A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval.
Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image database easily. Content-based medical image retrieval (CBMIR) techniques are employed to facilita...
| Published in: | Journal of Digital Imaging Vol. 26; no. 5; pp. 850 - 866 |
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| Main Authors: | , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104229513&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104229513 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2013 vid: 26 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104229513 90397231 10.1007/s10278-013-9591-x NLM23563792 PMC3782604 104229513 ppf: 850 ppct: 16 formats: fmt: @attributes: type: P tig: atl: A Novel Similarity Learning Method via Relative Comparison for Content-Based Medical Image Retrieval. aug: au: Huang, Wei Zhang, Peng Wan, Min affil: School of Information Engineering, Nanchang University, Nanchang China sug: subj: Diagnostic Imaging Information Retrieval Methods Artificial Intelligence Human Cataract Diagnosis Experimental Studies Precision One-Way Analysis of Variance P-Value Confidence Intervals Information Science Methods ab: Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image database easily. Content-based medical image retrieval (CBMIR) techniques are employed to facilitate the above process. It is known that a few concrete factors, including visual attributes extracted from images, measures encoding the similarity between images, user interaction, etc. play important roles in determining the retrieval performance. This paper concentrates on the similarity learning problem of CBMIR. A novel similarity learning paradigm is proposed via relative comparison, and a large database composed of 5,000 images is utilized to evaluate the retrieval performance. Extensive experimental results and comprehensive statistical analysis demonstrate the superiority of adopting the newly introduced learning paradigm, compared with several conventional supervised and semi-supervised similarity learning methods, in the presented CBMIR application. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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