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dc.contributor.authorBottigli, Uen
dc.contributor.authorCerello, Pen
dc.contributor.authorCheran, Sen
dc.contributor.authorDelogu, Pen
dc.contributor.authorFantacci, MEen
dc.contributor.authorFauci, Fen
dc.contributor.authorGolosio, Ben
dc.contributor.authorLauria, Aen
dc.contributor.authorTorres, ELen
dc.contributor.authorMagro, Ren
dc.contributor.authorMasala, GLen
dc.contributor.authorOliva, Pen
dc.contributor.authorPalmiero, Ren
dc.contributor.authorRaso, Gen
dc.contributor.authorRetico, Aen
dc.contributor.authorStumbo, Sen
dc.contributor.authorTangaro, Sen
dc.date.accessioned2016-11-11T10:16:28Z
dc.date.available2016-11-11T10:16:28Z
dc.identifier.urihttp://hdl.handle.net/10026.1/6715
dc.description6 pages, Proceedings of the Seventh Mexican Symposium on Medical Physics 2003, Vol. 682/1, pp. 67-72, Mexico City, Mexicoen
dc.description.abstract

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used for acquire new images, as archive and to perform statistical analysis. The images are completely described: pathological ones have a consistent characterization with radiologist's diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions analysis and microcalcification clusters analysis. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as "second reader" will also be presented.

en
dc.language.isoenen
dc.subjectphysics.med-phen
dc.subjectphysics.med-phen
dc.titleGPCALMA: A Tool For Mammography With A GRID-Connected Distributed Databaseen
dc.typeJournal Article
plymouth.author-urlhttp://arxiv.org/abs/physics/0410084v1en
plymouth.publisher-urlhttp://dx.doi.org/10.1063/1.1615100en
dc.identifier.doi10.1063/1.1615100en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1063/1.1615100en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


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