Content-Based
Image Retrieval with Relevance Feedback |
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Relevance feedback (RF)
has been an active research area in Content-based
Image Retrieval (CBIR). RF bridges the gap between
the low-level image features and the high-level
human visual perception by analyzing and employing
the feedback information provided by the user.
This gap becomes more evident and important in
medical image retrieval due to the two distinct
facts with regard to medical images: (1) subtle
differences between images, even between
pathological and non-pathological images; (2)
subjective and different diagnosis even among
experts.
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Project Sponsors:
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National Library of Medicine (2004-2005)
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Collaborators:
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Dr. Sameer Antani, National Library of
Medicine
Mr. L. Rodney Long, National Library of
Medicine
Dr. George Thoma, National Library of
Medicine
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Graduate Students:
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Xiaoqian Xu
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Publications:
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X.
Xu, D.J. Lee, S.K. Antani, L.R. Long,
and J.K. Archibald,? Using Relevance
Feedback with Short-term Memory for
Content-based Spine X-ray Image Retrieval, ?Journal
of Neurocomputing, vol. 72/10-12, p.
2259-2269, June 2009.
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X.
Xu, S.K. Antani, D.J. Lee, L.R. Long,
and G.R. Thoma, ? Relevance Feedback for
Shape-based Pathology in Spine X-ray Image
Retrieval ?, SPIE Medical Imaging,
Picture Archive and Communication Systems
(PACS) and Imaging Informatics, vol.
6145-21, p. 120-129, San Diego, CA, USA,
February 11-16, 2006.
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X.
Xu, D.J. Lee, S.K. Antani, and L.R.
Long, ? Relevance Feedback for Spine X-ray
Retrieval ?, Proceedings of The
18th IEEE Symposium on Computer-Based Medical
Systems, Dublin, Ireland, p. 197-202,
June 23-24, 2005.
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image to view.)
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