2003 Fiscal Year Final Research Report Summary
Studies of PC cluster-based parallel processing for large-scale medical images on navigation system of the next generation surgery
| Project/Area Number |
14580374
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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 |
計算機科学
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| Research Institution | Osaka University |
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Principal Investigator |
HAGIHARA Kenichi Osaka University, Graduate School of Information Science and Technology, Professor, 大学院・情報科学研究科, 教授 (00133140)
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| Project Period (FY) |
2002 – 2003
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| Keywords | 3 dimensional image / medical image / non-rigid registration / rigid registration / volume rendering / parallel processing / data distribution / PC cluster |
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| Research Abstract |
The following two problems are very important in medical image processing. In this study, PC cluster-based parallel programs for these problems are developed, This PC cluster has 64 PCs, each of which has two Pentium-3 (1GHz) processors with 2GB main memory, connected with Myrinet 2000 communication network.
(1) Volume rendering:
To render at least 512 x 512 x 512 voxel volumes in real-time, we have developed a sort-last parallel volume rendering method fqr distributed memory multiprocessors. Our sort-last method consists of two methods, Hsu' s segmented ray casting and our divided-screenwise hierarchical (DSH) composition, in which each processor produces a subimage and merges all the produced subimages into the final image. This paper describes the DSH method, which aims at achieving high performance composition on a large number of processors: Ourimplementation on the PC cluster can composite a 512 x 512 pixel image about twice as fast as an existing method, the binary-swap method, so that can render a 512 x 512 x 224 voxel volume at approximately eight frames per second (fps).
(2) No rigid image registration:
Our algorithm realizes scalable registration for high-resolution three-dimensional (3-D) images by employing three techniques: (1) data distribution; (2) data-parallel processing; and (3) dynamic load balancing. The experimental results show that our parallel implementation on the PC cluster registers liver CT images of 512 X 512 X 159 voxels within 8 minutes while a sequential implementation takes 12 hours. Furthermore, our implementation allows processors to use less memory, and thereby enables us to align 1024 X 1024 X 590 voxel images, which is not easy for single processor systems due to the restrictions on the memory space and the processing time.
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Research Products
(12 results)
