0000000000963453
AUTHOR
A Lauria
Distributed medical images analysis on a Grid infrastructure
In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists’ performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow…
Strategies for Disease Prevention and Health Promotion in Urban Areas: The Erice 50 Charter
The Erice 50 Charter titled "Strategies for Diseases Prevention and Health Promotion in Urban Areas" was unanimously approved at the conclusion of the 50th Residential Course "Urban Health. Instruments for promoting health and for assessing hygienic and sanitary conditions in urban areas", held from 29th March to 2nd April 2017 in Erice, at the "Ettore Majorana" Foundation and Centre for Scientific Culture and promoted by the International School of Epidemiology and Preventive Medicine "G. D'Alessandro" and the Study Group "Building Hygiene" of the Italian Society of Hygiene, Preventive Medicine and Public Health (SItI). At the conclusion of the intense learning experience during the Course…
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 secon…