2005 Fiscal Year Final Research Report Summary
Study on 3 Dimensional registration for pipe-shaped objects with branches
| Project/Area Number |
16560370
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Faculty of Computer and Information Sciences, Hosei University |
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Principal Investigator |
HANAIZUMI Hiroshi Hosei University, Faculty of Computer and Information Sciences, Professor, 情報科学部, 教授 (60143385)
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| Project Period (FY) |
2004 – 2005
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| Keywords | Successive region growing / Homotopy / Significance / Principal branch model / Adaptive division into tri-pyramids / Helical CT data / Multi-slice CT data / Artery-vein separation of lung vessels |
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| Research Abstract |
The objective of the research was to construct a system for lung cancer screening using helical CT data. The basic idea for constructing such a screening system came from that 3 Dimensional processing easily extracted tumor from 3D helical CT data. In this system, tumors were extracted as changes of 3D shape of lung vessels between multi-temporal helical CT data. Concrete targets of the research were construction of sub-system for recognizing lung vessels, one for matching branch of lung vessels between multi-temporal helical CT data and one for registering 3D shape of lung vessels.
1.Recognition of lung vessels : A method named as successive region growing (SRG) was developed for the recognition of pipe-shaped object with branches. From the inside of the heart, SRG recognized a surface element by using layer by layer processing. In the vessels, voxels attached to the original layer were regarded as the neighboring layer. The gravity point of the layer was regarded as the part of the sk
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eleton. The branching point was found when new layer consisted of two or more islands. This meant the homotopy changes between the original layer and new one. An index, significance was also introduced to reject false branches and to branch matching.
2.Vessel branch matching : Firstly, skeletons of lung vessels among multi-temporal helical CT data were registered. Originally, principal branch model was developed and branches are matched by using only significance on the assumption that vessels had only binary branching. Some vessels, however, had apparent triple branching near the heart and caused uncertainty in branching. So, spherical surface search algorithm was newly developed for the branch matching. Spheres were put on a previously matched branching point pair and their radii were synchronously increased until one of surfaces reached to the next branching point. Relative position of crossing points among skeletons and sphere surfaces were evaluated and nearest pairs were selected as the matched pair of branches. The performance was evaluated by using small scale vessel data with branches.
3.Registration of vessels : A piece-wise linear transformation method was proposed for the co-registration of vessel bodies between multi-temporal helical CT data using the skeleton matching results. In the method, the data region was divided into tri-pyramids whose vertices were branching points matched. After the division, 3D Affine transformation was applied to the corresponding pair of tri-pyramids. The performance was evaluated by using artificially distorted lung vessels which were originally recognized and extracted by SRG from a multi-slice CT data set. Less
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