| Project/Area Number |
15K16087
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
Terumasa Tokunaga 九州工業大学, 大学院情報工学研究院, 准教授 (50614806)
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| Project Period (FY) |
2015-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 統計学 / ベイズ推論 / 機械学習 / 画像処理 / データ科学 / 計算統計 / バイオイメージインフォマティクス / 非剛体イメージアライメント / 非剛体イメージレジストレーション / EMアルゴリズム / マルコフ確率場 / 医療画像処理 / 最適化 / データサイエンス / 非剛体イメージレジアライメント / データ欠損 / 位置合わせ |
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| Outline of Final Research Achievements |
Recent rapid advance of imaging techniques has produced a massive amount of imaging data in many fields, including biology and medical imaging. Image alignment ( or image registration ) is a process of transforming different set of imaging data measured at different environment into a common coordinate system. It is a core tool for analyzing fMRI, CT, MR images and other medical images. However, the existing alignment techniques often fail to aligning tubular objects ( e.g., blood vessels and neurites). In this work, we developed a new non-rigid image alignment technique based on the idea of a local deformation of kernel density functions. The proposed method begins by performing kernel density estimation to convert digital images into a continuous function. Then, the alignment problem is solved by minimizing the distance between two probability density functions based on EM algorithm. In some experiments, our method achieved a robust optimization for aligning tubular objects.
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