500 research outputs found
GPCALMA: a Grid Approach to Mammographic Screening
The next generation of High Energy Physics experiments requires a GRID
approach to a distributed computing system and the associated data management:
the key concept is the "Virtual Organisation" (VO), a group of geographycally
distributed users with a common goal and the will to share their resources. A
similar approach is being applied to a group of Hospitals which joined the
GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography),
which will allow common screening programs for early diagnosis of breast and,
in the future, lung cancer. HEP techniques come into play in writing the
application code, which makes use of neural networks for the image analysis and
shows performances similar to radiologists in the diagnosis. GRID technologies
will allow remote image analysis and interactive online diagnosis, with a
relevant reduction of the delays presently associated to screening programs.Comment: 4 pages, 3 figures; to appear in the Proceedings of Frontier
Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31
May 2003, La Biodola, Isola d'Elba, Ital
A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure
The use of an automatic system for the analysis of mammographic images has
proven to be very useful to radiologists in the investigation of breast cancer,
especially in the framework of mammographic-screening programs. A breast
neoplasia is often marked by the presence of microcalcification clusters and
massive lesions in the mammogram: hence the need for tools able to recognize
such lesions at an early stage. In the framework of the GPCALMA (GRID Platform
for Computer Assisted Library for MAmmography) project, the co-working of
italian physicists and radiologists built a large distributed database of
digitized mammographic images (about 5500 images corresponding to 1650
patients) and developed a CAD (Computer Aided Detection) system, able to make
an automatic search of massive lesions and microcalcification clusters. The CAD
is implemented in the GPCALMA integrated station, which can be used also for
digitization, as archive and to perform statistical analyses. Some GPCALMA
integrated stations have already been implemented and are currently on clinical
trial in some italian hospitals. The emerging GRID technology can been used to
connect the GPCALMA integrated stations operating in different medical centers.
The GRID approach will support an effective tele- and co-working between
radiologists, cancer specialists and epidemiology experts by allowing remote
image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th
IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200
GPCALMA: A Tool For Mammography With A GRID-Connected Distributed Database
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.Comment: 6 pages, Proceedings of the Seventh Mexican Symposium on Medical
Physics 2003, Vol. 682/1, pp. 67-72, Mexico City, Mexic
CADe tools for early detection of breast cancer
A breast neoplasia is often marked by the presence of microcalcifications and
massive lesions in the mammogram: hence the need for tools able to recognize
such lesions at an early stage. Our collaboration, among italian physicists and
radiologists, has built a large distributed database of digitized mammographic
images and has developed a Computer Aided Detection (CADe) system for the
automatic analysis of mammographic images and installed it in some Italian
hospitals by a GRID connection. Regarding microcalcifications, in our CADe
digital mammogram is divided into wide windows which are processed by a
convolution filter; after a self-organizing map analyzes each window and
produces 8 principal components which are used as input of a neural network
(FFNN) able to classify the windows matched to a threshold. Regarding massive
lesions we select all important maximum intensity position and define the ROI
radius. From each ROI found we extract the parameters which are used as input
in a FFNN to distinguish between pathological and non-pathological ROI. We
present here a test of our CADe system, used as a second reader and a
comparison with another (commercial) CADe system.Comment: 4 pages, Proceedings of the 4th International Symposium on Nuclear
and Related Techniques 2003, Vol. unico, pp. d10/1-d10/4 Havana, Cub
GPCALMA, a mammographic CAD in a GRID connection
6 pages, 4 figures, to appear in CARS 2003 Proceedings, Computer Assisted Radiology and Surgery 17th International Congress and Exhibition, London, June 25-28, 2003Purpose of this work is the development of an automatic system which could be useful for radiologists in the investigation of breast cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. GPCALMA (Grid Platform Computer Assisted Library for MAmmography), a collaboration among italian physicists and radiologists, has built a large distributed database of digitized mammographic images (at this moment about 5500 images corresponding to 1650 patients). This collaboration has developed a CAD (Computer Aided Detection) system which, installed in an integrated station, can also be used for digitization, as archive and to perform statistical analysis. With a GRID configuration it would be possible for the clinicians tele- and co-working in new and innovative groupings ('virtual organisations') and, using the whole database, by the GPCALMA tools several analysis can be performed. Furthermore the GPCALMA system allows to be abreast of the CAD technical progressing into several hospital locations always with remote working by GRID connection. We report in this work the results obtained by the GPCALMA CAD software implemented with a GRID connection
GPCALMA: a Grid-based tool for Mammographic Screening
The next generation of High Energy Physics (HEP) experiments requires a GRID
approach to a distributed computing system and the associated data management:
the key concept is the Virtual Organisation (VO), a group of distributed users
with a common goal and the will to share their resources. A similar approach is
being applied to a group of Hospitals which joined the GPCALMA project (Grid
Platform for Computer Assisted Library for MAmmography), which will allow
common screening programs for early diagnosis of breast and, in the future,
lung cancer. HEP techniques come into play in writing the application code,
which makes use of neural networks for the image analysis and proved to be
useful in improving the radiologists' performances in the diagnosis. GRID
technologies allow remote image analysis and interactive online diagnosis, with
a potential for a relevant reduction of the delays presently associated to
screening programs. A prototype of the system, based on AliEn GRID Services, is
already available, with a central Server running common services and several
clients connecting to it. Mammograms can be acquired in any location; the
related information required to select and access them at any time is stored in
a common service called Data Catalogue, which can be queried by any client. The
result of a query can be used as input for analysis algorithms, which are
executed on nodes that are in general remote to the user (but always local to
the input images) thanks to the PROOF facility. The selected approach avoids
data transfers for all the images with a negative diagnosis (about 95% of the
sample) and allows an almost real time diagnosis for the 5% of images with high
cancer probability.Comment: 9 pages, 4 figures; Proceedings of the HealthGrid Workshop 2004,
January 29-30, Clermont-Ferrand, Franc
LHC Optics Measurement with Proton Tracks Detected by the Roman Pots of the TOTEM Experiment
Precise knowledge of the beam optics at the LHC is crucial to fulfil the
physics goals of the TOTEM experiment, where the kinematics of the scattered
protons is reconstructed with the near-beam telescopes -- so-called Roman Pots
(RP). Before being detected, the protons' trajectories are influenced by the
magnetic fields of the accelerator lattice. Thus precise understanding of the
proton transport is of key importance for the experiment. A novel method of
optics evaluation is proposed which exploits kinematical distributions of
elastically scattered protons observed in the RPs. Theoretical predictions, as
well as Monte Carlo studies, show that the residual uncertainty of this optics
estimation method is smaller than 0.25 percent.Comment: 20 pages, 11 figures, 5 figures, to be submitted to New J. Phy
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