Histological image retrieval based on semantic content analysis.
The demand for automatically recognizing and retrieving medical images for screening, reference, and management is growing faster than ever. In this paper, we present an intelligent content-based image retrieval system called I-Browse, which integrates both iconic and semantic content for histological image analysis. The I-Browse system combines low-level image processing technology with high-level semantic analysis of medical image content through different processing modules in the proposed system architecture. Similarity measures are proposed and their performance is evaluated. Furthermore, as a byproduct of semantic analysis, I-Browse allows textual annotations to be generated for unknown images. As an image browser, apart from retrieving images by image example, it also supports query by natural language.
Tang
H Lilian
HL
Department of Computing, University of Surrey, Guildford, Surrey GU2 7XH, U.K. h.tang@surrey.ac.uk
Hanka
Rudolf
R
Ip
Horace H S
HH
D003952:Diagnostic Imaging; D016247:Information Storage and Retrieval