論文

査読有り
2020年

Supervoxel Graph Cuts: An Effective Method for GGO Candidate Regions Extraction on CT Images.

IEEE Consumer Electronics Magazine
  • Huimin Lu
  • ,
  • Masashi Kondo
  • ,
  • Yujie Li
  • ,
  • Joo Kooi Tan
  • ,
  • Hyoungseop Kim
  • ,
  • Seiichi Murakami
  • ,
  • Takatoshi Aoki
  • ,
  • Shoji Kido

9
1
開始ページ
61
終了ページ
66
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/MCE.2019.2941468

© 2012 IEEE. In this article, a method to reduce artifacts on temporal difference images is introduced. The proposed method uses a nonrigid 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 three-dimensional (3D) elastic matching are performed on the current and previous images, and an initial temporal subtraction 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 use supervoxel and graph cuts to segment region of interest in the 3D images. For each extracted region, a support vector machine is used to reduce the oversegmentation. The voxel matching is applied to generate the final temporal difference image, emphasizing the GGO regions while reducing the artifact.

リンク情報
DOI
https://doi.org/10.1109/MCE.2019.2941468
Scopus
https://www.scopus.com/record/display.uri?eid=2-s2.0-85076374234&origin=inward
Dblp Url
https://dblp.uni-trier.de/db/journals/cem/cem9.html#LuKLTKMAK20
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076374234&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85076374234&origin=inward

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