Automatic Medical Image Annotation and Retrieval Using SECC

Authors:
Jian Yao;Sameer Antani;Rodney Long;George Thoma;Zhongfei Zhang
Affiliations:
State University of New York at Binghamton, USA;National Institutes of Health, USA;National Institutes of Health, USA;National Institutes of Health, USA;State University of New York at Binghamton, USA
Venue:
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Year:
2006

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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.