Automatic medical image annotation and keyword-based image retrieval using relevance feedback.
This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification met...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 4; pp. 454 - 466 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2012
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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=104470702&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104470702 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2012 vid: 25 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104470702 77493907 10.1007/s10278-011-9443-5 NLM22193754 PMC3389081 104470702 ppf: 454 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Automatic medical image annotation and keyword-based image retrieval using relevance feedback. aug: au: Ko, Byoung Lee, JiHyeon Nam, Jae-Yeal affil: Shindang-Dong Dalseo-Gu, Dept. Of Computer Engineering, Keimyung University, Daegu 704-701 South Korea sug: subj: Image Retrieval Systems Abstracting and Indexing Methods Image Retrieval Classification Radiography Evaluation Research Validation Studies Human Feedback ab: This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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