34,676 research outputs found
An automated system for lung nodule detection in low-dose computed tomography
A computer-aided detection (CAD) system for the identification of pulmonary
nodules in low-dose multi-detector helical Computed Tomography (CT) images was
developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in
order to enable automated image analysis through a data and cpu GRID
infrastructure. The basic modules of our lung-CAD system, a dot-enhancement
filter for nodule candidate selection and a neural classifier for
false-positive finding reduction, are described. The system was designed and
tested for both internal and sub-pleural nodules. The results obtained on the
collected database of low-dose thin-slice CT scans are shown in terms of free
response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging
Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514,
65143
A Computer-Aided Detection system for lung nodules in CT images
Lung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer-Aided Detection (CAD) system, composed
by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nodules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection), an Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services
Grid Databases for Shared Image Analysis in the MammoGrid Project
The MammoGrid project aims to prove that Grid infrastructures can be used for
collaborative clinical analysis of database-resident but geographically
distributed medical images. This requires: a) the provision of a
clinician-facing front-end workstation and b) the ability to service real-world
clinician queries across a distributed and federated database. The MammoGrid
project will prove the viability of the Grid by harnessing its power to enable
radiologists from geographically dispersed hospitals to share standardized
mammograms, to compare diagnoses (with and without computer aided detection of
tumours) and to perform sophisticated epidemiological studies across national
boundaries. This paper outlines the approach taken in MammoGrid to seamlessly
connect radiologist workstations across a Grid using an "information
infrastructure" and a DICOM-compliant object model residing in multiple
distributed data stores in Italy and the UKComment: 10 pages, 5 figure
A perspective on the Healthgrid initiative
This paper presents a perspective on the Healthgrid initiative which involves
European projects deploying pioneering applications of grid technology in the
health sector. In the last couple of years, several grid projects have been
funded on health related issues at national and European levels. A crucial
issue is to maximize their cross fertilization in the context of an environment
where data of medical interest can be stored and made easily available to the
different actors in healthcare, physicians, healthcare centres and
administrations, and of course the citizens. The Healthgrid initiative,
represented by the Healthgrid association (http://www.healthgrid.org), was
initiated to bring the necessary long term continuity, to reinforce and promote
awareness of the possibilities and advantages linked to the deployment of GRID
technologies in health. Technologies to address the specific requirements for
medical applications are under development. Results from the DataGrid and other
projects are given as examples of early applications.Comment: 6 pages, 1 figure. Accepted by the Second International Workshop on
Biomedical Computations on the Grid, at the 4th IEEE/ACM International
Symposium on Cluster Computing and the Grid (CCGrid 2004). Chicago USA, April
200
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