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Automatic Centerline Extraction for Virtual Colonoscopy

This paper presents a novel algorithm for the automatic extraction of colon centerlines in virtual colonoscopy (VC). The method enhances traditional techniques by addressing key requirements such as connectivity, centricity, singularity, detectability, automation, and efficiency. By converting CT volumes into a 3D weighted graph and employing a modified Dijkstra’s algorithm, the proposed solution effectively identifies the centerline and branches without manually specifying endpoints. Results demonstrate improvements on a PC platform using 44 human colon datasets. The approach holds promise for more complex organ studies.

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Automatic Centerline Extraction for Virtual Colonoscopy

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  1. 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

  2. Outline • Introduction • Requirements • Brief Review of Existing Algorithms for VC • Description of New Algorithm • Results • Conclusion

  3. Introduction • Virtual endoscopy is an integration of • medical imaging • computer graphics • Advantages: • noninvasive • cost-effective • highly accurate

  4. Requirements(1/2) • 1.Connectivity: • centerline is a sequence of directly connected voxels. • 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

  5. Requirements(2/2) • 4. Detectability : • branch area • 5. Automation: • fully automatic procedure • 6. Efficiency: • seconds on PC platform

  6. Outline • Introduction • Requirements • Brief Review of Existing Algorithms for VC • Description of New Algorithm • Results • Conclusion

  7. 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

  8. Topological Thinning

  9. Outline • Introduction • Requirements • Brief Review of Existing Algorithms for VC • Description of New Algorithm • Results • Conclusion

  10. DFB / DFS • DFS: • distance from a user-specified source point to each voxel • DFB: ( DFB-cost = 1/DFB ) • distance from each inside voxel to the nearest object boundary B DFB A DFS S

  11. Description of New Algorithm • 1.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

  12. Description of New Algorithm • 2. Extraction of Colon Centerline and Branches • does not specify the end point of the colon centerline • Find inside voxel with the maximum DFS-value

  13. Modified Dijkstra Algorithm Current B Source DFS(C)

  14. 圖解 找DFB COST最小的點 也就是DFB最大的點 B1 B2 current B3 start 有相鄰26個點 B26

  15. 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 neighbors and find those voxel Bi • Pathlink pointing to C

  16. 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).

  17. Results • Machine • PC platform • CPU :Intel Pentium 700-MHz processor • Memory: 655 MB • Data: • 44 human colon datasets.

  18. Results

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

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