I. De Mitri

A completely automated CAD system for mass detection in a large mammographic database

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 segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologists diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

A CAD system for nodule detection in low‐dose lung CTs based on region growing and a new active contour model

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 of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

Scintillation efficiency of nuclear recoil in liquid xenon

Abstract We present the results of a test done with a Liquid Xenon (LXe) detector for “Dark Matter” search, exposed to a neutron beam to produce nuclear recoil events simulating those which would be generated by WIMPs elastic scattering. The aim of the experiment was to measure directly the scintillation efficiency of nuclear recoil. The nuclear recoil considered in the test was in the tens of keV range. The ratio of measured visible energy over the true recoil energy was evaluated to be about 20%, in good agreement with the theoretical predictions.

Detection of scintillation light in coincidence with ionizing tracks in a liquid argon time projection chamber

A system to detect light from liquid argon scintillation has been implemented in a small, ICARUS-like, liquid argon time projection chamber. The system, which uses a VUV-sensitive photomultiplier to collect the light, has recorded many ionizing tracks from cosmic-rays in coincidence with scintillation signals. Our measurements demonstrate that scintillation light detection can provide an effective method for absolute time measurement of events and eventually a useful trigger signal

Calibration of BC501A liquid scintillator cells with monochromatic neutron beams

Abstract The recoil proton energy response has been measured by exposing cylindrical cells, filled with BC501A BICRON liquid scintillator, to mono-energetic neutron reference fields. We determine the required calibration parameters and report the detailed procedures for the experimental data handling. A dedicated Monte Carlo simulation of the detector response and efficiency has been performed. It showed good agreement with the measured quantities. The results from this calibration are necessary for a detailed study of the neutron spectrum at the underground Gran Sasso Laboratory, with a neutron detector made of 32 liquid scintillator cells, like those used during the calibration.

The Trigger System of the ARGO-YBJ detector

Astrophysical radiation with ground-based observatory at YangBaJing (ARGO-YBJ) is a ground-based cosmic ray detector presently under construction at the Yangbajing High Altitude Cosmic Ray Laboratory (4300 m a.s.l.), Lhasa, Tibet. The apparatus has been designed to detect air showers with an energy threshold of a few hundred GeV and it has a total active area of 6400 m/sup 2/. The full-coverage central carpet of 5772 m/sup 2/ is equipped with an array of resistive plate counters. This array is surrounded by a partially instrumented guard ring to improve the reconstruction of showers with the core falling outside the carpet. The ARGO trigger logic implements simple yet robust algorithms, based on the timing distribution of the hits and their multiplicity on the central carpet. In this paper, we describe the hardware architecture and the main features of the trigger system.

Argo-ybj Observation of the Large-scale Cosmic ray Anisotropy During the Solar Minimum Between Cycles 23 and 24

This paper reports on the measurement of the large-scale anisotropy in the distribution of cosmic-ray arrival directions using the data collected by the air shower detector ARGO-YBJ from 2008 January to 2009 December, during the minimum of solar activity between cycles 23 and 24. In this period, more than 2 × 10 11 showers were recorded with energies between ∼1 and 30 TeV. The observed two-dimensional distribution of cosmic rays is characterized by two wide regions of excess and deficit, respectively, both of relative intensity ∼10 −3 with respect to a uniform flux, superimposed on smaller size structures. The harmonic analysis shows that the large-scale cosmic-ray relative intensity as a function of R.A. can be described by the first and second terms of a Fouries series. The high event statistics allow the study of the energy dependence of the anistropy, showing that the amplitude increases with energy, with a maximum intensity at ∼10 TeV, and then decreases while the phase slowly shifts toward lower values of R.A. with increasing energy. The ARGO-YBJ data provide accurate observations over more than a decade of energy around this feature of the anisotropy spectrum.

STUDY OF THE DIFFUSE GAMMA-RAY EMISSION FROM THE GALACTIC PLANE WITH ARGO-YBJ

The events recorded by ARGO-YBJ in more than fiveyears of data collection have been analyzed to determine the diffuse gamma-ray emission in the Galactic plane at Galactic longitudes 25° < l < 100° and Galactic latitudes b 5 ∣ ∣< °. The energy range covered by this analysis, from ∼350 GeV to ∼2 TeV, allows the connection of the region explored by Fermi with the multi-TeV measurements carried out by Milagro. Our analysis has been focused on two selected regions of the Galactic plane, i.e., 40° < l < 100° and 65° < l <8 5 °( the Cygnus region), where Milagro observed an excess with respect to the predictions of current models. Great care has been taken in order to mask the most intense gamma-ray sources, including the TeV counterpart of the Cygnus cocoon recently identified by ARGO-YBJ, and to remove residual contributions. The ARGO-YBJ results do not show any excess at sub-TeV energies corresponding to the excess found by Milagro, and are consistent with the predictions of the Fermi model for the diffuse Galactic emission. From the measured energy distribution we derive spectral indices and the differential flux at 1 TeV of the diffuse gamma-ray emission in the sky regions investigated.

A massive lesion detection algorithm in mammography

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps: 1) reduction of the dimension of the image to be processed through the identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the INFN (Istituto Nazionale Fisica Nucleare) research project GPCALMA (Grid Platform for Calma) which recruits physicists and radiologists from different Italian Research Institutions and hospitals to develop software for breast cancer detection.

The MAGIC-5 Project: medical applications on a GRID infrastructure connection

The MAGIC-5 Project aims at developing computer aided detection (CAD) software for medical applications on distributed databases by means of a GRID infrastructure connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, computed-tomography (CT) images for lung cancer analysis, and the positron emission tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 Collaboration can be seen as a group of distributed users sharing their resources for implementing different virtual organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.