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- AFIFI Ahmed
- Graduate School of Advanced Integration Science, Chiba University
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- NAKAGUCHI Toshiya
- Graduate School of Advanced Integration Science, Chiba University
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- TSUMURA Norimichi
- Graduate School of Advanced Integration Science, Chiba University
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- MIYAKE Yoichi
- Graduate School of Advanced Integration Science, Chiba University Research Center for Frontier Medical Engineering, Chiba University
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抄録
The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E93-D (4), 882-890, 2010
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390001204378390656
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- NII論文ID
- 10026815062
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- NII書誌ID
- AA10826272
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可