| Project/Area Number |
24300076
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
CHEN YAN WEI 立命館大学, 情報理工学部, 教授 (60236841)
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| Co-Investigator(Kenkyū-buntansha) |
TANAKA T, Hiromi 立命館大学, 情報理工学部, 教授 (10268154)
HAN Xian-Hua 立命館大学, 立命館グローバルイノベーション研究機構, 准教授 (60469195)
SATO Yoshinobu 奈良先端科学技術大学院大学, 情報科学研究科, 教授 (70243219)
FURUKAWA Akira 首都大学東京, 人間健康科学研究科, 教授 (80199421)
MORIKAWA Shigehiro 滋賀医科大学, 医学部, 教授 (60220042)
TATEYAMA Tomoko 立命館大学, 情報理工学部, 助手 (90550153)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥16,250,000 (Direct Cost: ¥12,500,000、Indirect Cost: ¥3,750,000)
Fiscal Year 2014: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2013: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2012: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
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| Keywords | 多重線形 / 腹部複数臓器 / スパース / Low-rank / 局所解析 / ボリューム / 医用画像 / テンプレートマッチング / 多重線形代数 / スパースコーディング / テンソル / 腹部CT / 複数臓器 / 統計モデリング / 主成分分析 / 医用画像データベース |
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| Outline of Final Research Achievements |
Recently, sparse coding is a hot topic for efficient data representation, and has been widely used in computer vision field. In this project, we proposed a generalized ND sparse coding based on multi-linear algebra, for direct analysis of multi-dimensional data without unfolding process. Experiments results on noise reduction demonstrated that the proposed method can achieve better results compared with the conventional sparse coding. We also proposed a framework for local morphological analysis (local statistical shape models) of 3D organs based on sparse and low rank matrix decomposition and applied our proposed method to computer-aided diagnostics of liver cirrhosis. The local abnormal regions can be detected by estimating the sparse components. The norm of the sparse components can be used as a measure for classification of the normal and abnormal livers. The classification accuracy by our proposed method is improved to 95%.
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