Fast Fingerprints Classification Only Using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

Image Processing Chain For Digital Still Cameras Based On The Simpil Architecture

The new generation of wireless devices herald the development of products for integrated portable image and video communication requiring to image and video applications high computing performance. Portable MultiMedia Supercomputers (PMMS), a new class of architectures, allow to combine high computational performance, needed by multimedia applications, and a big energy efficiency, needed by portable devices. Among PMMS, the SIMPil (SIMD processor pixel) architecture satisfies the above requirements, especially with video and digital images processing tasks. In this paper we, exploit the SIMPil computation and throughput efficiency to implement the whole image processing chain of a digital s…

MLP Neural Network Implementation on a SIMD Architecture

An Automatic Road Sign Recognition System {A(RS)2} is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an A(RS)2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology …

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…

Morphological Enhancement and Triangular Matching for Fingerprint Recognition

Among the principal problems for realizing a robust Automated Fingerprint Identification System (AFIS) there are the images quality and matching algorithms. In this paper a fingerprint enhancement algorithm based on morphological filter and a triangular matching are introduced. The enhancement phase is based on tree steps: directional decomposition, morphological filter and composition. For the matching phase a global transformation to overcame the effects of rotation, displacement and deformation between acquired and stored fingerprint is performed using the number of similar triangular, having fingerprint minutiae as vertexes. The performance of the proposed approach has been evaluated on…

Fast Fuzzy Fusion in Multimodal Biometric Systems

A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks

Service-continuity in distributed computing can be enhanced by designing self-organized systems, with a non-fixed structure, able to modify their structure and organization, as well as adaptively react to internal and external environment changes. In this paper, an architecture exploiting a bio-inspired management approach, i.e., the functioning of cell metabolism, for specialized computing environments in Service-Oriented Networks (SONs) is proposed. Similar to the processes acting in metabolic networks, the nodes communicate to each other by means of stimulation or suppression chains giving rise to emergent behaviors to defend against foreign invaders, attacks, and malfunctioning. The mai…

An extended JADE-S based framework for developing secure Multi-Agent Systems

Agent communities are self-organized virtual spaces consisting of a large number of agents and their dynamic environments. Within a community, agents group together offering special e-services for effective, reliable, and mutual benefits. Usually, an agent community is composed of specialized agents performing one or more tasks in a single domain/sub-domain, or in highly intersecting domains. However, secure Multi- Agent Systems require severe mechanisms in order to prevent malicious attacks. Several limits affect exiting secure agents platform, such as the lack of a strong authentication system, the lack of a flexible distributed mechanism for access control and the lack of a system for st…

Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2 in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume…

An Embedded Biometric Sensor for Ubiquitous Authentication

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems…

CliffoSor: A Parallel Embedded Architecture for Geometric Algebra and Computer Graphics

Geometric object representation and their transformations are the two key aspects in computer graphics applications. Traditionally, compute-intensive matrix calculations are involved to model and render 3D scenery. Geometric algebra (a.k.a. Clifford algebra) is gaining growing attention for its natural way to model geometric facts coupled with its being a powerful analytical tool for symbolic calculations. In this paper, the architecture of CliffoSor (Clifford Processor) is introduced. ClifforSor is an embedded parallel coprocessing core that offers direct hardware support to Clifford algebra operators. A prototype implementation on an FPGA board is detailed. Initial test results show more …

Valutazione semi-automatica dell’impregnazione post-contrastografica delle anse intestinali sede di malattia di Crohn, utilizzando diverse finestre temporali.

A SIR based method for pancreas iron burden measurement in MRI T2* GRE sequences.

GAPPCO: An Easy to Configure Geometric Algebra Coprocessor Based on GAPP Programs

Because of the high numeric complexity of Geometric Algebra, its use in engineering applications relies heavily on tools and devices for efficient implementations. In this article, we present a novel hardware design for a Geometric Algebra coprocessor, called GAPPCO, which is based on Geometric Algebra Parallelism Programs (GAPP). GAPPCO is a design for a coprocessor combining the advantages of optimizing software with a configurable hardware able to implement arbitrary Geometric Algebra algorithms. The idea is to have a fixed hardware easily and fast to be configured for different algorithms. We describe the new hardware design together with the complete tool chain for its configuration.

Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …

A vision agent for mobile robot navigation in time-variable environments

We present an architecture for mobile robot navigation based on Bayesian networks. The architecture allows a robot to plan the correct path inside an environment with dynamic obstacles. Interactions between the robot and the environment are based on a powerful vision agent. The results of simulations, showing the effectiveness of the approach, are described.

A bio-inspired approach to attack graphs analysis

Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graphs analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building…

A Novel Iris Recognition System based on Micro-Features

In this paper a novel approach for iris recognition system based on iris micro-features is proposed. The proposed system follows the minutiae based approach developed for fingerprint recognition systems. The proposed system uses four iris microfeatures, considered as minutiae, for identification. The individualized characteristics are nucleus, collarette, valleys and radius. Iris recognition is divided in three main phases: image preprocessing, micro-features extraction and matching. The algorithm has been tested on CASIA v1.0 iris image database obtaining an high accuracy. The obtained experimental results have been analyzed and compared with the Daugman based approach.

An edge-driven 3D region growing approach for upper airways morphology and volume evaluation in patients with Pierre Robin sequence

In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D model of the upper airways. This method can be used to integrate information inside a medical decision support system, making it possible to enhance medical evaluation. The effectiveness of the proposed segmentation approach was evaluated using Jaccard (92.1733%) and dice (94.6441%) similarity indices and specificity (96.8895%) and sensitivity (97.6682%) rates. The proposed method achieved an average computation time reduced by a 16x factor with respect to m…

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.…

Design Space Exploration of Parallel Embedded Architectures for Native Clifford Algebra Operations

In the past few decades, Geometric or Clifford algebra (CA) has received a growing attention in many research fields, such as robotics, machine vision and computer graphics, as a natural and intuitive way to model geometric objects and their transformations. At the same time, the high dimensionality of Clifford algebra and its computational complexity demand specialized hardware architectures for the direct support of Clifford data types and operators. This paper presents the design space exploration of parallel embedded architectures for native execution of four-dimensional (4D) and five-dimensional (5D) Clifford algebra operations. The design space exploration has been described along wit…

Three Hours Ahead Prevision of SO2 Pollutant Concentration Using an Elman Neural based Forecaster

Abstract Indoor air quality near the industrial site is tightly joined to pollutant concentration level, since outdoor pollution heavily influences air quality and, consequently, inhabitants health. A pollution management system is essential for health protection. Automatic air quality management systems have became an important research issue with strong implications for inhabitants’ health. In this paper an automatic forecaster based on neural networks for SO 2 concentration prevision is proposed. The analyzed area covers different small towns near the industrial site of Priolo, in the south of the world. Among these towns, Melilli was the first town in Italy that was evacuated for high l…

Fingerprint and Smartcards in Embedded Authentication Systems

Assessment of volume and density of epicardial fat: comparison between CT calcium score and CT coronary angiography scans.

