| Project/Area Number |
11680389
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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 |
Intelligent informatics
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| Research Institution | Osaka University |
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Principal Investigator |
SATO Yoshinobu Osaka University Graduate School of Medicine, Associate Professor, 医学系研究科, 助教授 (70243219)
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| Co-Investigator(Kenkyū-buntansha) |
TAMURA Shinichi Osaka University Graduate School of Medicine, Professor, 医学系研究科, 教授 (30029540)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2001: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1999: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | quantification / image measurement / blood vessels / pulmonary nodules / articular cartilage / accuracy validation / Thickness / diameter / 局部濃淡構造 / 方向空間 / ヘッセ行列 / 固有値解析 / Gaussフィルタ / Gaborフィルタ / ボリュームビジュアライゼーション / セグメンテーション |
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| Research Abstract |
This project investigated a basic framework and clinical applications for the enhancement, segmentation, and quantification of line-like shapes such as blood vessels and sheet-like shapes such as articular cartilage in medical volume data. Based on multiscale Gaussian filters and the Hessian matrix of the volume function, line and sheet structures with various widths are enhanced. Local intensity structures are represented as continuous functions by further combining the gradient vectors. Medial axis/surface elements are locally determined based on the second-order approximations of local intensity structures. Diameter/thickness quantification is performed based on detected medial axis/surface elements, and various widths of structures are combined through multiscale integration. The utility of the framework was demonstrated by applications to the characterization of pulmonary nodules based on the spatial distribution of vessels surrounding pulmonary nodules, and articular cartilage thickness determination using real computed tomography (CT) and magnetic resonance (MR) volume data.
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