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…

Low-velocity impact behaviour of green epoxy biocomposite laminates reinforced by sisal fibers

Abstract Due to its good mechanical characteristics, low cost and high availability in the current market, sisal fiber is one of the most used for the manufacturing of biocomposites in various industrial fields (automotive, marine, civil construction etc.). The particular sub-fibrillar structure of the sisal fiber (similar to aramid fibers) and the corresponding anisotropic behavior detected by recent research activities, suggest that such biocomposites should exhibit also high impact strength, in such a way to permit their advantageously use also for the manufacturing of crashworthy components (bumpers, helmets, protection systems etc.), that are at the same time also eco-friendly, lightwe…

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…

SVILUPPO DI BIOCOMPOSITI AD ELEVATE PERFORMANCE RINFORZATI CON FIBRE DI AGAVE

Fingerprint and Smartcards in Embedded Authentication Systems

First lamina hybridization of high performance CFRP with Kevlar fibers: Effect on impact behavior and nondestructive evaluation

The impact behavior of a carbon-Kevlar hybrid composite, widely used in sport car manufacturing, was evaluated. To highlight the hybridization effect, comparative analyses were performed with the basic CFRP laminate having the same lay-up. Tensile, bending and low velocity impact tests, followed by nondestructive inspections, highlighted that Kevlar first lamina hybridization leads to an increment in specific impact strength, up to 55%. To assess the most reliable technique to detect the impact damage, nondestructive evaluation was performed by pulsed thermography, phased array ultrasonic technique, computed tomography and digital radiography. Phased array ultrasonic technique can be consid…

A Self-Contained Biometric Sensor for Ubiquitous Autentication

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…

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…

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, …

ANALISI DEL COMPORTAMENTO MECCANICO DI SANDWICHES GREEN COSTITUITI DA PELLI IN BIOCOMPOSITO E CORE IN BALSA/SUGHERO

Negli ultimi anni l’aumento della sensibilità nei confronti della salvaguardia ambientale, anche attraverso apposite norme in materia di lotta all’inquinamento e di riciclo dei materiali a fine vita, ha portato ad un notevole interesse dei ricercatori verso i cosiddetti compositi green, materiali costituiti in genere da rinforzi di origine naturale e matrici “bio-based” a basso impatto ambientale. Una categoria interessante di materiali innovativi, caratterizzati da una elevata resistenza a flessione unita ad un basso peso specifico, è rappresentata dai cosiddetti sandwiches, costituiti in genere da robuste pelli in composito fibro-rinforzato e da un core in materiale molto leggero e poco r…

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

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…

On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (…

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…

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…

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…

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…

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…

Biological and Mechanical Characterization of the Random Positioning Machine (RPM) for Microgravity Simulations

The rapid improvement of space technologies is leading to the continuous increase of space missions that will soon bring humans back to the Moon and, in the coming future, toward longer interplanetary missions such as the one to Mars. The idea of living in space is charming and fascinating; however, the space environment is a harsh place to host human life and exposes the crew to many physical challenges. The absence of gravity experienced in space affects many aspects of human biology and can be reproduced in vitro with the help of microgravity simulators. Simulated microgravity (s-μg) is applied in many fields of research, ranging from cell biology to physics, including cancer biology. In…

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

New concept in bioderived composites: Biochar as toughening agent for improving performances and durability of agave-based epoxy biocomposites

Biocomposites are increasingly used in the industry for the replacement of synthetic materials, thanks to their good mechanical properties, being lightweight, and having low cost. Unfortunately, in several potential fields of structural application their static strength and fatigue life are not high enough. For this reason, several chemical treatments on the fibers have been proposed in literature, although still without fully satisfactory results. To overcome this drawback, in this study we present a procedure based on the addition of a carbonaceous filler to a green epoxy matrix reinforced by Agave sisalana fibers. Among all carbon-based materials, biochar was selected for its environment…

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…

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…

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…

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…

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 …

Influence of the anisotropy of sisal fibers on the mechanical properties of high performance unidirectional biocomposite lamina and micromechanical models

Abstract High performance biocomposites reinforced by sisal fibers, are between the most promising materials that could be used in various fields, from automotive to civil constructions, thanks to their good mechanical performance, as well as to the low cost and the great availability of the fiber. Nevertheless, at present their practical use is prevented by the limited knowledge of their mechanical performance. The results of the present study have shown that the intimate fibrillar structure of the sisal fiber is associated with a high anisotropy involving not only the elastic parameters, but also the damage processes with typical fiber splitting phenomena, that influence noticeably the bi…