Aims and objectives Methods and materials Results Conclusion Personal information References

An Empirical Set of Metrics for Embedded Systems Testing

Editor’s note: Selecting the right platform for an embedded system is a challenging task, because there are no systematic methodologies for comprehensive evaluation and comparison of competing alternatives. This article partially addresses this problem by formulating a set of relevant metrics and an evaluation methodology. By applying their concepts to the evaluation of two alternative platforms, the Raspberry Pi3 and the Intel Edison boards, the authors demonstrate the utility of their approach and show under which objectives one is preferable over the other. —Axel Jantsch, TU Wien

Semi-automated evaluation of small bowel mural attenuation at CT enterography using different temporal windows in patients affected by active Crohn disease

A critical review of non invasive procedures for the evaluation of body iron burden in thalassemia major patients

CliffoSor, an innovative FPGA-based architecture for geometric algebra

Accelerating Clifford Algebra Operations using GPUs and an OpenCL Code Generator

Clifford Algebra (CA) is a powerful mathematical language that allows for a simple and intuitive representation of geometric objects and their transformations. It has important applications in many research fields, such as computer graphics, robotics, and machine vision. Direct hardware support of Clifford data types and operators is needed to accelerate applications based on Clifford Algebra. This paper proposes a mixed software-hardware system that exploits the computational power of Graphics Processing Units (GPUs) to accelerate Clifford operations. A code generator, namely OpenCLifford, is presented that automatically generates Java and C libraries for the direct support of Clifford ele…

A Self-Contained Biometric Sensor for Ubiquitous Autentication

Embedded Knowledge-based Speech Detectors for Real-Time Recognition Tasks

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of automatic speech recognition (ASR) systems are comparable to human speech recognition (HSR) only under very strict working conditions, and in general much lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to raise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as de…

BioAnalysis: A Framework for Structural and Functional Robustness Analysis of Metabolic Networks

The main objective of this work is to analyze metabolic networks evolution in terms of their robustness and fault tolerance capabilities. In metabolic networks, errors can be seen as random removal of network nodes, while attacks are high-connectivity-degree node deletion aimed at compromising network activity. This paper proposes a software framework, namely BioAnalysis, used to test the robustness and the fault tolerance capabilities of real metabolic networks, when mutations and node deletions affect the network structure. The performed simulations are related to the central metabolic network of the well-known E. coli single-celled bacterium and involve either hub nodes or non-hub nodes,…

Fingerprint image enhancement using directional morphological filter

Fingerprint images quality enhancement is a topic phase to ensure good performance in an automatic fingerprint identification system (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on three steps: directional decomposition, morphological filter and composition. The performance of the proposed approach has been evaluated on two sets of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner

Applications of a neural network to forecast the hourly pollutant’s concentrations (SO2) in the industrial site of Priolo (Sicily)

Biologically Inspired Hardware: Status and Perspectives (Invited Talk)

Biologically Inspired Computing places its emphasis on robustness, adaptability, and emergent organization considering the interaction of many processes. Bio-inspired algorithms exhibit strength and flexibility in poorly defined or time-variable tasks, as well as when the global behaviour is achieved by simple interacting of species or agents. The above philosophy links various disciplines such as artificial and computationally intelligence, evolutionary computation, bio-robotics, agent-based systems, and Digital Ecosystems. In this paper, the main branches of bio-inspired computing are briefly discussed. Successively, the impact of the bioinspired paradigm on innovative hardware structures…

Illumination correction on biomedical images

RF-Inhomogeneity Correction (aka bias) artifact is an important re- search field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images alter- ing their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the ap…

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray ima…

Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis

Nowadays, uterine fibroids can be treated using Magnetic Resonance guided Focused Ultrasound Surgery (MRgFUS), which is a non-invasive therapy exploiting thermal ablation. In order to measure the Non-Perfused Volume (NPV) for treatment response assessment, the ablated fibroid areas (i.e., Region of Treatment, ROT) are manually contoured by a radiologist. The current operator-dependent methodology could affect the subsequent follow-up phases, due to the lack of result repeatability. In addition, this fully manual procedure is time-consuming, considerably increasing execution times. These critical issues can be addressed only by means of accurate and efficient automated Pattern Recognition ap…

Neural Networks and Metabolic Networks: Fault Tolerance and Robustness Features

The main objective of this work is the comparison between metabolic networks and neural networks (ANNs) in terms of their robustness and fault tolerance capabilities. In the context of metabolic networks errors are random removal of network nodes, while attacks are failures in the network caused intentionally. In the contest of neural networks errors are usually defined configurations of input submitted to the network that are affected by noise, while the failures are defined as the removal of some network neurons. This study have proven that ANNs are very robust networks, with respect to the presence of noise in the inputs, and the partial removal of some nodes, until it reached a critical…

A Mobile Agent Based System for Documents Classification and Retrieval

A Programmable Networked Processing Node for 3D Brain Vessels Reconstruction

Real-time 3D imaging represents a developing trend in medical imaging. However, most of the 3D medical imaging algorithms are computationally intensive. In this paper, a programmable networked node for 3D brain vessels reconstruction is proposed. Starting from 2D PC-MRA (Phase-Contrast Magnetic Resonance Angiography) sequences, the node is able to generate the 3D brain vasculature using the MIP (Maximum Intensity Projection) algorithm. The node has been prototyped on the Celoxica RC203E board, equipped with a Virtex II FPGA, to get the advantages of an hardware implementation, reaching a better throughput with respect to analogous software implementations. Its generality and programmable ca…

Design exploration of aes accelerators on FPGAS and GPUs

The embedded systems are increasingly becoming a key technological component of all kinds of complex tech-nical systems and an exhaustive analysis of the state of the art of all current performance with respect to architectures, design methodologies, test and applications could be very in-teresting. The Advanced Encryption Standard (AES), based on the well-known algorithm Rijndael, is designed to be easily implemented in hardware and software platforms. General purpose computing on graphics processing unit (GPGPU) is an alternative to recongurable accelerators based on FPGA devices. This paper presents a direct comparison between FPGA and GPU used as accelerators for the AES cipher. The res…

Clinical Anatomy and information technology.

Fingerprint and Iris based Authentication in Intercooperative Emerging e-Infrastructures

E-infrastructures must support the development of heterogeneous applications for workstation network, for mobile and portable systems and devices. In this context and relating to all collaborative and pervasive computational technology a very important role is played by security and authentication systems, which represent the first step of the whole process. Biometric authentication systems represent a valid alternative to conventional authentication systems providing robust procedures for user authentication. On the other hand, Internet of Things involves a heterogeneous set of interacting devices to enable innovative global and local applications and services for users. In this chapter fi…

Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments

Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, …

Biological target volume segmentation for radiotherapy treatment planning

An ontology-based retrieval system for mammographic reports

In healthcare domain it can be useful to compare unstructured free-text clinical reports in order to enable the search for similar and/or relevant clinical cases. In data mining and text analysis tasks, the cosine similarity is usually used for texts comparison purposes. It is usually performed by computing the standard document vector cosine similarity between the two vectors representing the report pair under analysis. In this paper a novel system based on text pre-processing techniques and a modelled medical knowledge, using an improved radiological ontology, is proposed. Medical terms organized in a hierarchical tree can assess semantic similarity relationships between unstructured repo…

Driver Assistance Systems: a Real-Time Road Signs Recognizer

Un Sistema ad Agenti Mobili per la Classificazione e il Recupero di Documenti - A Mobile Agent Based System for Documents Classification and Retrieval

A kernel support vector machine based technique for Crohn’s disease classification in human patients

In this paper a new technique for classification of patients affected by Crohn’s disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The eff…

A Novel System for Multi-level Crohn’s Disease Classification and Grading Based on a Multiclass Support Vector Machine

Crohn’s disease (CD) is a chronic inflammatory condition of the gastrointestinal tract that can highly alter patient’s quality of life. Diagnostic imaging, such as Enterography Magnetic Resonance Imaging (E-MRI), provides crucial information for CD activity assessment. Automatic learning methods play a fundamental role in the classification of CD and allow to avoid the long and expensive manual classification process by radiologists. This paper presents a novel classification method that uses a multiclass Support Vector Machine (SVM) based on a Radial Basis Function (RBF) kernel for the grading of CD inflammatory activity. To validate the system, we have used a dataset composed of 800 E-MRI…

Information Assurance and Advanced Human-Computer Interfaces

METABOLIC NETWORKS ROBUSTNESS: THEORY, SIMULATIONS AND RESULTS

Metabolic networks are composed of several functional modules, reproducing metabolic pathways and describing the entire cellular metabolism of an organism. In the last decade, an enormous interest has grown for the study of tolerance to errors and attacks in metabolic networks. Studies on their robustness have suggested that metabolic networks are tolerant to errors, but very vulnerable to targeted attacks against highly connected nodes. However, many findings on metabolic networks suggest that the above classification is too simple and imprecise, since hub node attacks can be by-passed if alternative metabolic paths can be exploited. On the contrary, non-hub nodes attacks can affect cell …

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 …

Efficient MLP Digital Implementation on FPGA

The efficiency and the accuracy of a digital feed-forward neural networks must be optimized to obtain both high classification rate and minimum area on chip. In this paper an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the High Energy Physics domain and the automatic Road Sign Recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standar…

Human-to-human interfaces: emerging trends and challenges

We present a new research domain, human-to-human interaction (HHI) that describes how today's human interaction is largely indirect and mediated by a wide variety of technologies and devices. We show how this new and exciting field of design originates from the convergence of a few well-established research areas, such as traditional graphical user interfaces (GUIs), tangible user interfaces (TUIs), touchless gesture user interfaces (TGUIs), voice user interfaces (VUIs), and brain computer interfaces (BCIs). We analyse and describe current research in those areas and offer a first-hand view and presentation of its salient aspects for the human-to human interaction domain.

