Content and semantic context based image retrieval for medical image grid

Authors:
Hai Jin;Aobing Sun;Ran Zheng;Ruhan He;Qin Zhang;Yingjie Shi;Wen Yang
Affiliations:
Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007;Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 43007
Venue:
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Year:
2007

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Abstract

Physicians create Medical Image Libraries (MILs) to collect typical case images, and utilize CBIR (Content-based Image Retrieval) tools to search feature-similar samples within them to aid clinical intervention and diagnoses. This paper presents a CSBIR (Content and Semantic Context based Image Retrieval) scheme for MedImGrid (Medical Image Grid) to tackle the sharing difficulties of special and heterogeneous MILs within wide areas. The scheme encapsulates distributed CBIR engines, MILs and their metadata as WS (Web Services), and links them in the grid environment. It combines CBIR and semantic context of images to automatically choose the optimal WS set to serve users. Our integrated-features based CSBIR engine for thorax CR (Computer Radiology Image) is related as one instance, which can merge the superiorities of randomly selected CBIR services. MedImGrid CSBIR prototype is based on CGSP (China Grid Supporting Platform) and its loosely coupled structure makes the integration of decentralized CBIR systems within or acros hospitals more efficiently.