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Search Results: 1 - 10 of 507797 matches for " M. E. Fantacci "
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An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms
A. Retico,P. Delogu,M. E. Fantacci,P. Kasae
Physics , 2007, DOI: 10.1016/j.nima.2006.08.093
Abstract: 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.
An automatic system to discriminate malignant from benign massive lesions in mammograms
P. Delogu,M. E. Fantacci,P. Kasae,A. Retico
Physics , 2007,
Abstract: Evaluating the degree of malignancy of a massive lesion on the basis of the mere visual analysis of the mammogram is a non-trivial task. We developed a semi-automated system for massive-lesion characterization with the aim to support the radiological diagnosis. A dataset of 226 masses has been used in the present analysis. The system performances have been evaluated in terms of the area under the ROC curve, obtaining A_z=0.80+-0.04.
Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier
P. Delogu,M. E. Fantacci,P. Kasae,A. Retico
Physics , 2008, DOI: 10.1016/j.compbiomed.2007.01.009
Abstract: The computer-aided diagnosis system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented mass. Mass segmentation plays a key role in most computerized systems. Our technique is a gradient-based one, showing the main characteristic that no free parameters have been evaluated on the dataset used in this analysis, thus it can directly be applied to datasets acquired in different conditions without any ad-hoc modification. A dataset of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses. Sixteen features based on shape, size and intensity of the segmented masses are analyzed by a multi-layered perceptron neural network. A feature selection procedure has been carried out on the basis of the feature discriminating power and of the linear correlations interplaying among them. The comparison of the areas under the ROC curves obtained by varying the number of features to be classified has shown that 12 selected features out of the 16 computed ones are powerful enough to achieve the best classifier performances. The radiologist assigned the segmented masses to three different categories: correctly-, acceptably- and non-acceptably-segmented masses. We initially estimated the area under ROC curve only on the first category of segmented masses (the 88.5% of the dataset), then extending the dataset to the second sub-class (reaching the 97.8% of the dataset) and finally to the whole dataset, obtaining Az = 0.805+-0.030, 0.787+-0.024 and 0.780+-0.023, respectively.
An automated system for lung nodule detection in low-dose computed tomography
I. Gori,M. E. Fantacci,A. Preite Martinez,A. Retico
Physics , 2007, DOI: 10.1117/12.709642
Abstract: 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.
Lung nodule detection in low-dose and high-resolution CT scans
P. Delogu,M. E. Fantacci,I. Gori,A. Preite Martinez,A. Retico,A. Tata
Physics , 2007,
Abstract: We are developing a computer-aided detection (CAD) system for the identification of small pulmonary nodules in screening CT scans. The main modules of our system, i.e. a dot-enhancement filter for nodule candidate selection and a neural classifier for false positive finding reduction, are described. The preliminary results obtained on the so-far collected database of lung CT are discussed.
Computer-aided detection of pulmonary nodules in low-dose CT
P. Delogu,M. E. Fantacci,I. Gori,A. Preite Martinez,A. Retico
Physics , 2007,
Abstract: A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.
A scalable system for microcalcification cluster automated detection in a distributed mammographic database
P. Delogu,M. E. Fantacci,A. Preite Martinez,A. Retico,A. Stefanini,A. Tata
Physics , 2007,
Abstract: 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 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 mammographic data. We report and discuss the system performances on different datasets of mammograms and the status of the GRID-enabled CADe analysis.
A scalable Computer-Aided Detection system for microcalcification cluster identification in a pan-European distributed database of mammograms
A. Retico,P. Delogu,M. E. Fantacci,A. Preite Martinez,A. Stefanini,A. Tata
Physics , 2007, DOI: 10.1016/j.nima.2006.08.094
Abstract: 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.
A multi-scale approach to the computer-aided detection of microcalcification clusters in digital mammograms
P. Delogu,M. E. Fantacci,A. Retico,A. Stefanini,A. Tata
Physics , 2007,
Abstract: A computer-aided detection (CADe) system for the identification of microcalcification clusters in digital mammograms has been developed. It is mainly based on the application of wavelet transforms for image filtering and neural networks for both the feature extraction and the classification procedures. This CADe system is easily adaptable to different databases. We report and compare the FROC curves obtained on the private database we used for developing the CADe system and on the publicly available MIAS database. The results achieved on the two databases show the same trend, thus demonstrating the good generalization capability of the system.
GPCALMA: a Grid Approach to Mammographic Screening
S. Bagnasco,U. Bottigli,P. Cerello,P. Delogu,M. E. Fantacci,E. Lopez Torres,G. L. Masala,P. Oliva,A. Retico,S. Stumbo
Physics , 2003, DOI: 10.1016/j.nima.2003.11.032
Abstract: 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.
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