Efficient rapid prototyping of image and video processing algorithms

Image and video processing tasks are often confined for real-time execution on large size workstations or expensively custom designed hardware. The current availability of mature reconfigurable hardware, like Field Programmable Gate Arrays (FPGAs), coupled with the usage of hardware programming languages offers a good path for porting such applications on portable devices. This paper explores the rapid prototyping of a real-time road sign recognition system on a FPGA, using an algorithmic-like hardware programming language: the Handel-C language. We investigate the relationship between efficient Handel-C data, structures, constructs and the related high level C data, structures, constructs.…

A novel numerical meshless approach for electric potential estimation in transcranial stimulation

In this paper, a first application of the method of fundamental solutions in estimating the electric potential and the spatial current density distribution in the brain due to transcranial stimulation, is presented. The coupled boundary value p roblems for the electric potential are solved in a meshless way, so avoiding the use of grid based numerical methods. A multi-spherical geometry is considered and numerical results are discussed.

Automatic multi-seed detection for MR breast image segmentation

In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and m…

An Embedded Fingerprint Recognizer

A Dual-Core Coprocessor with Native 4D Clifford Algebra Support

Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6× average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40× o…

Clinical support in radiation therapy scenarios: MR brain tumor segmentation using an unsupervised fuzzy C-Means clustering technique

An FPGA Implementation of a Quadruple-Based Multiplier for 4D Clifford Algebra

Geometric or Clifford algebra is an interesting paradigm for geometric modeling in fields as computer graphics, machine vision and robotics. In these areas the research effort is actually aimed at finding an efficient implementation of geometric algebra. The best way to exploit the symbolic computing power of geometric algebra is to support its data types and operators directly in hardware. However the natural representation of the algebra elements as variable-length objects causes some problems in the case of a hardware implementation. This paper proposes a 4D Clifford algebra in which the variable-length elements are mapped into fixed-length elements (quadruples). This choice leads to a s…

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-…

MRgFUS Uterine Fibroids treatments in Sicily: Preliminary Results and comparison of a Semi-Automatic and manual contouring

Background: Traditional surgery for uterine fibroids treatments (e.g. myomectomy, hysterectomy) offers very invasive therapeutic approaches, which not always preserve reproductive potential of the woman. MRgFUS (MR guided Focused UltraSound) is a new and non-invasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time [1]. On June 2011 and July 2012 the first treatments were started at HSR-Giglio Hospital in Cefalù and at University Hospital (DIBIMEF) in Palermo. An initial assessment of MRgFUS treatment was made by computing the thermally-ablated volume of uterine fibroid. This volume was evaluated considering the NPV (Non Perfused Volume) on a post-t…

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…

Fingerprint Registration Using Specialized Genetic Algorithms

One of the most common problem to realize a robust matching algorithm in an Automated Fingerprint Identification System (AFIS) is the images registration. In this paper a fingerprints registration method based on a specialized genetic algorithm (GA) is proposed. A global transformation between two fingerprint images is performed using genetic data evolutions based on specialized mutation rate and solution refining. An AFIS including the above method has been developed and tested on two different fingerprint databases: NIST 4 ink-on-paper and self optical scanned. The obtained experimental results show that the proposed approach is comparable with literature systems working on medium quality…

Fast Fingerprints Classification only using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

Message from the SEC 2007 Symposium Chairs

An Embedded, FPGA-based Computer Graphics Coprocessor with Native Geometric Algebra Support

The representation of geometric objects and their transformation are the two key aspects in computer graphics applications. Traditionally, computer-intensive matrix calculations are involved in modeling and rendering three-dimensional (3D) scenery. Geometric algebra (aka Clifford algebra) is attracting attention as a natural way to model geometric facts and as a powerful analytical tool for symbolic calculations. In this paper, the architecture of Clifford coprocessor (CliffoSor) is introduced. CliffoSor is an embedded parallel coprocessing core that offers direct hardware support to Clifford algebra operators. A prototype implementation on a programmable gate array (FPGA) board is detailed…

A Novel Embedded Fingerprints Authentication System Based on Singularity Points

In this paper a novel embedded fingerprints authentication system based on core and delta singularity points detection is proposed. Typical fingerprint recognition systems use core and delta singularity points for classification tasks. On the other hand, the available optical and photoelectric sensors give high quality fingerprint images with well defined core and delta points, if they are present. In the proposed system, fingerprint matching is based on singularity points position, orientation, and relative distance detection. As result, fingerprint matching involves the comparison between few features leading to a very fast system with recognition rates comparable to the standard minutiae…

HCI for biomedical decision-making: From diagnosis to therapy.

Abstract Human-Computer Interaction (HCI) plays a fundamental role in the design of software oriented towards clinical decision-making tasks. Currently, physicians have to deal with an ensemble of systems and software tools in the clinical environment, such as clinical Decision Support Systems (CDSSs), Electronic Health Records (EHRs), Picture Archiving and Communication Systems (PACSs). Moreover, additional platforms aim at collaborative work particularly in telemedicine, where rehabilitation technologies and conversational agents can support the healthcare professionals.

Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users

The way people access resources, data and services, is radically changing using modern mobile technologies. In this scenario, biometry is a good solution for security issues even if its performance is influenced by the acquired data quality. In this paper, a novel embedded automatic fingerprint authentication system (AFAS) for mobile users is described. The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures. The system is focused on the quality evaluation of the raw acquired fingerprint, identifying areas of poor quality. Using this approach, no image enhancement process is needed after the …

Fingerprint Traits and RSA Algorithm Fusion Technique

The present work deals with modern computing systems security issues, focusing on biometric based asymmetric keys generation process. Conventional PKI systems are based on private/public keys generated through RSA or similar algorithms. The present solution embeds biometric information on the private/public keys generation process. In addition the corresponding private key depends on physical or behavioural biometric features and it can be generated when it is needed. Starting from fingerprint acquisition, the biometric identifier is extracted, cyphered, and stored in tamper-resistant smart card to overcome the security problems of centralized databases. Biometric information is then used f…

A User-Friendly Interface for Fingerprint Recognition Systems Based on Natural Language Processing

Biometric recognition systems represent a valid solution to the safety problem of internet accessibility, even if they do not always provide an environment easily comprehensible by users and operators with a mid-level of competence. This gap can be partially filled if, instead of using the conventional access routines to the authentication system, the user could simply write to the system through the interface and using high level sentences and requests be able to use its own natural language to reach the intended goal. On the other hand, biometrics features are widely used for recognition and identification all over the world, generating large databases. In this paper a user-friendly inter…

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment pla…

Neural Classification of HEP Experimental Data

High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topol…

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study ev…

Geometric Calculus Applications to Medical Imaging: Status and Perspectives

Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and De…

An Advanced Technique for User Identification Using Partial Fingerprint

User identification is a very interesting and complex task. Invasive biometrics is based on traits uniqueness and immutability over time. In forensic field, fingerprints have always been considered an essential element for personal recognition. The traditional issue is focused on full fingerprint images matching. In this paper an advanced technique for personal recognition based on partial fingerprint is proposed. This system is based on fingerprint local analysis and micro-features, endpoints and bifurcations, extraction. The proposed approach starts from minutiae extraction from a partial fingerprint image and ends with the final matching score between fingerprint pairs. The computation o…

