| Project/Area Number |
12558020
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Statistical science
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| Research Institution | University of Tsukuba |
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Principal Investigator |
KUDO Hiroyuki University of Tsukuba, Information Sciences and Electronics, Associate Professor, 電子・情報工学系, 助教授 (60221933)
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| Co-Investigator(Kenkyū-buntansha) |
MURAYAMA Hideo National Institute of Radiological Sciences, Division of Medical Physics, Head of Diagnostic System Group, 医学物理部, 診断システム開発室長 (50166310)
KUNO Takahito University of Tsukuba, Information Sciences and Electronics, Associate Professor, 電子・情報工学系, 助教授 (00205113)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2002: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2001: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Medical Imaging / Image Processing / Image Reconstruction / PET / MRI / Statistical Estimation / Image Segmentation / Optical Flow / アンジオグラフィー / SPECT / マイクロCT / EMアルゴリズム |
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| Research Abstract |
The aim of this research project is to develop a statistical medical image and data processing system based on our previous research results on statistical image processing. To achieve this aim, we performed the research according to the following three steps. First, we improved the PET and SPECT reconstruction algorithm and the MRI image segmentation algorithm which we developed previously in terms of computational costs. In general, statistical image and data processing algorithms require a lot of computational costs because they are based on the minimization of pre-defined cost functions. However, the improved algorithms worked with reasonable computational costs comparable to those of the commonly used algorithms. To achieve this aim, we introduced the concept of block iterations and some optimization methods in the field of mathematical programming. Second, we developed image processing algorithms based on statistical estimation for new problems appearing in medical imaging. For example, these include 3-D blood-vessel reconstruction from a few angiographc x-ray projection data, segmented attenuation map reconstruction for PET and SPECT, and optical flow computation from medical image sequences. To achieve this aim we used new concepts such as optimization methods in the field of mathematical programming, topology-constrained image labeling, and wavelet transform. Finally, all the developed softwares are unified in a workstation such that they work with the same data format and the same usage, and some user-interfaces such as image display are combined too. We evaluated the performances of this image and data processing system with several existing data.
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