Bellotti, Roberto and Cerello, Piergiorgio and Tangaro, Sonia and Bevilacqua, Vitantonio and Castellano, Marcello and Mastronardi, Giuseppe and De Carlo, Francesco and Bagnasco, Stefano and Bottigli, Ubaldo and Cataldo, Rossella and Catanzariti, Ezio and Cheran, Sorin Cristian and Delogu, Pasquale and De Mitri, Ivan and De Nunzio, Giorgio and Fantacci, Maria Evelina and Fauci, Francesco and Gargano, Gianfranco and Golosio, Bruno and Indovina, Pier Luigi and Lauria, Adele and Lopez Torres, Ernesto and Magro, Rosario and Masala, Giovanni Luca Christian and Massafra, Raffaella and Oliva, Piernicola and Preite Martinez, Alessandro and Quarta, Maurizio and Raso, Giuseppe and Retico, Alessandra and Sitta, M. and Stumbo, Simone and Tata, Alessandro and Squarcia, Sandro and Schenone, Andrea and Molinari, Elisa and Canesi, Barbara (2007) Distributed medical images analysis on a Grid infrastructure. Future Generation Computer Systems, Vol. 23 (3), p. 475-484. ISSN 0167-739X. Article.
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In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists’ performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated with the diagnosis in the screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed- Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) images for the early diagnosis of Alzheimer Disease (AD). A Virtual Organization (VO) has been deployed, so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans, statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the AD. A small-scale prototype of the required Grid functionality was already implemented for the analysis of digitized mammograms.
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