2013 Fiscal Year Final Research Report
Development of a universal organ segmentation method based on similar image retrieval and machine-learning by using a large database of medical images
| Project/Area Number |
23500118
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Gifu University |
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Principal Investigator |
ZHOU Xiangrong 岐阜大学, 医学(系)研究科(研究院), 助教 (00359738)
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| Project Period (FY) |
2011 – 2013
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| Keywords | データベース / 情報システム / 学習と知識獲得 / 画像情報処理 / 医用・生体画像 |
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| Research Abstract |
The aim of this research (one procedure solves different organ segmentation problems on medical images) was almost realized. Our research policy for procedure design by using (machine learning and data driven based approach instead of transferring the human experience to computer program directly) was proved to be efficient to solve organ segmentation problems on medical images. We reached a conclusion that (image processing procedures such as organ segmentation can be greatly simplified under the supporting of a large database and well preparations). The algorithm that proposed to (accomplish the automatic organ segmentation within several tens of seconds) was implemented. We evaluated the performance of the algorithm and confirmed that the major organ such as kidney, spleen, and so on can be segmented automatically within one minute from the torso CT images.
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