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モルフォロジー処理を利用した頭部MR画像における小脳および脳幹部の自動抽出法
http://hdl.handle.net/2297/24686
http://hdl.handle.net/2297/2468613d2b09b-2008-48b6-8ff5-8dce00dd5052
名前 / ファイル | ライセンス | アクション |
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HO-PR-HAYASHI-N-109.pdf (683.0 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2017-10-05 | |||||
タイトル | ||||||
タイトル | モルフォロジー処理を利用した頭部MR画像における小脳および脳幹部の自動抽出法 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Automated Segmentation Method of the Cerebellum and Brainstem on MRI Images Using Mathematical Morphology | |||||
言語 | ||||||
言語 | jpn | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
林, 則夫
× 林, 則夫× 真田, 茂× 鈴木, 正行× 松浦, 幸広 |
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提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学附属病院放射線部 | |||||
書誌情報 |
医用画像情報学会雑誌 = Japan Society of Imaging and Information Sciences in Medicine 巻 21, 号 1, p. 109-115, 発行日 2004-01-01 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0910-1543 | |||||
NCID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AN10156808 | |||||
出版者 | ||||||
出版者 | 医用画象情報学会 | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | MR imaging is an important method for the diagnosis of diseases caused by various cerebral pathologies. Assessment of the volume reduction such as cerebral atrophy, SDAT (Senile Dementia of Alzheimer Type) and OPCA (Olivopontocerebellar atrophy) is very important in clinical practice. However, the assessment of the atrophy used to be performed by manual measurement or visual evaluation. Therefore, in order to diagnose by quantitative assessment, it is desirable to measure the regional volume automatically. In this study, we investigated an automated segmentation method of cerebellum and brainstem on MR images using morphological information. An automated method was consisted of the following three steps: (1) segmentation of the brain on MR images (2) segmentation of the cerebellum and brainstem on the brain images using mathematical morphology (3) correction of errors on the segmented images using 3-D information. The results indicated that the regions obtained by automated segmentation method were visually similar to those by manual method. An average of the rate of correctly recognized regions is over 70%. However, an average of the rate of unrecognized regions is over 10%. If segmentation accuracy is improved moreover, our method may provide the quantitative diagnostic information. | |||||
権利 | ||||||
権利情報 | Copyright (c) 2005 医用画像情報学会 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
関連URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.mii-sci.jp/ | |||||
関連URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.jstage.jst.go.jp/article/mii/21/1/21_109/_article/-char/ja/ | |||||
関連URI | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://ci.nii.ac.jp/naid/10012016783 |