An Enhanced Autentication System for the JADES-S Platform

Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model

Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most promi-nent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a mini-invasive technique used to deal with inaccessible or insufficiently treated tumors. During the planning phase, the GTV is usually contoured by radiation oncologists using a manual segmentation procedure on MR images. This methodology is certainly time-consuming and op-erator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, is only obtained by using computer-assisted appr…

An Intelligent Sensor for Fingerprint Recognition

Embedded Coprocessors for Native Execution of Geometric Algebra Operations

Clifford algebra or geometric algebra (GA) is a simple and intuitive way to model geometric objects and their transformations. Operating in high-dimensional vector spaces with significant computational costs, the practical use of GA requires dedicated software and/or hardware architectures to directly support Clifford data types and operators. In this paper, a family of embedded coprocessors for the native execution of GA operations is presented. The paper shows the evolution of the coprocessor family focusing on the latest two architectures that offer direct hardware support to up to five-dimensional Clifford operations. The proposed coprocessors exploit hardware-oriented representations o…

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerpr…

Using anatomic and metabolic imaging in stereotactic radio neuro-surgery treatments

Bright Pupil Detection in an Embedded, Real-Time Drowsiness Monitoring System

Driver’s drowsiness is stated as an important cause of road and highway accidents. Therefore, the development of a system for monitoring the driver’s level of fatigue is desirable in order to prevent accidents. The paper presents the design and the implementation of a system able to find and evidence the drowsiness level of a driver in an ordinary motor vehicle, in order to prevent car accidents. The system, made up of a car installed infrared video camera connected to the Celoxica RC203E FPGA based board, is able to perform a real time video stream processing. The system exploits the “bright pupil” phenomenon produced by the retina, that reflects the 90% of the incident light when a radiat…

A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra

Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to ex…

Studio quantitativo della densità ossea dell’ottava vertebra toracica in pazienti sottoposti a TC del cuore

The location of vertebral fractures due to osteoporosis is more frequent in the middle thoracic tract (T8) and in the thoraco-lumbar junction (T12-L1): the T8 vertebra is included in the CT scan volume of the heart. The purpose of the study is to evaluate the density T8 bone in relation to cardiovascular risk factors and the severity of the pathology found in the CT of the heart.

Evaluation of a Support Vector Machine Based Method for Crohn’s Disease Classification

Crohn’s disease (CD) is a chronic, disabling inflammatory bowel disease that affects millions of people worldwide. CD diagnosis is a challenging issue that involves a combination of radiological, endoscopic, histological, and laboratory investigations. Medical imaging plays an important role in the clinical evaluation of CD. Enterography magnetic resonance imaging (E-MRI) has been proven to be a useful diagnostic tool for disease activity assessment. However, the manual classification process by expert radiologists is time-consuming and expensive. This paper proposes the evaluation of an automatic Support Vector Machine (SVM) based supervised learning method for CD classification. A real E-…

Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T…

Fingerprint classification based on deep learning approaches: Experimental findings and comparisons

Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs…

Assessing Coastal Sustainability: A Bayesian Approach for Modeling and Estimating a Global Index for Measuring Risk

Integrated Coastal Zone Management is an emerg- ing research area. The aim is to provide a global view of dif- ferent and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate use- ful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second la…

An Enhanced Autentication System for JADE-S Platform

An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using …

A framework for data-driven adaptive GUI generation based on DICOM

Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investi…

Clifford Algebra based Edge Detector for Color Images

Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend …

Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation

In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysi…

Creazione di un plug-in per il software open-source di visualizzazione di immagini DICOM OsiriX per lo studio delle variazioni dimensionali e densitometriche di metastasi epatiche in TCMS

Presentiamo la nostra esperienza riguardo la progettazione e l'utilizzo di un plug-in per il software open-source di visualizzazione di immagini DICOM (OsiriX 32-bit; GNU General Public License) per la valutazione dei criteri dimensionali e densitometrici di metastasi epatiche in TCMS.

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

A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by…

Web Directories as a Knowledge Base to Build a Multi-Agent System for Information Sharing

. A neural based multi-agent system, exploiting the Web Directories as a Knowledge Base for information sharing and documents retrieval, is presented. The system is based on the E?Net architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve, among documents shared by a networked community, documents satisfying a query and dealing with a specific topic. The system is composed by four agents: the Trainer Agent, the Neural Classifier Agent, the Interface Agent, and the Librarian Agent. The sub-symbolic knowledge of the Neural Classifier Agent is automatically updated each time a new, n…

Multimodal and Agent-Based Human–Computer Interaction in Cultural Heritage Applications: an Overview

One of the most recent and interesting applications of human–computer interaction technologies is the provision of advanced information services within public places, such as cultural heritage sites or schools and university campuses. In such contexts, concurrent technologies used in smart mobile devices can be used to satisfy the mobility need of users allowing them to access relevant resources in a context-dependent manner. Of course, most of the constraints to be taken into account when designing a pervasive information providing system are given by the actual domain where they are deployed.

An Osirix based plug-in for the study of dimensional and densitometric changes of hepatic metastases on CT images

Aims and objectives Methods and materials Results Conclusion Personal information References

CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

STIMA DEL POTENZIALE ELETTRICO IN tDCS CON APPROCCIO MESHLESS INNOVATIVO

Transcranial DC stimulation (transcranial Direct Current Stimulation, tDCS) is a non-invasive technique aimed at modifying neuronal activity for the purpose therapeutic and / or for the improvement of mental performance. A continuous current of entity modest (below the threshold of perception) is injected into the brain via electrodes placed on the scalp surface to produce changes in long-term cortical activity. Despite the increasing use of this and other similar techniques, and the relevant ones applications - for example in the field of neuropsychological rehabilitation - their impact on neuronal activity is not yet fully known, mainly due to the difficulty of predict the spatial distrib…

An Embedded Real-Time Lane-Keeper for Automatic Vehicle Driving

Automatic vehicle driving involves several issues, such as the capability to follow the road and keep the right lane, to maintain the distance between vehicles, to regulate vehiclepsilas speed, to find the shortest route to a destination. In this paper a real-time automatic lane-keeper is proposed. The main features of the system are the lane markers location process as well as the computation of the vehiclepsilas steering lock. The above techniques require high elaboration speed to execute, check and complete an operation before a prearranged time. Clearly if system processing exceeds the deadline, the whole operation became meaningless or, in the meantime, the vehicle can reach a critical…

A multimodal retina-iris biometric system using the Levenshtein distance for spatial feature comparison

Abstract The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and…

ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI

Breast cancer is the most common malignancy that threatening women’s health. Although Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) for breast lesions characterization is widely used in the clinical practice, physician grading performance is still not optimal, showing a specificity of about 72%. In this work Radiomics was used to analyze a dataset acquired with two different protocols in order to train Machine-Learning algorithms for breast cancer classification. Original radiomic features were expanded considering Laplacian of Gaussian filtering and Wavelet Transform images to evaluate whether they can improve predictive performance. A Multi-Instant features selection invo…

ConformalALU: A Conformal Geometric Algebra Coprocessor for Medical Image Processing

Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform …

Daily Peak Temperature Forecasting with Elman Neural Networks

This work presents a forecaster based on an Elman artificial neural network trained with resilient backpropagation algorithm for predicting the daily peak temperatures one day ahead. The available time series was recorded at Petrosino (TP), in the west coast of Sicily, Italy and it is composed by temperature (min and max values), the humidity (min and max values) and the rainfall value between January 1st, 1995 and May 14th, 2003. Performances and reliabilities of the proposed model were evaluated by a number of measures, comparing different neural models. Experimental results show very good prediction performances.

