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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

臓器存在尤度アトラスとグラフカットを用いた腹部3次元CT像からの臓器領域抽出

http://hdl.handle.net/2237/23695
7a253ecb-327e-44a6-87f0-ed69df2ee27e
名前 / ファイル ライセンス アクション
110008675433.pdf 110008675433.pdf (1.0 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2016-03-02
タイトル
タイトル 臓器存在尤度アトラスとグラフカットを用いた腹部3次元CT像からの臓器領域抽出
その他のタイトル
その他のタイトル Organ segmentation from 3D abdominal CT images using likelihood atlas of organ existence and graph cut
著者 中岡, 輝久

× 中岡, 輝久

WEKO 62994

中岡, 輝久

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小田, 昌宏

× 小田, 昌宏

WEKO 62995

小田, 昌宏

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北坂, 孝幸

× 北坂, 孝幸

WEKO 62996

北坂, 孝幸

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古川, 和宏

× 古川, 和宏

WEKO 62997

古川, 和宏

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三澤, 一成

× 三澤, 一成

WEKO 62998

三澤, 一成

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藤原, 道隆

× 藤原, 道隆

WEKO 62999

藤原, 道隆

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森, 健策

× 森, 健策

WEKO 63000

森, 健策

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NAKAOKA, Teruhisa

× NAKAOKA, Teruhisa

WEKO 63001

NAKAOKA, Teruhisa

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ODA, Masahiro

× ODA, Masahiro

WEKO 63002

ODA, Masahiro

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KITASAKA, Takayuki

× KITASAKA, Takayuki

WEKO 63003

KITASAKA, Takayuki

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FURUKAWA, Kazuhiro

× FURUKAWA, Kazuhiro

WEKO 63004

FURUKAWA, Kazuhiro

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MISAWA, Kazunari

× MISAWA, Kazunari

WEKO 63005

MISAWA, Kazunari

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FUJIWARA, Michitaka

× FUJIWARA, Michitaka

WEKO 63006

FUJIWARA, Michitaka

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MORI, Kensaku

× MORI, Kensaku

WEKO 63007

MORI, Kensaku

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権利
権利情報 (c)一般社団法人電子情報通信学会 本文データは学協会の許諾に基づきCiNiiから複製したものである
キーワード
主題Scheme Other
主題 セグメンテーション
キーワード
主題Scheme Other
主題 腹部CT像
キーワード
主題Scheme Other
主題 グラフカット
キーワード
主題Scheme Other
主題 複数アトラス
キーワード
主題Scheme Other
主題 segmentation
キーワード
主題Scheme Other
主題 abdominal CT image
キーワード
主題Scheme Other
主題 graph cut
キーワード
主題Scheme Other
主題 multi−atlas
抄録
内容記述 本稿では腹部3次元CT像からの臓器領域抽出手法を提案する.本手法ではCT画像間で臓器存在尤度アトラスを用いて抽出を行う.この際,単一の臓器存在尤度アトラスを用いるのではなく,複数の臓器存在尤度アトラスを選択的に用いる.具体的には,まず,画像間類似度に基づき画像のクラスタリングを行う.各クラスタの平均画像を作成し,平均画像上での臓器存在尤度アトラスの構築を行う.入力画像が与えられた際には平均画像との画像間類似度が最大となる臓器存在尤度アトラスを用いて抽出を行う.選択された臓器存在尤度アトラスと入力画像からMAP推定により粗抽出を行い,最後に,粗抽出結果を基にグラフカットを用いて精密抽出を行う.腹部CT像100例に対して実験を行い,平均一致度は肝臓88.6%,肺臓73.9%,膵臓42.0%,腎臓79.8%であった. In this paper, we propose a multi organ segmentation method from 3D abdominal CT images. In our method, we extract organs using multiple likelihood atlases of the organ existence, instead of single atlas. In our method, first we apply a clustering method to training image datasets based on image similarity. We generate average images and atlases for each cluster. When an input image is given, we select an atlas that has the maximum image similarity between the average image and the input image. We use the selected atlas to extract organs. Then, we extract multi organs roughly by the MAP estimation from the selected atlas and the input image. Finally, we perform precise segmentation by using a multi label graph cut. We apply this method to 100 cases of abdominal CT images. Jaccard indices were 88.6% for liver, 73.9% for spleen, 42.0% for pancreas, and 79.8% for kidney, respectively.
内容記述タイプ Abstract
出版者
出版者 一般社団法人電子情報通信学会
言語
言語 jpn
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
ISSN
収録物識別子タイプ ISSN
収録物識別子 0913-5685
書誌情報 電子情報通信学会技術研究報告. MI, 医用画像

巻 110, 号 364, p. 223-228, 発行日 2011-01
著者版フラグ
値 publisher
シリーズ
関連名称
関連名称 IEICE Technical Report;MI2010-123
URI
識別子 http://ci.nii.ac.jp/naid/110008675433/
識別子タイプ URI
URI
識別子 http://hdl.handle.net/2237/23695
識別子タイプ HDL
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