論文

査読有り
2018年8月20日

Automatic fascia extraction and classification for measurement of muscle layer thickness

2018 15th International Conference on Ubiquitous Robots, UR 2018
  • Tsubasa Imaizumi
  • ,
  • Norihiro Koizumi
  • ,
  • Ryosuke Kondo
  • ,
  • Yu Nishiyama
  • ,
  • Naoki Matsumoto

開始ページ
493
終了ページ
496
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/URAI.2018.8441877
出版者・発行元
IEEE

In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.

リンク情報
DOI
https://doi.org/10.1109/URAI.2018.8441877
DBLP
https://dblp.uni-trier.de/rec/conf/urai/ImaizumiKKNM18
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000447274600072&DestApp=WOS_CPL
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053490503&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85053490503&origin=inward
URL
https://dblp.uni-trier.de/rec/conf/urai/2018
URL
https://dblp.uni-trier.de/db/conf/urai/urai2018.html#ImaizumiKKNM18
ID情報
  • DOI : 10.1109/URAI.2018.8441877
  • ISSN : 2325-033X
  • ISBN : 9781538663349
  • DBLP ID : conf/urai/ImaizumiKKNM18
  • SCOPUS ID : 85053490503
  • Web of Science ID : WOS:000447274600072

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