Multi-modal biometric authentication systems

The main goal of a biometric system is to discriminate automatically subjects in a reliable and dependable way, accordingly to a specific target application. The discrimination is based on one or more types of information derived from physical or behavioural traits, such as fingerprint, face, iris, voice, hand, or signature. Applications of biometrics range from homeland security and border control to e-commerce and e-banking, including secure networking and authentication. Traditionally, biometric systems working on a single biometric feature, have many limitations, such as, trouble with data sensors, where captured sensor data are often affected by noise, distinctiveness ability, because …

A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier

Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancem…

CliifoSor: a Parallel embedded architecture for Geometric Algebra and Computer Graphics

BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture

Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault…

A Neural Solution for a Mobile Robot Navigation into Unknown Indoor Environments Using Visual Landmarks

In this paper we present a neural solution for a mobile robot navigation into unknown indoor environments by using landmarks. Robot navigation task is implemented by two groups of processes based on MLP neural networks classifiers: a Low Level Vision System performs obstacle avoidance and corridor following, while an High Level Vision System extracts landmarks contents and performs goal directed navigation. A path-planner manages the two navigation systems and interacts with the robot hardware. The proposed solution is very strong and flexible and can be used to drive a mobile robot in real indoor environments. In the paper experimental results are also reported.

Reti metaboliche: studio e simulazione delle variazioni dei parametri strutturali al variare della temperatura

In questo lavoro, si sono analizzate le correlazioni tra la temperatura e i parametri strutturali delle reti metaboliche. L’obiettivo che si è posto, è stato quello di determinare con quali leggi matematiche variano i parametri strutturali delle reti metaboliche, rispetto alla temperatura, e alla forte pressione dei vincoli selettivi a cui essi sono sottoposti. Al fine di comprendere meglio l’analisi di questo obiettivo, dal punto di vista analitico, si sono mostrate l’equazioni che regolano i parametri strutturali delle reti e si sono mostrati i grafici che descrivono gli andamenti dei vari parametri strutturali delle reti metaboliche al variare della temperatura. Successivamente, dopo ave…

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…

A New Directional Morphological Filter in a Automated Fingerprint Identification System

CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability …

Metabolic Response Assessment in Non-Small Cell Lung Cancer Patients after Platinum-Based Therapy: A Preliminary Analysis

The purpose of this study was to evaluate the clinical value of PET (Positron Emission Tomography) for early prediction of tumor response to platinum-based therapy in patients with nonsmall cell lung cancer (NSCLC). The evaluation was carried out comparing the standard treatment response using RECIST (Response Evaluation Criteria in Solid Tumors) with metabolic treatment response according to European Organization for Research and Treatment of Cancer (EORTC) recommendations, PET Response Criteria in Solid Tumors (PERCIST), Total Lesion Glycolysis (TLG) and Metabolic Tumor Volume (MTV). Seventeen inoperable patients with stage IV NSCLC were enrolled between October 2011 and June 2013: PET st…

An Optimized Architecture for CGA Operations and Its Application to a Simulated Robotic Arm

Conformal geometric algebra (CGA) is a new geometric computation tool that is attracting growing attention in many research fields, such as computer graphics, robotics, and computer vision. Regarding the robotic applications, new approaches based on CGA have been proposed to efficiently solve problems as the inverse kinematics and grasping of a robotic arm. The hardware acceleration of CGA operations is required to meet real-time performance requirements in embedded robotic platforms. In this paper, we present a novel embedded coprocessor for accelerating CGA operations in robotic tasks. Two robotic algorithms, namely, inverse kinematics and grasping of a human-arm-like kinematics chain, ar…

Medical Data Processing and Analysis for Remote Health and Activities Monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human’s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in thi…

Design and Implementation of an Efficient Fingerprint Features Extractor

Biometric recognition systems are rapidly evolving technologies and their use in embedded devices for accessing and managing data and resources is a very challenging issue. Usually, they are composed of three main modules: Acquisition, Features Extraction and Matching. In this paper the hardware design and implementation of an efficient fingerprint features extractor for embedded devices is described. The proposed architecture, designed for different acquisition sensors, is composed of four blocks: Image Pre-processor, Macro-Features Extractor, Micro- Features Extractor and Master Controller. The Image Pre- processor block increases the quality level of the input raw image and performs an a…

A Family of Embedded Coprocessors with Native Geometric Algebra Support

Clifford Algebra or Geometric Algebra (GA) is a simple and intuitive way to model geometric objects and their transformations. Operating in high-dimensional vector spaces with significant computational costs, the practical use of GA requires, however, dedicated software and/or hardware architectures to directly support Clifford data types and operators. In this paper, a family of embedded coprocessors for the native execution of GA operations is presented. The paper shows the evolution of the coprocessor family focusing on the latest two architectures that offer direct hardware support to up to five-dimensional Clifford operations. The proposed coprocessors exploit hardware-oriented represe…

Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors

BACKGROUND: Incisional hernia is one of the most common complications after abdominal surgery with an incidence rate of 11 to 20% post laparotomy. Many different factors can be considered as risk factors of incisional hernia recurrence. The aim of this study is to confirm and to validate the incisional hernia recurrence risk factors and to identify and to validate new ones. METHODS: In the period from July 2007 to July 2017, 154 patients were selected and subjected to incisional hernia repair. The surgical operations were conducted under general anaesthesia. Patients received antibiotic prophylaxis when indicated, according to the hospital prophylaxis scheme. Inclusion criteria of the study…

Fuzzy techniques for access and data management in home automation environments

Home Automation Environments are characterized by the integration of electronic devices as well as by the performance of communication and control systems. Environment infrastructure has to meet several requirements including Quality of Service (QoS), safety, security, and energy saving. However, Home Automation deals with complex environments, so that advanced data management systems are required to meet the above constraints. Fuzzy Logic based techniques can be successful used to improve system performance management. This work proposes and describes the use and application of fuzzy rules on a two-tiered architecture integrating a biometric authentication module and communication real-tim…

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

An Embedded Fingerprints Classification System based on Weightless Neural Networks

Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases in Automatic Fingerprint Identification Systems (AFISs). The paper presents a new fast fingerprint classification module implementing on embedded Weightless Neural Network (RAM-based neural network). The proposed WNN architecture uses directional maps to classify fingerprint images in the five NIST classes (Left Loop, Right Loop, Whorl, Arch and Tented Arch) without anyone enhancement phase. Starting from the directional map, the WNN architecture computes the fingerprint classification rate. The proposed architecture is implemented on Celoxica RC20…

A New Embedded Coprocessor for Clifford Algebra based Software Intensive Systems

Computer graphics applications require efficient tools to model geometric objects and their transformations. Clifford algebra (also known as geometric algebra) is receiving a growing attention in many research fields, such as computer graphics, machine vision and robotics, as a new, interesting computational paradigm that offers a natural and intuitive way to perform geometric calculations. At the same time, compute-intensive graphics algorithms require the execution of million Clifford operations. Clifford algebra based software intensive systems need therefore the support of specialized hardware architectures capable of accelerating Clifford operations execution. In this paper the archite…

GUI Usability in Medical Imaging

The diffusion of computer technologies in everyday life has involved the birth of standard methodologies to control their development. Indeed, the purpose of standardization procedures consists of providing rules aimed to control technologies leaving no space for empirical improvisations. In general, medical software manufacturers provide their applications with Graphic User Interfaces (GUI) that are not compliant with any clear and standard usability criterion. The only guideline is the creation of GUIs inherited from the ones adopted on medical consoles because physicians use them routinely. This paper addresses this issue: medical software interfaces should be designed trying to overcome…

A fully automatic method for biological target volume segmentation of brain metastases

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of…

Visceral Adiposity Index

OBJECTIVE To individuate a novel sex-specific index, based on waist circumference, BMI, triglycerides, and HDL cholesterol, indirectly expressing visceral fat function. RESEARCH DESIGN AND METHODS Visceral adiposity index (VAI) was first modeled on 315 nonobese healthy subjects. Using two multiple logistic regression models, VAI was retrospectively validated in 1,498 primary care patients in comparison to classical cardio- and cerebrovascular risk factors. RESULTS All components of metabolic syndrome increased significantly across VAI quintiles. VAI was independently associated with both cardiovascular (odd ratio [OR] 2.45; 95% CI 1.52–3.95; P < 0.001) and cerebrovascular (1.63; 1.0…

Gastrointestinal Stromal Tumors: Diagnosis, Follow-up and Role of Radiomics in a Single Center Experience

: Gastrointestinal stromal tumors (GISTs) arise from the interstitial cells of Cajal in the gastrointestinal tract and are the most common intestinal tumors. Usually GISTs are asymptomatic, especially small tumors that may not cause any symptoms and may be found accidentally on abdominal CT scans. Discovering of inhibitor of receptor tyrosine kinases has changed the outcome of patients with high-risk GISTs. This paper will focus on the role of imaging in diagnosis, characterization and follow-up. We shall also report our local experience in radiomics evaluation of GISTs.

