大学 | Based on the method for building up of the pulmonary parenchyma region surface model of CT image |
| US10366534B2 (en) | 2015-06-10 | 2019-07-30 | Microsoft Technology Licensing, Llc | Selective surface mesh regeneration for 3-dimensional renderings |
| CN109492069A (en) * | 2018-11-02 | 2019-03-19 | 中国地质大学（武汉） | A kind of marching cube parallel calculating method and system based on ray computing unit |
| CN109492069B (en) * | 2018-11-02 | 2020-06-26 | 中国地质大学（武汉） | Ray computing unit-based mobile cube parallel computing method and system |
| CN109934922A (en) * | 2019-03-14 | 2019-06-25 | 哈尔滨理工大学 | A kind of three-dimensional rebuilding method based on improvement MC algorithm |
| CN110021059A (en) * | 2019-04-11 | 2019-07-16 | 中国人民解放军国防科技大学 | Efficient Marching cube isosurface extraction method and system without redundant computation |
| CN110021059B (en) * | 2019-04-11 | 2023-02-07 | 中国人民解放军国防科技大学 | High-efficiency Marking Cubes isosurface extraction method and system without redundant computation |
| CN112016572A (en) * | 2020-09-09 | 2020-12-01 | 北京推想科技有限公司 | Method and device for extracting isosurface and method and device for drawing image |
| CN113888700A (en) * | 2021-10-20 | 2022-01-04 | 哈尔滨理工大学 | Medical image three-dimensional reconstruction method based on voxel growth |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101976465A (en) | Acceleration improvement algorithm based on cube edge sharing equivalent point | |
| CN103236058B (en) | Obtain the method for volume of interest of four-dimensional heart image | |
| CN101393653B (en) | Method for reconstructing three dimensional model of complete teeth through CT data of dentognathic gypsum model and dentognathic panoramic perspective view | |
| CN101625767B (en) | Method for rebuilding point-cloud type three-dimensional surface of nonparallel outline medical image | |
| CN107230206A (en) | A kind of 3D Lung neoplasm dividing methods of the super voxel sequence lung images based on multi-modal data | |
| EP3398032A1 (en) | Transform lightweight skeleton and using inverse kinematics to produce articulate skeleton | |
| CN101692286A (en) | Method for acquiring three-view drawing of medical image | |
| CN102930602A (en) | Tomography-image-based facial skin three-dimensional surface model reconstructing method | |
| CN106570859A (en) | Tooth image analysis system | |
| CN102835974A (en) | Method for medical ultrasound three-dimensional imaging based on parallel computer | |
| CN101324962A (en) | Parallel processing method drawn by pre-projection light ray projection body | |
| CN109727306A (en) | A kind of backbone medical image three-dimensional visualization method based on VTK | |
| CN100541541C (en) | Ultra-large medical image method of surface reconstruction based on the individual layer surface tracking | |
| CN109509259B (en) | Medical image three-dimensional reconstruction isosurface grid extraction method | |
| Wang et al. | A marching cube algorithm based on edge growth | |
| CN101561937A (en) | Big data size medical image three-dimensional interaction method based on common computer | |
| CN114863061A (en) | Three-dimensional reconstruction method and system for remote monitoring medical image processing | |
| CN103745495A (en) | Medical volume data based volume rendering method | |
| CN106898043A (en) | A kind of PACK based on virtual reality | |
| CN109636915B (en) | Medical image three-dimensional reconstruction isosurface grid extraction method based on edge growth | |
| CN104463940B (en) | A kind of compound tree parallel constructing method based on GPU | |
| CN105957141B (en) | A kind of three-dimensional flower modeling method based on symmetrical structure | |
| CN113421339A (en) | Medical image three-dimensional reconstruction isosurface grid accelerated extraction method based on voxel growth | |
| CN101174331A (en) | Maximum density projection generating method for medical image | |
| Englmeier et al. | Virtual reality and multimedia human-computer interaction in medicine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110216 |