Image analysis of endosocopic ultrasonography in submucosal tumor using fuzzy inference.
Endoscopists usually make a diagnosis in the submucosal tumor depending on the subjective evaluation about general images obtained by endoscopic ultrasonography. In this paper, we propose a method to extract areas of gastrointestinal stromal tumor (GIST) and lipoma automatically from the ultrasonic...
| Publicado en: | BioMed Research International Vol. 2013; pp. 329046 - 329047 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104094529&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104094529 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104094529 2012258986 NLM24024188 PMC3760272 104094529 ppf: 329046 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Image analysis of endosocopic ultrasonography in submucosal tumor using fuzzy inference. aug: au: Kim, Kwang Baek Kim, Gwang Ha affil: Department of Computer Engineering, Silla University, Busan 617-736, Republic of Korea. sug: subj: Endosonography Gastrointestinal Neoplasms Ultrasonography Lipoma Ultrasonography Algorithms Gastrointestinal Neoplasms Pathology Image Processing, Computer Assisted Lipoma Pathology ROC Curve ab: Endoscopists usually make a diagnosis in the submucosal tumor depending on the subjective evaluation about general images obtained by endoscopic ultrasonography. In this paper, we propose a method to extract areas of gastrointestinal stromal tumor (GIST) and lipoma automatically from the ultrasonic image to assist those specialists. We also propose an algorithm to differentiate GIST from non-GIST by fuzzy inference from such images after applying ROC curve with mean and standard deviation of brightness information. In experiments using real images that medical specialists use, we verify that our method is sufficiently helpful for such specialists for efficient classification of submucosal tumors. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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