{"_buckets": {"deposit": "b0913539-53ea-459f-a056-882db095333e"}, "_deposit": {"id": "21563", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21563"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021563"}, "item_10_alternative_title_19": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u8a00\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Study on Bronchus Segmentation based on Machine Learning and Graph Cuts from Chest CT Image"}]}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "411", "bibliographicPageEnd": "196", "bibliographicPageStart": "191", "bibliographicVolumeNumber": "112", "bibliographic_titles": [{"bibliographic_title": "\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u6280\u8853\u7814\u7a76\u5831\u544a. 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A lot of previous methods utilize region growing or level set algorithms based on anatomical knowledge of connectivity of bronchi However, it is difficult to extract bronchus precisely by only using these algorithms. Because connectivity of bronchi is often lost by partial volume effects, heartbeat, image noise or tumor in actual CT images. In this paper, we propose a method of bronchus segmentation based on another anatomical knowledge about bronchus. The proposed method detects voxels of medial lines of bronchi and its radius by using local intensity structure analysis, and extracts bronchi by using graph cuts segmentation that utilizes cost function with radius information. 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