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Automatic Centerline Extraction for Virtual Colonoscopy 作作 : Ming Wan, Zhengrong Liang*, Qi Ke, Lichan Hong, Ingmar Bitter, and Arie Kaufman 作作 : IEEE Transaction on Medical Imaging, Dec. 2002, pp. 1450 - 1460 作作 : 作作作 作作作作 : 作作作

Automatic Centerline Extraction for Virtual Colonoscopy

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Automatic Centerline Extraction for Virtual Colonoscopy. 作者 : Ming Wan, Zhengrong Liang*, Qi Ke, Lichan Hong, Ingmar Bitter, and Arie Kaufman 出處 : IEEE Transaction on Medical Imaging, Dec. 2002, pp. 1450 - 1460 學生 : 林上智 指導老師 : 張顧耀. Outline. Introduction Requirements - PowerPoint PPT Presentation

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Page 1: Automatic Centerline Extraction for Virtual Colonoscopy

Automatic Centerline Extractionfor Virtual Colonoscopy

作者 :

Ming Wan, Zhengrong Liang*, Qi Ke, Lichan Hong, Ingmar Bitter, and Arie Kaufman出處 :

IEEE Transaction on Medical Imaging, Dec. 2002, pp. 1450 - 1460

學生 :林上智指導老師 :張顧耀

Page 2: Automatic Centerline Extraction for Virtual Colonoscopy

2

Outline

IntroductionRequirementsBrief Review of Existing Algorithms

for VCDescription of New AlgorithmResults Conclusion

Page 3: Automatic Centerline Extraction for Virtual Colonoscopy

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Introduction

Virtual endoscopy is an integration of medical imaging computer graphics

Advantages: noninvasive cost-effective highly accurate

Page 4: Automatic Centerline Extraction for Virtual Colonoscopy

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Requirements(1/2)

1.Connectivity: centerline is a sequence of directly connected v

oxels. 6- , 18- , 26- connected

2. Centricity: centerline should stay away from the colon wall

3. Singularity: centerline should be a single path of one-voxel

width

Page 5: Automatic Centerline Extraction for Virtual Colonoscopy

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Requirements(2/2)

4. Detectability : branch area

5. Automation: fully automatic procedure

6. Efficiency: seconds on PC platform

Page 6: Automatic Centerline Extraction for Virtual Colonoscopy

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Outline

IntroductionRequirementsBrief Review of Existing Algorithms

for VCDescription of New AlgorithmResults Conclusion

Page 7: Automatic Centerline Extraction for Virtual Colonoscopy

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Brief Review of Existing Algorithms for VC

Manual Extraction: manually mark the center of each colon region

on each image

Topological Thinning: peels off a volumetric object layer by layer

Page 8: Automatic Centerline Extraction for Virtual Colonoscopy

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Topological Thinning

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Outline

IntroductionRequirementsBrief Review of Existing Algorithms

for VCDescription of New AlgorithmResults Conclusion

Page 10: Automatic Centerline Extraction for Virtual Colonoscopy

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DFB / DFS

DFS: distance from a user-specified source point to e

ach voxelDFB: ( DFB-cost = 1/DFB )

distance from each inside voxel to the nearest object boundary

SDFS

DFB A

B

Page 11: Automatic Centerline Extraction for Virtual Colonoscopy

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Description of New Algorithm1.Construction of a MST tree:

minimum-cost spanning tree First:

converts the CT volume with DFB-distances to a 3D directed weighted graph.

Second: builds up a MST tree from the weighted graph

• Dijkstra’s shortest path technique.

DFB-cost

Page 12: Automatic Centerline Extraction for Virtual Colonoscopy

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Description of New Algorithm

2. Extraction of Colon Centerline and Branches does not specify the end point of the colon cent

erline Find inside voxel with the maximum DFS-value

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Modified Dijkstra Algorithm

SourceCurrent

B

DFS(C)

Page 14: Automatic Centerline Extraction for Virtual Colonoscopy

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圖解

start

current

B1B2

B26

有相鄰 26個點

找 DFB COST最小的點也就是 DFB最大的點

B3

Page 15: Automatic Centerline Extraction for Virtual Colonoscopy

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Branch detection algorithm(1/2)

Step1: Scan the centerline by tracking back from

end point(E) to start point(S)

Step2: For each centerline voxel C, check its 24 neigh

bors and find those voxel Bi Pathlink pointing to C

Page 16: Automatic Centerline Extraction for Virtual Colonoscopy

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Branch detection algorithm(2/2)

Setp3: for each voxel Bi

Record voxel C to be the closet centerline voxel

Find the voxel with largest DFS-distance,Ti.

Length of branch DFS(Ti) – DFS(C).

Page 17: Automatic Centerline Extraction for Virtual Colonoscopy

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Results

Machine PC platform

CPU :Intel Pentium 700-MHz processor Memory: 655 MB

Data: 44 human colon datasets.

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Results

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Conclusion

Extend their centerline algorithm to study more complicated human organs with tree structures as airways and blood vessels.