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 …

Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering

Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…

Environmental aging effects on high-performance biocomposites reinforced by sisal fibers

Among the innovative materials, an important role is played by the so-called biocomposites, generally made by an eco-friendly matrix reinforced with natural fibers. Unfortunately, due to the degradability of the green matrixes as well as to hydrophilicity of the natural fibers, the resistance of such innovative materials to the environmental agents is, in general, relativity low, and it can significantly limit their use in outdoor conditions. To contribute to the knowledge of the effects of the leading environmental agents on the mechanical properties of highperformance biocomposites made of a green epoxy matrix reinforced by agave fibers, a systematic experimental testing campaign has been…

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…

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…

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

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, th…

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…

PROVE DI INVECCHIAMENTO SU BIOCOMPOSITI EPOSSIDICI AD ELEVATE PERFORMANCE RINFORZATI CON FIBRE DI AGAVE

La riduzione dell'impatto ambientale nella moderna produzione di materiali compositi a matrice polimerica ha attratto una significativa attività di ricerca finalizzata alla messa a punto di nuovi materiali compositi “green” caratterizzati altresì da una apprezzabile riduzione dei costi e del peso specifico. Tra questi materiali giocano un ruolo importante i cosiddetti biocompositi, materiali costituiti da matrici a basso impatto ambientale o rinnovabili, rinforzate con fibre naturali. Nel presente lavoro, attraverso una sistematica campagna di prove sperimentali sono analizzati gli effetti dei principali agenti ambientali sulle proprietà meccaniche di biocompositi ad elevate prestazioni cos…

Fingerprints and Smartcards in Embedded Authentication Systems

Biometric Authentication Technologies

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …

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…

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 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…

Basalt Fiber Hybridization Effects on High-Performance Sisal-Reinforced Biocomposites

The increasing attention given to environmental protection, largely through specific regulations on environmental impact and the recycling of materials, has led to a considerable interest of researchers in biocomposites, materials consisting of bio-based or green polymer matrixes reinforced by natural fibers. Among the various reinforcing natural fibers, sisal fibers are particularly promising for their good mechanical properties, low specific weight and wide availability on the current market. As proven in literature by various authors, the hybridization of biocomposites by synthetical fibers or different natural fibers can lead to an interesting improvement of the mechanical properties or…

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…

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…

Mode I translaminar fracture toughness of high performance laminated biocomposites reinforced by sisal fibers: Accurate measurement approach and lay-up effects

Abstract The present work performs a systematic experimental analysis of the translaminar fracture behavior of high performance biocomposites constituted by green epoxy reinforced by sisal fibers, by varying the main influence parameters as fiber concentration and lay-up. Despite the corrective function properly introduced to take into account the anisotropy as well as the use of the equivalent crack length, the study shows that the LEFM does not give accurate estimations of the fracture toughness, because the extension of the near tip damaged zone is higher than the singular dominated one. Accurate estimations can be obtained instead by the proposed modified area method that takes into acc…

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 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…

Analysis of the Parameters Affecting the Stiffness of Short Sisal Fiber Biocomposites Manufactured by Compression-Molding

The use of natural fiber-based composites is on the rise in many industries. Thanks to their eco-sustainability, these innovative materials make it possible to adapt the production of components, systems and machines to the increasingly stringent regulations on environmental protection, while at the same time reducing production costs, weight and operating costs. Optimizing the mechanical properties of biocomposites is an important goal of applied research. In this work, using a new numerical approach, the effects of the volume fraction, average length, distribution of orientation and curvature of fibers on the Young’s modulus of a biocomposite reinforced with short natural fibers were stud…

IBRIDIZZAZIONE SUPERFICIALE MEDIANTE BASALTO DI BIOCOMPOSITI AD ELEVATE PERFORMANCE RINFORZATI CON FIBRE DI AGAVE

Una delle principali limitazioni nell’uso pratico dei biocompositi polimerici rinforzati con fibre naturali (lino, canapa, agave, ecc.), specie in applicazioni di tipo “outdoor”, è costituita dalla bassa resistenza all’invecchiamento prodotto dagli agenti ambientali (UV, umidita, acqua ecc.), caratteristica questa in genere strettamente derivante dalla elevata idrofilia delle fibre naturali e dalla limitata protezione offerta in tal senso dalle matrici polimeriche, specie se di tipo termoindurente. Con riferimento ai biocompositi ad elevate performance rinforzati con fibre di agave, materiali già messi a punto in letteratura e pure ampiamente caratterizzati nelle loro interessanti proprietà…

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…

GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unf…

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…

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…