Rosella Cataldo

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 novel multithreshold method for nodule detection in lung CT

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 detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.

Construction of Digital Elevation Models for a Southern European City and a Comparative Morphological Analysis with Respect to Northern European and North American Cities

Abstract A morphometric analysis of a southern European city and the derivation of relevant fluid dynamical parameters for use in urban flow and dispersion models are explained in this paper. Calculated parameters are compared with building statistics that have already been computed for parts of three northern European and two North American cities. The aim of this comparison is to identify similarities and differences between several building configurations and city types, such as building packing density, compact versus sprawling neighborhoods, regular versus irregular street orientation, etc. A novel aspect of this work is the derivation and use of digital elevation models (DEMs) for parts of a southern European city. Another novel aspect is the DEMs’ construction methodology, which is low cost, low tech, and of simple implementation. Several building morphological parameters are calculated from the urban DEMs using image processing techniques. The correctness and robustness of these techniques have be...

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.

Diagnostic of the conservation state in the crypt of the Abbey of Montecorona: biological, microclimatic and geophysical evaluations

The Abbey S Salvatore of Montecorona, an important Benedictine monastary of the eleventh century, is placed at Umbertide, on the Northwest of Perugia (Italy). The site is in the Umbria region, characterized by a well-documented historical and instrumental seismicity, which periodically exposes this area to hazards with widespread damage for the population and the built-up environment. This paper focused on the study of the conservation state of the crypt of the Abbey. A multidisciplinary approach, using biological and physical non-destructive methods, is proposed. First, we investigated the microbial biodiversity of the crypt, analysing the presence of microorganisms by microscopic and cultivation methods. The second step was the study of the influence of the environment on the colonization and growth of these microorganisms, with a continuous monitoring of the microclimate inside the crypt, especially the thermo-hygrometric conditions. Moreover, with the aims of localizing the structures involved in the deterioration process, such as fractures, moisture, etc, ground penetrating radar (GPR) surveys, with different methodologies, were carried out in the crypt: reflection mode on the floor and traveltime tomography on the ceiling. From GPR data, a structure of archaeological interest was evidenced and, by means of a frequency signal analysis, the underground water content of the stone was also evaluated, assessing the correlation between the spectral content and moisture degree. The integration of information from these different methods provided some interesting results, also addressing possible interventions for protection and conservation of the crypt.

Subsurface water-content identification in a crypt using GPR and comparison with microclimatic conditions

The effects of climate, pollution and human negligence cause severe and sometimes irreversible damage to buildings and monuments of cultural interest. It is well known that the presence of water and/or moisture content in a porous material is the initial cause of deterioration. In a previous paper, the authors reported an integrated study on a building of cultural importance, namely the crypt of the Cattedrale di Otranto in Apulia, Italy, based on non-destructive integrated biological and physical surveys. The method described was able to identify the ‘internal’ factors responsible for deterioration. It was discovered that the distribution of moisture in the stone depended mainly on adverse environmental conditions, and the presence of wet buried structures in the ground. The first aim of the present study was to identify subsurface water-content in this same crypt using a ground-penetrating radar (GPR) technique, and to compare these results with those of the previous microclimatic survey. In particular, the existence of underground discontinuities was verified; we located them and analysed their influence. Moreover, by means of velocity analysis, we obtained a quantitative estimate of the volumetric water-content under the pavement of the crypt. This finding completes the results of the previous research, as it indicates the causes of the deterioration in the crypt and provides significant information, on the basis of which, effective decisions can be made for safeguarding the historic building.

Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages

We detail a procedure for generating a set of templates for the hippocampal region in magnetic resonance (MR) images, representative of the clinical conditions of the population under investigation.

Modeling the microscopic electrical properties of thrombin binding aptamer (TBA) for label-free biosensors

Aptamers are chemically produced oligonucleotides, able to bind a variety of targets such as drugs, proteins and pathogens with high sensitivity and selectivity. Therefore, aptamers are largely employed for producing label-free biosensors (aptasensors), with significant applications in diagnostics and drug delivery. In particular, the anti-thrombin aptamers are biomolecules of high interest for clinical use, because of their ability to recognize and bind the thrombin enzyme. Among them, the DNA 15-mer aptamer (TBA), has been widely explored around the possibility of using it in aptasensors. This paper proposes a microscopic model of the electrical properties of TBA and of the aptamer-thrombin complex, combining information from both structure and function, following the issues addressed in an emerging branch of electronics known as proteotronics. The theoretical results are compared and validated with measurements reported in the literature. Finally, the model suggests resistance measurements as a novel tool for testing aptamer-target affinity.

Detection and classification of microcalcifications clusters in digitized mammograms

In the present paper we discuss a new approach for the detection of microcalcification clusters, based on neural networks and developed as part of the MAGIC-5 project, an INFN-funded program which aims at the development and implementation of CAD algorithms in a GRID-based distributed environment. The proposed approach has as its roots the desire to maximize the rejection of background during the analytical pre-processing stage, in order to train and test the neural network with as clean as possible a sample and therefore maximize its performance. The algorithm is composed of three modules: the image pre-processing, the feature extraction component and the Backpropagation Neural Network module. The First module comprises the use of several algorithms: H-Dome Transformation, Masking, Binarisation of grayscale images, Connected Components Labeling; for the classification, initially 27 features are extracted from the output image, features that are statistically analyzed and reduced to 17, which are used as input to the Backpropagation Neural Network. The algorithm was trained (tested) on 139 (139) images respectively, containing 149 (152) true clusters and 146 (415) false

3D high resolution GPR survey to help the reconstruction of the archaeological stratigraphy of Lecce (Italy)

3D high resolution Ground Penetrating Radar (GPR) survey was performed in the Crypt of the Duomo of Lecce (South Italy), built in 1114. The GPR data revealed us a stratified subsoil in which there is a distribution, with the depth, of several “remains” referable to different epochs. Here we present and discuss the experimental evidences, comparing them with the historical-archaeological documentation. We think that they constitute a valid contribution to the knowledge of the ancient stratigraphy, as well as of the Roman history of Lecce, especially because of many suppositions have not yet found a confirmation.