SEMI-AUTOMATIC VOLUMETRIC SEGMENTATION OF THE UPPER AIRWAYS IN PATIENTS WITH PIERRE ROBIN SEQUENCE

Pierre Robin malformation is a rare craniofacial dysmorphism whose pathogenesis is multifactorial. Although there is some agreement in non-invasive treatment in less severe cases, the dispute is still open on cases with severe respiratory impairment. We present a semi-automatic novel diagnostic tool for calculating upper airway volume, in order to eventually address surgery in patients with Pierre Robin Sequence (PRS). Multidetector CT datasets of two patients and two controls were tested to assess the proposed method for ROI segmentation, upper airway volume computation and three-dimensional reconstructions. The experimental results show an irregular pattern and a severely reduced cross-s…

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

Urban Daily Peak-Forecasting NOx Using Recurrent Neural Network

Visceral Adiposity Index (VAI): A Reliable Indicator Of Visceral Fat Function Associated With Cardiometabolic Risk.

Objective: To individuate a novel sex-specific index, based on Waist Circumference (WC), Body Mass Index (BMI), Triglycerides (TG) and HDL cholesterol (HDL), indirectly expressing visceral fat function. Research design and Methods: Visceral Adiposity Index (VAI) was first modelled on 315 non-obese healthy subjects. Using two multiple logistic regression models, VAI was retrospectively validated in 1,498 primary care patients in comparison to classical cardio and cerebrovascular risk factors. Results: All components of metabolic syndrome increased significantly across VAI quintiles. VAI was independently associated with both cardiovascular (OR:2.45; 95%CI: 1.52-3.95; p<0.001) and cerebrov…

A Computational Study on Temperature Variations in MRgFUS Treatments Using PRF Thermometry Techniques and Optical Probes

Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasounds are currently emerging in the clinical practice as a noninvasive technology for therapy. Indeed, the sound waves can be used to increase the temperature inside the target solid tumors, leading to apoptosis or necrosis of neoplastic tissues. The Magnetic resonance-guided focused ultrasound surgery (MRgFUS) technology represents a valid application of this ultrasound property, mainly used in oncology and neurology. In this paper

A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study

Target volume delineation of Positron Emission Tomography (PET) images in radiation treatment planning is challenging because of the low spatial resolution and high noise level in PET data. The aim of this work is the devel- opment of an accurate and fast method for semi-automatic segmentation of me- tabolic regions on PET images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Va- lidation was first performed on phantoms containing spheres and irregular in- serts of different and known volumes, then tumors from a patient with head and neck cancer were segmented to discuss the clinical applicability of this algo- rith…

A Self-Contained Biometric Sensor for Ubiquitous Authentication

This paper describes a real-life behavior framework in simulation game based on Probabilistic State Machine (PSM) with Gaussian random distribution. According to the dynamic environment information, NPC can generate behavior planning autonomously associated with defined FSM. After planning process, we illuminate Gaussian probabilistic function for real-life action simulation in time and spatial domains. The expected value of distribution is estimated during behavior planning process and variance is determined by NPC personality in order to realize real life behavior simulation. We experiment the framework and Gaussian PSM on a restaurant simulation game. Furthermore we give some suggestions…

An Embedded Real-Time Automatic Lane-Keeping System

Advanced Driver Assistance Systems (ADAS) are safety systems capable of identifying an unavoidable dangerous scenario and reacting coherently. An automatic lane-keeping system is designed to prevent dangerous events when the driver left inadvertently his/her own lane. In this paper a real-time automatic lane-keeping system is proposed. The main features of the system are the lane markers location process as well as the generation of the vehicle's steering angle. The system has been prototyped using the Celoxica RC203 board, equipped with a Xilinx Virtex II FPGA, for real-time processing of real motorway scenes coming from a CCD camera on a moving vehicle. The required processing time is 25,…

A real-time network architecture for biometric data delivery in Ambient Intelligence

Ambient Intelligent applications involve the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs (Ambi- ent Intelligence-AmI). Biometrics plays a key role in surveillance and security applications. Fingerprint, iris and voice/speech traits can be acquired by contact, contact-less, and at-a-distance sensors embedded in the environment. Biometric traits transmission and delivery is very critical and it needs real-time transmission net- work with guaranteed performance and QoS. Wireless networks become suitable for AmI if they are able to satisfy real-time communication and security system requi…

Fingerprint in User Authentication Process and Agent Ownership

Fingerprints and Smartcards in Embedded Authentication Systems

Quantificazione del tessuto adiposo epicardico in Calcium Score e Cardio-TC: confronto tra valori di attenuazione, spessore e volumi.

The aim of the study is to compare the characteristics of epiphonic adipose tissue (TAE) studied using Calcium Score (CS) and Cardio-CT (CTC).

Smart Wireless Sensor Networks and Biometric Authentication for Real Time Traffic Light Junctions Management

The main challenge of intelligent transportation systems (ITS) is to deal with ‘real-time’ information to improve vehicular traffic management. Road data can be processed and used for dynamic traffic light management in order to reduce waiting times in queues. This paper proposes an innovative distributed architecture based on a wireless sensor network (WSN) with a network coordinator providing remote and ubiquitous authentication module for managing unexpected events. The architecture is completed by a dynamic module for street priority management depending on traffic rate. Many experimental trials have been carried out considering three different levels of traffic intensity to prove the e…

An enhanced random walk algorithm for delineation of head and neck cancers in PET studies

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…

GAPP Compiler for Hardware Accelerated Geometric Algebra Computing

Because of the high numeric complexity of Geometric Algebra, its use in engineering applications relies heavily on tools for ecient implementations. In this article, we introduce a new quality of Geometric Algebra Computing solutions based on a new compiler for Geometric Algebra Parallelism Programs (GAPP). These programs are already optimized in a sense that only the really needed computations are left. The GAPP compiler is able to generate two output formats leading to advanced hardware accelerated Geometric Algebra Computing. On one hand, there is the direct generation of HSAIL code, in order to more eciently support the solutions of the broad range of heterogeneous computing architectur…

A neural network based automatic road signs recognizer

Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the…

A MAS Security Framework Implementing Reputation Based Policies and Owners Access Control

Multi-agent systems expose users to risks related to lack of knowledge above interacting users. Such systems should provide tools to protect their own resources from illegal accesses by unauthorized users. This paper describes a security framework for Multi-agent systems preventing a trusted agent to interact with malicious agents and granting agent and platform resources. This feature is obtained adding an access control mechanism that joins the benefits of Credential Based Access Control, Role Based Access Control and Mandatory Access Control. Authorizations and access control policies are set by XML based policy files. A case study on a distributed document retrieval system is also illus…

Biometric Authentication Technologies

Visceral Adiposity Index (VAI) as a simple indicator of “adipose tissue dysfunction” in patients with type 2 diabetes.

Although still there is no a clear definition of “adipose tissue dysfunction”, the identification of clinical and biological markers of altered fat distribution and function may provide the needed tools to early identify a condition of cardiometabolic risk. Visceral Adiposity Index (VAI) is a mathematical gender-specific index estimated with the use of simple anthropometric [(BMI and Waist circumference (WC)] and biochemical parameters [HDL cholesterol (HDL) and Triglycerides (Tg)], that in recent studies has shown to reflect accurately the degree of visceral adiposity and insulin resistance. However, although VAI has been indirectly shown to be a marker of impaired fat distribution and fun…

A novel solution based on scale invariant feature transform descriptors and deep learning for the detection of suspicious regions in mammogram images.

