Bellotti, R., Cerello, P., Tangaro, S., Bevilacqua, V., Castellano, M., Mastronardi, G., De Carlo, F., Bagnasco, S., Bottigli, U., Cataldo, R., and others. (2007) Distributed medical images analysis on a Grid infrastructure. Future Generation Computer Systems, 23 (3). pp. 475-484. ISSN 0167-739X. (doi:10.1016/j.future.2006.07.006) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91928)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: https://doi.org/10.1016/j.future.2006.07.006 |
Abstract
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.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1016/j.future.2006.07.006 |
| Uncontrolled keywords: | Applied computing; Life and medical sciences; Computing methodologies; Artificial intelligence; Computer vision; Computer vision problems; Computer vision tasks; Scene understanding; Information systems; Information retrieval; Information storage systems |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Amy Boaler |
| Date Deposited: | 02 Dec 2021 14:00 UTC |
| Last Modified: | 05 Nov 2024 12:57 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/91928 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Masala, G.L..
| Creator's ORCID: |
 https://orcid.org/0000-0001-6734-9424
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