Francesco De Carlo

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Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule(More)
A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists(More)
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image(More)
Bone's microporosities play important biologic and mechanical roles. Here, we quantified 3D changes in cortical osteocyte-lacunae and other small porosities induced by estrogen withdrawal and two different osteoporosis treatments. Unlike 2D measurements, these data collected via synchrotron radiation-based μCT describe the size and 3D spatial distribution(More)
OBJECTIVE The aim of this study was to analyze EEG background activity in Huntington's disease (HD) patients and relatives at risk, in relation to CAG repeat size and clinical state, in order to detect an electrophysiological marker of early disease. METHODS We selected 13 patients and 7 subjects at risk. Thirteen normal subjects, sex- and age-matched,(More)
The aim of this study is to perform a topographic classification of electroencephalographic (EEG) patterns in subjects affected by the Huntington's disease (HD). The alpha activity is a discriminating feature for HD, as its amplitude reduction turns out to be a clear mark of the illness. When used as input variable to a supervised neural network, a good(More)
Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an(More)
BACKGROUND Currently, men are younger at the time of diagnosis of prostate cancer and more interested in less invasive surgical approaches (traditional laparoscopy, 3D-laparoscopy, robotics). Outcomes of continence, erectile function, cancer cure, positive surgical margins and complication are well collected in the pentafecta rate. However, no comparative(More)
Spontaneous EEG patterns are studied to detect migraine patients both during the attack and in headache-free periods. The EEG signals are analyzed through the wavelets and both scale-dependent and scale-independent features are computed to characterize the patterns. The classification is carried out by a supervised neural network. The efficiency of the(More)