Using grid technologies to face medical image analysis challenges
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis
Web-based grid-enabled interaction with 3D medical data
Future Generation Computer Systems
Securing web services for deployment in health grids
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Grid technology for biomedical applications
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
Proceedings of the 2008 ACM symposium on Applied computing
Improving the ranking quality of medical image retrieval using a genetic feature selection method
Decision Support Systems
Grid based sleep research - Analysis of polysomnographies using a grid infrastructure
Future Generation Computer Systems
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Content-based image retrieval (CBIR) has been one the most vivid research areas in the field of computer vision, and substantial progress has been made over the last years. As such, many have argued for the use of CBIR to support medical imaging diagnosis. However, the sheer volume of data produced in radiology centers has precluded the use of CBIR in the daily routine of hospitals and clinics. This paper aims to change this status quo. We here present a solution that applies Computational Grids to significantly speed up the CBIR procedure, while preserving the security of data in the clinical routine. This solution combines texture attributes and registration algorithms that together are capable of retrieving images with greater-than-90% precision, yet running in a few minutes over the Grid, making it usable in the clinical routine.