Extraction of GGO Candidate Regions on Thoracic CT Images using SuperVoxel-Based Graph Cuts for Healthcare Systems
In this paper, we propose a method to reduce artifacts on temporal difference images by improving the conventional method using a non-rigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local...
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| Veröffentlicht in: | Mobile networks and applications 2018-12, Vol.23 (6), p.1669-1679 |
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| container_title | Mobile networks and applications |
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| creator | Lu, Huimin Kondo, Masashi Li, Yujie Tan, JooKooi Kim, Hyoungseop Murakami, Seiichi Aoki, Takotoshi Kido, Shoji |
| description | In this paper, we propose a method to reduce artifacts on temporal difference images by improving the conventional method using a non-rigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. In this method, global matching, local matching, and 3D elastic matching are performed on the current image and past image, and an initial temporal difference image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and perform segmentation using SuperVoxel and Graph Cuts in which a superpixel is extended to three dimensions for each region of interest. For each extracted region, a support vector machine (SVM) is used to reduce the over-segmentation. Finally, in the method that greatly reduces artifacts other than the remaining GGO candidate regions, Voxel Matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact. The resulting ratio of artifacts to lung volume is 0.101 with an FWHM of 28.3, which is an improvement over the conventional method and shows the proposed method’s effectiveness. |
| doi_str_mv | 10.1007/s11036-018-1111-2 |
| format | Article |
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In this method, global matching, local matching, and 3D elastic matching are performed on the current image and past image, and an initial temporal difference image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and perform segmentation using SuperVoxel and Graph Cuts in which a superpixel is extended to three dimensions for each region of interest. For each extracted region, a support vector machine (SVM) is used to reduce the over-segmentation. Finally, in the method that greatly reduces artifacts other than the remaining GGO candidate regions, Voxel Matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact. The resulting ratio of artifacts to lung volume is 0.101 with an FWHM of 28.3, which is an improvement over the conventional method and shows the proposed method’s effectiveness.</description><identifier>ISSN: 1383-469X</identifier><identifier>EISSN: 1572-8153</identifier><identifier>DOI: 10.1007/s11036-018-1111-2</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Communications Engineering ; Computed tomography ; Computer Communication Networks ; Concentration gradient ; Electrical Engineering ; Engineering ; Image segmentation ; IT in Business ; Matching ; Medical imaging ; Networks ; Support vector machines</subject><ispartof>Mobile networks and applications, 2018-12, Vol.23 (6), p.1669-1679</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Mobile Networks and Applications is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-21af180baa509ad087e499fca1afb28e068452a068ddbc68e3fd5a796460baef3</citedby><cites>FETCH-LOGICAL-c316t-21af180baa509ad087e499fca1afb28e068452a068ddbc68e3fd5a796460baef3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11036-018-1111-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11036-018-1111-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Lu, Huimin</creatorcontrib><creatorcontrib>Kondo, Masashi</creatorcontrib><creatorcontrib>Li, Yujie</creatorcontrib><creatorcontrib>Tan, JooKooi</creatorcontrib><creatorcontrib>Kim, Hyoungseop</creatorcontrib><creatorcontrib>Murakami, Seiichi</creatorcontrib><creatorcontrib>Aoki, Takotoshi</creatorcontrib><creatorcontrib>Kido, Shoji</creatorcontrib><title>Extraction of GGO Candidate Regions on Thoracic CT Images using SuperVoxel-Based Graph Cuts for Healthcare Systems</title><title>Mobile networks and applications</title><addtitle>Mobile Netw Appl</addtitle><description>In this paper, we propose a method to reduce artifacts on temporal difference images by improving the conventional method using a non-rigid registration method for ground glass opacification (GGO), which is light in concentration and difficult to detect early. 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In this method, global matching, local matching, and 3D elastic matching are performed on the current image and past image, and an initial temporal difference image is generated. After that, we use an Iris filter, which is the gradient vector concentration degree filter, to determine the initial GGO candidate regions and perform segmentation using SuperVoxel and Graph Cuts in which a superpixel is extended to three dimensions for each region of interest. For each extracted region, a support vector machine (SVM) is used to reduce the over-segmentation. Finally, in the method that greatly reduces artifacts other than the remaining GGO candidate regions, Voxel Matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact. The resulting ratio of artifacts to lung volume is 0.101 with an FWHM of 28.3, which is an improvement over the conventional method and shows the proposed method’s effectiveness.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11036-018-1111-2</doi><tpages>11</tpages></addata></record> |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Communications Engineering Computed tomography Computer Communication Networks Concentration gradient Electrical Engineering Engineering Image segmentation IT in Business Matching Medical imaging Networks Support vector machines |
| title | Extraction of GGO Candidate Regions on Thoracic CT Images using SuperVoxel-Based Graph Cuts for Healthcare Systems |
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