loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)
Automatic Medical Image Annotation and Retrieval Using SECC
Salt Lake City, Utah
June 22-June 23
ISBN: 0-7695-2517-1
ASCII Text
x 
 
Jian Yao, Sameer Antani, Rodney Long, George Thoma, Zhongfei Zhang, "Automatic Medical Image Annotation and Retrieval Using SECC," Computer-Based Medical Systems, IEEE Symposium on, pp. 820-825, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006.
 
 
BibTex
x
 
@article{ 10.1109/CBMS.2006.57,
author = {Jian Yao and Sameer Antani and Rodney Long and George Thoma and Zhongfei Zhang},
title = {Automatic Medical Image Annotation and Retrieval Using SECC},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2006},
issn = {1063-7125},
pages = {820-825},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.57},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Automatic Medical Image Annotation and Retrieval Using SECC
SN - 1063-7125
SP820
EP825
A1 - Jian Yao,
A1 - Sameer Antani,
A1 - Rodney Long,
A1 - George Thoma,
A1 - Zhongfei Zhang,
PY - 2006
KW - null
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Jian Yao, State University of New York at Binghamton, USA
Sameer Antani, National Institutes of Health, USA
Rodney Long, National Institutes of Health, USA
George Thoma, National Institutes of Health, USA
Zhongfei Zhang, State University of New York at Binghamton, USA
The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image annotation method based on the proposed Semantic Error-Correcting output Codes (SECC). With this annotation method, we present a new semantic image retrieval method, which exploits the high level semantic similarity. For example, a user may query the system using an image of arm while he/she expects images of hand. This cannot be realized by traditional retrieval methods. The experimental results on the IMAGECLEF 2005 annotation data set clearly show the strength and the promise of the presented methods.
Citation:
Jian Yao, Sameer Antani, Rodney Long, George Thoma, Zhongfei Zhang, "Automatic Medical Image Annotation and Retrieval Using SECC," cbms, pp.820-825, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
Usage of this product signifies your acceptance of the Terms of Use.