DocumentCode
2155776
Title
Automatic Medical Image Annotation and Retrieval Using SECC
Author
Yao, Jian ; Antani, Sameer ; Long, Rodney ; Thoma, George ; Zhang, Zhongfei
Author_Institution
Dept. of Comput. Sci., State Univ. of New York, Binghamton, NY
fYear
0
fDate
0-0 0
Firstpage
820
Lastpage
825
Abstract
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
Keywords
error correction codes; image retrieval; medical image processing; automatic medical image annotation; medical image retrieval; semantic error-correcting output codes; semantic image retrieval method; Biomedical imaging; Computer science; Elbow; Foot; Image retrieval; Libraries; Medical diagnosis; Medical diagnostic imaging; Medical treatment; Surgery;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer-Based Medical Systems, 2006. CBMS 2006. 19th IEEE International Symposium on
Conference_Location
Salt Lake City, UT
ISSN
1063-7125
Print_ISBN
0-7695-2517-1
Type
conf
DOI
10.1109/CBMS.2006.57
Filename
1647672
Link To Document