38 research outputs found
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
A computer-aided detection (CADe) system for microcalcification cluster
identification in mammograms has been developed in the framework of the
EU-founded MammoGrid project. The CADe software is mainly based on wavelet
transforms and artificial neural networks. It is able to identify
microcalcifications in different kinds of mammograms (i.e. acquired with
different machines and settings, digitized with different pitch and bit depth
or direct digital ones). The CADe can be remotely run from GRID-connected
acquisition and annotation stations, supporting clinicians from geographically
distant locations in the interpretation of mammographic data. We report the
FROC analyses of the CADe system performances on three different dataset of
mammograms, i.e. images of the CALMA INFN-founded database collected in the
Italian National screening program, the MIAS database and the so-far collected
MammoGrid images. The sensitivity values of 88% at a rate of 2.15 false
positive findings per image (FP/im), 88% with 2.18 FP/im and 87% with 5.7 FP/im
have been obtained on the CALMA, MIAS and MammoGrid database respectively.Comment: 6 pages, 5 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms
Mammography is widely recognized as the most reliable technique for early
detection of breast cancers. Automated or semi-automated computerized
classification schemes can be very useful in assisting radiologists with a
second opinion about the visual diagnosis of breast lesions, thus leading to a
reduction in the number of unnecessary biopsies. We present a computer-aided
diagnosis (CADi) system for the characterization of massive lesions in
mammograms, whose aim is to distinguish malignant from benign masses. The CADi
system we realized is based on a three-stage algorithm: a) a segmentation
technique extracts the contours of the massive lesion from the image; b)
sixteen features based on size and shape of the lesion are computed; c) a
neural classifier merges the features into an estimated likelihood of
malignancy. A dataset of 226 massive lesions (109 malignant and 117 benign) has
been used in this study. The system performances have been evaluated terms of
the receiver-operating characteristic (ROC) analysis, obtaining A_z =
0.80+-0.04 as the estimated area under the ROC curve.Comment: 6 pages, 3 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
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
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
Fully automated hippocampus segmentation with virtual ant colonies
The development of tools for a fully automatic segmentation of the relevant brain structures, such as the hippocampus, is potentially very useful for pathologies detection. In this paper, a method for the automated hippocampal segmentation, based on virtual ant colonies, is proposed. The algorithm used, the Channeler Ant Model (CAM), represents an effective way to segment 3D objects with a complex shape in a noisy background. The CAM was modified by inserting a shape knowledge that is crucial to face the hippocampus segmentation. The algorithm was trained and tested using a database of 56 T1 weighted MRI images with a known manual segmentation of the hippocampus volume. The results are comparable to other methods: an average Dice Index of 0.74 and 0.72 is obtained over the left and right hippocampi, respectively. The lack of a heavy training procedure, because all the model parameters are fixed, and the speed make this approach very effective
A Scalable System for Microcalcification Cluster Automated Detection in a Distributed Mammographic Database
Abstract: A computer-aided detection (CADe) system for microcalci cation cluster identi cation in mammograms has been developed in the framework of the EU-founded MammoGrid project. The CADe software is mainly based on wavelet transforms and arti cial neural networks. It is able to identify microcalci cations in different datasets of mammograms (i.e. acquired with different machines and settings, digitized with different pitch and bit depth or direct digital ones). The CADe can be remotely run from GRID-connected acquisition and annotation stations, supporting clinicians from geographically distant locations in the interpretation of marnmographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis
Digital mammographic application of a single photon counting pixel detector
We present the imaging capabilities of a system based on a single photon counting chip, bump-bounded to a Si pixel detector. The detector is 300 mum thick a matrix of 64 x 64 square pixels with a dimension side of 170 mum. The active area is about 1.2 cm(2). The photon counting chip matches the geometry of the detector so it has 4096 asynchronous read-out cells, each containing a charge preamplifier, a leading edge comparator and a pseudo-random counter. We have tested the image quality and the stability of our acquisition system imaging some phantoms used for mammografic quality checks. Our system allows to see low contrast details with a patient dose comparable with that given in mammographic examinations