A Novel Expert System for Non-Invasive Liver Iron Overload Estimation in Thalassemic Patients

Expert Systems can integrate logic based often on computational intelligence methods and they are used in complex problem solving. In this work an Expert System for classifying liver iron concentration in thalassemic patients is presented. In this work, an ANN is used to validate the output of the L.I.O.MO.T (Liver Iron Overload Monitoring in Thalassemia) method against the output of the state-of-the-art method based on MRI T2 assessment for liver iron concentration. The model has been validated with a dataset of 200 samples. The experimental Mean Squared Error results and Correlation show interesting performances. The proposed algorithm has been developed as a plug in for OsiriX Dicom View…

Detecting faulty wireless sensor nodes through Stochastic classification

In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an ana…

Quantification of epicardial adipose tissue in coronary calcium score and CT coronary angiography image data sets: comparison of attenuation values, thickness and volumes

The aim of the study was to compare epicardial adipose tissue (EAT) characteristics assessed with coronary calcium score (CS) and CT coronary angiography (CTCA) image data sets.In 76 patients (mean age 59 ± 13 years) who underwent CS and CTCA owing to suspected coronary artery disease (CAD), EAT was quantified in terms of density (Hounsfield units), thickness and volume. The EAT volume was extracted with a semi-automatic software.A moderate correlation was found between EAT density in CS and CTCA image data sets (-100 ± 19 HU vs -70 ± 24 HU; p  0.05, r = 0.55). The distribution of EAT was not symmetrical with a maximal thickness at the right atrioventricular groove (14.2 ± 5.3 mm in CS, 15.…

Exploiting the Human Factor in a WSN-Based System for Ambient Intelligence

Practical applications of ambient intelligence cannot leave aside requirements about ubiquity, scalability, and transparency to the user. An enabling technology to comply with this goal is represented by wireless sensor networks (WSNs); however, although capable of limited in-network processing, they lack the computational power to act as a comprehensive intelligent system. By taking inspiration from the sensory processing model of complex biological organisms, we propose here a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part. WSNs act as a transparent interface that allows the system to understand human requirements through impl…

A PROBABILISTIC APPROACH TO ANOMALY DETECTION FOR WIRELESS SENSOR NETWORKS

An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform …

A Novel Web Service for Mammography Images Indexing

Medical community needs to extract precise information from a large amount of data. These data are a collection of different types such as text documents, images and video. Currently medical technology do not provide an intelligent methodology for documents recovery and classification of such documents based on their content. In this work the radiological structured reports are analysed with the corresponding mammographic images. The presented system is composed of an Indexing Engine and a Searching Engine, based on innovative methods for IR (Information Retrieval). The proposed work is useful for physicians as support diagnosis system, for students as learning support system, and finally, …

SINCONAPP: A Computerized learning tool for CBCT normal anatomy and variants of the nose and paranasal sinuses

1. Purpose To supply an useful learning tool aimed to interactively display on mobile devices normal anatomy and variants of the nose and paranasal sinuses as seen on CBCT images. 2. Methods and Materials Images Images of the nose and paranasal sinuses were derived by a study series acquired by a CBCT device. CBCT studies of the paranasal sinuses were acquired in patients referred for nasal obstruction or sinusitis with the following parameters: 90 kVp, 12.5 mA, 20 s rotation time, FOV 13 x 14.5 cm, 0.25 x 0.25 x 0.25 mm voxel size. Software The application has been developed for iOS based mobile devices through the platform XCode provided by Apple®, and it is developed using the Objective-…

A Novel Approach for Faulty Sensor Detection and Data Correction in Wireless Sensor Network

he main Wireless Sensor Networks purpose is represented by areas of interest monitoring. Even if the Wireless sensor network is properly initialized, errors can occur during its monitoring tasks. The present work describes an approach for detecting faulty sensors in Wireless Sensor Network and for correcting their corrupted data. The approach is based on the assumption that exist a spatio-temporal cross- correlations among sensors. Two sequential mathematical tools are used. The first stage is a probabilistic tools, namely Markov Random Field, for a two-fold sensor classification (working or damaged). The last stage is represented by the Locally Weighted Regression model, a learning techniq…

Probabilistic Anomaly Detection for Wireless Sensor Networks

Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities.

Come quantificare la fibrosi del miocardio ventricolare sinistro mediante software semiautomatico in pazienti sottoposti a risonanza magnetica cardiaca per sospetta miocardite

A text based indexing system for mammographic image retrieval and classification

Abstract In modern medical systems huge amount of text, words, images and videos are produced and stored in ad hoc databases. Medical community needs to extract precise information from that large amount of data. Currently ICT approaches do not provide a methodology for content-based medical images retrieval and classification. On the other hand, from the Internet of Things (IoT) perspective, the ICT medical data can be produced by several devices. Produced data complies with all Big Data features and constraints. The IoT guidelines put at the center of the system a new smart software to manage and transform Big Data in a new understanding form. This paper describes a text based indexing sy…

Come effettuare il calcolo del volume del grasso epicardico mediante software semiautomatico in pazienti sottoposti a TC del cuore

Quantificazione del grasso epicardico mediante TC del cuore: associazione con i fattori di rischio cardiovascolare e con l'aterosclerosi coronarica

Confronto tra acquisizioni di Calcium Score e CardioTC per la quantificazione del volume e della densità del grasso epicardico

Bayesian Network Based Classification of Mammography Structured Reports

In modern medical domain, documents are created directly in electronic form and stored on huge databases containing documents, text in integral form and images. Retrieving right informations from these servers is challenging and, sometimes, this is very time consuming. Current medical technology do not provide a smart methodology classification of such documents based on their content. In this work the radiological structured reports are analysed classified and assigning an appropriate label. The text classifier is used to label a mammographic structured report. The experimental data are real clinical report coming from a hospital server. Analysing the structured report content, the classif…