枝単位リージョングローイングによる頭部MRAからの血管抽出 [in Japanese] A Blood Vessel Segmentation for Head MRA Using Branch-Based Region-Growing [in Japanese]
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- 関口 博之 SEKIGUCHI Hiroyuki
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 杉本 直三 SUGIMOTO Naozo
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 英保 茂 [他] EIHO Shigeru
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 花川 隆 HANAKAWA Takashi
- 京都大学大学院医学研究科付属高次脳機能総合研究センタ Human Brain Research Center, Graduate School of Medicine, Kyoto University
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- 浦山 慎一 URAYAMA Shinichi
- 京都大学大学院医学研究科付属高次脳機能総合研究センタ Human Brain Research Center, Graduate School of Medicine, Kyoto University
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Author(s)
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- 関口 博之 SEKIGUCHI Hiroyuki
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 杉本 直三 SUGIMOTO Naozo
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 英保 茂 [他] EIHO Shigeru
- 京都大学大学院情報学研究科 Graduate School of Informatics, Kyoto University
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- 花川 隆 HANAKAWA Takashi
- 京都大学大学院医学研究科付属高次脳機能総合研究センタ Human Brain Research Center, Graduate School of Medicine, Kyoto University
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- 浦山 慎一 URAYAMA Shinichi
- 京都大学大学院医学研究科付属高次脳機能総合研究センタ Human Brain Research Center, Graduate School of Medicine, Kyoto University
Abstract
MRAにおける血管領域の信号強度は血流量を反映するため,細い血管ほど輝度が低くなる.また細い血管ほどパーシャルボリュームの影響を強く受け,その輝度は更に低下する.このためMRAの血管輝度は広い範囲にわたり,単純なしきい値処理ですべての血管を抽出することは不可能である.血管輝度のレンジの広さはリージョングローイングによる抽出手法においても大きな問題となり,拡張条件を血管の位置や輝度に応じて動的に変更するといった対処法が必要になる.しかし一般的なリージョングローイングでは複数の血管内で拡張が同時に進行するため,各拡張点に異なる拡張条件を適用することは難しい.これを解決するために,血管を各枝単位に抽出する,枝単位リージョングローイングを提案する.枝単位リージョングローイングでは拡張条件の局所的な最適化だけでなく,拡張の途切れ先への再接続も容易に行えるため,抽出精度の更なる向上が期待できる.本手法を頭部MRAデータ5例に適用したところ,すべての例について抽出誤差が従来手法に比べて減少することを確認した.本論文では上記の抽出手法,並びに,客観的評価を行うために今回開発した,投影像を利用した数値評価法について述べる.
Journal
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- The Transactions of the Institute of Electronics,Information and Communication Engineers.
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The Transactions of the Institute of Electronics,Information and Communication Engineers. 87(1), 126-133, 2004-01-01
The Institute of Electronics, Information and Communication Engineers
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