Background: Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. Methods: We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature …

A Bio-Inspired Cognitive Agent for Autonomous Urban Vehicles Routing Optimization

Autonomous urban vehicle prototypes are expected to be efficient even in not explicitly planned circumstances and dynamic environments. The development of autonomous vehicles for urban driving needs real-time information from vehicles and road network to optimize traffic flows. In traffic agent-based models, each vehicle is an agent, while the road network is the environment. Cognitive agents are able to reason on the perceived data, to evaluate the information obtained by reasoning, and to learn and respond, preserving their self-sufficiency, independency, self-determination, and self-reliance. In this paper, a bio-inspired cognitive agent for autonomous urban vehicles routing optimization…

An automatic method for metabolic evaluation of gamma knife treatments

Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

An Embedded Module for Iris Micro-Characteristics Extraction

In this paper a new approach, based on iris micro-characteristics, has been used to make possible an embedded biometric extractor. This recognition approach is based on ophthalmologic studies that have proven the existence of different micro-characteristics as well as fingerprint minutiae. These micro-characteristics are permanent and immutable and they can be used to create strong and robust identification systems.Biometric recognition systems are critical components of our everyday lives. Since such electronic products evolve to software intensive systems, where software, becoming larger, more complex and prevalent, introduces many problems in the development phases. The development of em…

Daily Urban NOx Peak Forecasting Using Recurrent Neural Network

A fast fusion technique for fingerprint and iris spatial descriptors in multimodal biometric systems

Multimodal biometric identification systems aim to combine two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR). In this paper a new fusion technique for fingerprint and iris spatial descriptors with a low execution time is presented. The report focuses on spatial fusion strategies, offering and proposing a modern perspective on multi-biometrics. In greater detail, a spatial-based approach and a homogeneous biometric vector, integrating iris and fingerprint data, are generated, fused, and processed for the overall system matching score. The goal of this approach is to demonstrate that, by using spatial iris features followin…

Machine Learning for Early Diagnosis of ATTRv Amyloidosis in Non-Endemic Areas: A Multicenter Study from Italy

Background: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv) is an adult-onset multisystemic disease, affecting the peripheral nerves, heart, gastrointestinal tract, eyes, and kidneys. Nowadays, several treatment options are available; thus, avoiding misdiagnosis is crucial to starting therapy in early disease stages. However, clinical diagnosis may be difficult, as the disease may present with unspecific symptoms and signs. We hypothesize that the diagnostic process may benefit from the use of machine learning (ML). Methods: 397 patients referring to neuromuscular clinics in 4 centers from the south of Italy with neuropathy and at least 1 more red flag, as well as undergoin…

Multimodal and Agent-Based Human–Computer Interaction in Cultural Heritage Applications: an Overview

Evaluation of Platinum-based Therapy Response in Non-Small Cell Lung Cancer

Aim: To evaluate the clinical value of PET imaging for an early prediction of tumor response to platinum-based therapy in patients with non-small cell lung cancer (NSCLC). In order to avoid unnecessary toxicity of ineffective chemotherapy treatment, an early identification of NSCLC patients who benefit from this therapy is mandatory. Materials and methods: Seventeen patients are enrolled prospectively: 18F-FDG-PET examinations are carried out before treatment and after the first course. The lesions with the highest uptake in each patient are evaluated according to EORTC, PERCIST and RECIST classifications to discriminate between patients who respond (complete and partial response) from thos…

Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …

A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation

PurposeMagnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. MethodTo address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means cl…

Embedded access points for trusted data and resources access in HPC systems

Biometric authentication systems represent a valid alternative to the conventional username-password based approach for user authentication. However, authentication systems composed of a biometric reader, a smartcard reader, and a networked workstation which perform user authentication via software algorithms have been found to be vulnerable in two areas: firstly in their communication channels between readers and workstation (communication attacks) and secondly through their processing algorithms and/or matching results overriding (replay attacks, confidentiality and integrity threats related to the stored information of the networked workstation). In this paper, a full hardware access poi…

An Intelligent Sensor for Fingerprint Recognition

In this paper an intelligent sensor for fingerprint recognition is proposed. The sensor has the objective to overcome some limits of the fingerprint recognition software systems, as elaboration time and security issues related to fingerprint transmission between sensor and processing unit. Intelligent sensor has been prototyped using the Hamster Secugen sensor for image acquisition and the Celoxica RC1000 board, employing a Xilinx VirtexE2000 FPGA, for image processing and analysis. Resources used, elaboration time as well the recognition rates in both verification and identification modes are reported in the paper. To the best of our knowledge, this is the first implementation for a full h…

Recent Advances in Mobile and Multimedia Applications

An Embedded Processor for Metabolic Networks Optimization

In recent years biological processes modelling and simulation have become two key issues in analyzing complex cellular systems. The computational requirements suggest to investigate alternative solutions to the common supercomputers and clusters in order to optimize and overcome computational bottleneck. The goal of this work is the design and the realization of an embedded processor for metabolic networks optimization in order to examine their behaviour and robustness under malfunctions of one or more nodes. The embedded processor has been prototyped on the Celoxica RC203E board, equipped with programmable FPGA technologies. A case studied outlining the E. Coli bacteria metabolic network i…

Novel Human-to-Human Interactions from the Evolution of HCI

The interaction ways made available by the evolution of the human-computer interfaces, led to novel Human-to-Human Interaction (HHI) modes, enabling people to cooperate for almost any task any time and any where. HHI nowadays is largely indirect and mediated by a wide variety of technologies and devices. This new and exciting field of design originates from the convergence of a few well-established research fields within the HCI area, such as traditional Graphical User Interfaces (GUI), Tangible User Interfaces (TUI), Touchless Gesture User Interface (TGUI), Voice User Interfaces (VUI), and Brain Computer Interfaces (BCI). We analyze and describe the evolution of the HCI in those fields, an…

MultiSlice human organ extraction based on GVF

Segmentation techniques based on active contours algorithm are widely used in medical imaging. Unfortunately, they require a lot of parameters to be used and this can rep- resent an issue for those physicians with not much informatics skills. This paper proposes a software tool which allows to segment multiple slice can differ organ extraction setting a small number of parameters. Moreover, the tool offers the functionality to perform a multiple segmentation the same time, so that an entire volume composed by hundreds slices can be segmented.

Referenceless thermometry using radial basis function interpolation

The Proton Resonance Frequency (PRF) shift provide a method for temperature change measurements during thermotherapy. Conventional PRF thermometry works subtracting one or multiple baseline images. The method leads to artifacts caused by tissue motion and frequency drift. Various works estimating the background phase from each acquired image phase are present in literature. These algorithms are called “referenceless” because they don’t require any subtraction of baseline images for calculating temperature increment. Conventional referenceless methods estimate baseline image by fitting the background phase outside the heated region through a polynomial approach. In this work a background pha…

Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique

MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife (R) is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy. The target to be treated with radiation beams is currently contoured through slice-by-slice manual segmentation on MR images. This procedure is time consuming and operator-dependent. Segmentation result repeatability may be ensured only by using automatic/semi-automatic methods with the clinicians supporting the planning phase. In this paper a semi-automatic segmentation method, based on an unsuperv…

Automatic Volumetric Liver Segmentation Using Texture Based Region Growing

In this paper an automatic texture based volumetric region growing method for liver segmentation is proposed. 3D seeded region growing is based on texture features with the automatic selection of the seed voxel inside the liver organ and the automatic threshold value computation for the region growing stop condition. Co-occurrence 3D texture features are extracted from CT abdominal volumes and the seeded region growing algorithm is based on statistics in the features space. Each CT volume is composed by 230 slices, having 512 x 512 pixels as spatial resolution, and 12-bit gray level resolution. In this initial feasible study, 5 healthy volunteer acquisitions has been used. Tests have been p…

A novel automated tool for calculating upper airways volume in patients with isolated Pierre Robin Sequence (IPRS).

