Abstract

Abstract
With the increase of Endoscopic Capsule (EC) exams, the amount of video data to be analyzed by automated image processing algorithms grows exponentially and standard desktop computers start to present limitations to complete that procedure in an acceptable "clinical time". This has motivated us to assess the potential of Grid computing paradigm to process and analyze EC data, using existing Grid infrastructures. We used our Automated Topografic Segmentation (ATS) algorithm as a case study to run experiments on the Ibergrid Grid infrastructure. As a result, we were able to port the existing ATS algorithm used in routine to run on the Grid seamlessly. A friendly portal environment was adopted, allowing for clinical end-users to harness from existing production Grids in a practical way. The processing time could be reduced in some cases but, when considering job processing overheads, the desktop version is preferable for small scale processing. These results show that Grid computing is a promising technology to process massive amounts of EC data, but current infrastructures present a high variability in jobs processing times, especially when compared with dedicated clusters.

Abstract

Abstract

Abstract

Abstract
This paper describes the present state of evolution of the "Brain Imaging Network Grid" (BING) project in Portugal. This network is composed of 5 nodes connected through a Gbps dark fiber bitways and is served by two clusters and (-)60 TBytes of storage. A distributed information system to support operations and manage the BING community is already in place and further developments to evolve the infrastructure to provide computational neurosciences services are under way and described in the present paper. Moreover, we undercover the architecture that we are implementing that includes the intention to add BING resources to the EGEE node IEETA/UA has been exploring since 2005 under the EGEE Biomed VO. Other aspects of the BING infrastructure evolution aimed to constitute a Grid virtual community in brain imaging are presented and discussed.