{"_buckets": {"deposit": "7410782d-da9e-416a-9762-82f9d3f084ee"}, "_deposit": {"id": "21552", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21552"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021552", "sets": ["314"]}, "author_link": ["63062", "63063", "63064", "63065", "63066", "63067", "63068", "63069", "63070", "63071", "63072", "63073"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A Study on Multi Organ Segmentation from 3D CT Images Using Likelihood Atlases of Organ Existence Constructed by using Affinity Propagation Technique", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2011-11", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "331", "bibliographicPageEnd": "66", "bibliographicPageStart": "61", "bibliographicVolumeNumber": "111", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. MI, 医用画像", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "本稿では,複数臓器存在尤度アトラスを用いた腹部3次元CT像からの多臓器抽出手法について提案する.本手法では,全ての学習画像をクラスタリングし,各クラスタごとにアトラスを構築するため,クラスタリング手法は構築するアトラスの有効性に大きく影響する.また,クラスタリングの結果は学習画像の画像間類似度にも大きく依存する.そのため,最適な類似度計算手法とクラスタリング手法の組み合わせを求める必要がある.従来研究では,類似度にはNCC,クラスタリング手法はNormalized Cutを用いていた.本研究では,Normalized CutとAffinity Propagationをクラスタリング手法として採用し,NCCとIMEDの二つの類似度計算方法を用い,クラスタリング手法と類似度計算手法の全ての組み合わせについて,実験データ数が100の場合の抽出結果を比較した.結果として,IMED + Affinity Propagationの組が最も高い性能が得られた. ", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In this paper, we propose a method for organ segmentation based on multiple likelihood atlases from 3D abdominal CT images. In our method, performance of clustering methods affects performance of segmentation methods utilizing the clustering results. Also, similarities between the training data affect the performance of the clustering methods. Hence, we need to find an optimal combination of the similarity criteria and the clustering method. In this research, we utilize two similarity criterion including the Normalized Cross Correlation and the Image Euclidean Distance, combined them with two clustering methods of the Normalized Cut and the Affinity Propagation. For all of the combinations, we evaluated the segmentation performance using 100 cases of CT images. Prom the experimented results, we found that the combination of the IMED and the Affinity Propagation showed the best performance.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "IEICE Technical Report;MI2011-73", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://ci.nii.ac.jp/naid/110009466324/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23700"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人電子情報通信学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_40": {"attribute_name": "シリーズ", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE 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