Purpose Methods and Materials Results Conclusion References Personal Information

A Novel Iris Recognition System based on Microfeature

Fixed-size Quadruples for a New, Hardware-Oriented Representation of the 4D Clifford Algebra

Clifford algebra (geometric algebra) offers a natural and intuitive way to model geometry in fields as robotics, machine vision and computer graphics. This paper proposes a new representation based on fixed-size elements (quadruples) of 4D Clifford algebra and demonstrates that this choice leads to an algorithmic simplification which in turn leads to a simpler and more compact hardware implementation of the algebraic operations. In order to prove the advantages of the new, quadruple-based representation over the classical representation based on homogeneous elements, a coprocessing core supporting the new fixed-size Clifford operands, namely Quad-CliffoSor (Quadruple-based Clifford coproces…

Multi-Platform Agent Systems with Dynamic Reputation Policy Management

Open, distributed multi-platform agent systems require new management approaches for resources and data secure access. In this paper a Jade-S based multi-platform agent system implementing dynamic reputation policy management is proposed. The implemented extension deals with biometrics, X-Security, DES cryptography and agent reputation. With more details, the proposed reputation management system helps to assess the agent's behavior and reliability, in order to select trusted agents. This is made possible by the knowledge that agents are able to acquire, over time, and that allows them to choose the best solution using own intelligence in total autonomy.

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

Feature Dimensionality Reduction for Mammographic Report Classification

The amount and the variety of available medical data coming from multiple and heterogeneous sources can inhibit analysis, manual interpretation, and use of simple data management applications. In this paper a deep overview of the principal algorithms for dimensionality reduction is carried out; moreover, the most effective techniques are applied on a dataset composed of 4461 mammographic reports is presented. The most useful medical terms are converted and represented using a TF-IDF matrix, in order to enable data mining and retrieval tasks. A series of query have been performed on the raw matrix and on the same matrix after the dimensionality reduction obtained using the most useful techni…

A neural multi-agent based system for smart html pages retrieval

A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically …

A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

The basic aim of a biometric identification system is to discriminate automatically between subjects in a reliable and dependable way, according to a specific-target application. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In this paper, an innovative multimodal biometric identification system based on iris and fingerprint traits is proposed. The paper is a state-of-the-art advancement of multibiometrics, offering an innovative perspective on features fusion. In greater detail, a frequency-based approach result…

Towards human cell simulation

The faithful reproduction and accurate prediction of the phe-notypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extr…

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…

Biometrics authentication and reputation based policies in a mas security framework

Fuzzy Fusion in Multimodal Biometric Systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy…

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms,…

Fuzzy Fusion in Multimodal Biometric Systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy…

Radial Basis Function Interpolation for Referenceless Thermometry Enhancement

MRgFUS (Magnetic Resonance guided Focused UltraSound) is a new and non-invasive technique to treat different diseases in the oncology field, that uses Focused Ultrasound (FUS) to induce necrosis in the lesion. Temperature change measurements during ultrasound thermo-therapies can be performed through magnetic resonance monitoring by using Proton Resonance Frequency (PRF) thermometry. It measures the phase variation resulting from the temperature-dependent changes in resonance frequency by subtracting one phase baseline image from actual phase. Referenceless thermometry aims to re-duce artefacts caused by tissue motion and frequency drift, fitting the back-ground phase outside the heated reg…

A Concurrent Neural Classifier for HTML Documents Retrieval

A neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the EαNet architecture, a neural network having good generalization capabilities and able to learn the activation function of its hidden units. The starting hypothesis is that the HTML pages are stored in networked repositories. The system goal is to retrieve documents satisfying a user query and belonging to a given class (i.e. documents containing the word “football” and talking about “Sports”). The system is composed by three interacting agents: the EαNet Neural Classifier Mobile Agent, the Query Agent, and the Locator Agent. The whole system was successfully implemented exploiting t…

A Semi-automatic Multi-seed Region-Growing Approach for Uterine Fibroids Segmentation in MRgFUS Treatment

Fibroids are benign tumors growing in the uterus. Most of fibroids do not require treatment unless they are causing symptoms. Traditional surgery treatments, like myomectomy and hysterectomy, are very invasive therapeutic approaches which not always preserves reproductive potential of the woman. MRgFUS, performed with Insightec ExAblate 2100 equipment, is a new and noninvasive technique for uterine fibroids treatment, not requiring hospitalization and recovery time for patients. An initial assessment of MRgFUS treatment is made by computing the ablated volume of uterine fibroid. In this paper a semi-automatic approach, based on region-growing segmentation technique, is proposed. The impleme…

Biometric sensors rapid prototyping on field-programmable gate arrays

AbstractBiometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong an…

Design and implementation of an embedded coprocessor with native support for 5D, quadruple-based Clifford algebra

Geometric or Clifford algebra (CA) is a powerful mathematical tool that offers a natural and intuitive way to model geometric facts in a number of research fields, such as robotics, machine vision, and computer graphics. Operating in higher dimensional spaces, its practical use is hindered, however, by a significant computational cost, only partially addressed by dedicated software libraries and hardware/software codesigns. For low-dimensional algebras, several dedicated hardware accelerators and coprocessing architectures have been already proposed in the literature. This paper introduces the architecture of CliffordALU5, an embedded coprocessing core conceived for native execution of up t…

Bio-inspired security analysis for IoT scenarios

Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building…

Message from CISIS 2013 Workshops Co-Chairs

Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm

Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…

NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Stereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and gro…

A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management a…

Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays: An Experimental Case Study

Both computational performances and energy efficiency are required for the development of any mobile or embedded information processing system. The Internet of Things (IoT) is the latest evolution of these systems, paving the way for advancements in ubiquitous computing. In a context in which a large amount of data is often analyzed and processed, it is mandatory to adapt node logic and processing capabilities with respect to the available energy resources. This paper investigates under which conditions a partially reconfigurable hardware accelerator can provide energy saving in complex processing tasks. The paper also presents a useful analysis of how the dynamic partial reconfiguration te…

An embedded iris recognizer for portable and mobile devices

Software-intensive systems play an increasingly dominant role in our lives and daily activities. Several applications, in which a timely response to user and environment stimulus is essential, require real-time software intensive systems. Computation-intensive applications, such as video compression, control systems, security systems, result in significant growth for processor workload. To address the above issues, one possible solution is to design embedded specialized components. At the same time, the integration of new features in portable and mobile devices is rapidly increasing. Several services and applications require robust user authentication for access to services, data, and resou…

A real-time non-intrusive FPGA-based drowsiness detection system

Automotive has gained several benefits from the Ambient Intelligent researches involving the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs. One of the main topics in automotive is to anticipate driver needs and safety, in terms of preventing critical and dangerous events. Considering the high number of caused accidents, one of the most relevant dangerous events affecting driver and passengers safety is driver’s drowsiness and hypovigilance. This paper presents a low-intrusive, real-time driver’s drowsiness detection system for common vehicles. The proposed system exploits the ‘‘bright p…

CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease

AbstractThis study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alon…

A graph-based method for biological target volume segmentation

APPLICATION OF AN AUTOMATIC COMPUTERIZED ALGORITHM FOR THE ANALYSIS OF UPPER AIRWAYS REDUCTION IN PATIENTS WITH PIERRE ROBIN SEQUENCE STUDIED BY MDCT

Efficient FPGA Implementation of a Knowledge-Based Automatic Speech Classifier

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general far lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as dete…

Semi-Automated evaluation of small bowel mural attenuation at CT enterography using different temporal windows in patients affected by active Crohn disease

Disfunzione del tessuto adiposo e diabete mellito tipo 2: scarsa utilità diagnostica della quantificazione del grasso tramite RMN.

An edge-driven 3D region-growing approach for upper airway morphology and volume evaluation in patients with Pierre Robin sequence

Abstract: Pierre Robin sequence (PRS) is a pathological condition responsible for a sequence of clinical events, such as breathing and feeding difficulties, that must be addressed to give the patient at least a chance to survive. By using medical imaging techniques, in a non-intrusive way, the surgeon has the opportunity to obtain 3D views, reconstruction of the regions of interest (ROIs), useful to increase understanding of the PRS patient’s condition. In this paper, a semi-automatic approach for segmentation of the upper airways is proposed. The implemented approach uses an edge-driven 3D region-growing algorithm to segment ROIs and 3D volume-rendering technique to reconstruct the 3D mode…

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