Carmelo Militello

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 results in a homogeneous biometric vector, integrating iris and fingerprint data. Successively, a hamming-distance-based matching algorithm deals with the unified homogenous biometric vector. The proposed multimodal system achieves interesting results with several commonly used databases. For example, we have obtained an interesting working point with FAR = 0% and FRR = 5.71% using the entire fingerprint verification competition (FVC) 2002 DB2B database and a randomly extracted same-size subset of the BATH database. At the same time, considering the BATH database and the FVC2002 DB2A database, we have obtained a further interesting working point with FAR = 0% and FRR = 7.28% ÷ 9.7%.

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 storing past behaviors of agent/user. Biometric owner agents authentication, agent/users policies to regulate agents behavior and actions, and agent/users reputation level to select trusted agents can be used to overcome the above limits and enhance the level of security for these applications. In this paper an extended JADE-S based framework for developing secure Multi-Agent Systems is proposed. The framework functionalities are extended by self-contained FPGA biometric sensors providing secure and fast user authentication service. Each agent owner, by means of biometric authentication, acquires his/her own X.509v3 digital certificate. Policy files and a flexible, fast distributed Access Control Mechanism can regulate behavior and actions of any users/agent inside the platform. In addition, a mechanism based on the agent reputation is used: reputation is an attribute associated to each owner and/or agent on the basis of its past behavior and integrity. In order to prove the feasibility of the proposed framework, we have developed a multi-agent e-Banking system. System goal deals with e-Banking services such as bank account statements, account transactions and so on. In the paper, the experimental features of the biometric self-contained sensors are also outlined.

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, fingerprint processing involves few steps and, in the same way, fingerprint matching involves the comparison of few features with recognition rates comparable to the standard minutiae based systems. The experiments trials have been conducted on many official Fingerprint Verification Competition (FVC) databases. The achieved results show the effectiveness of the proposed approach, obtaining a False Acceptance Rate (FAR) = 1.22% and a False Rejection Rate (FRR) = 9.23% with FVC2002 DB2-A database. In the best of case, a FAR=0.26% and a FRR=7.36% with FVC2000 DB1-B database is achieved. To the best of our knowledge, this is the first recognition system based only on singularity regions.

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 based recognition systems. The whole system has been prototyped on the Celoxica RC203E board, equipped with a Xilinx VirtexII FPGA. The prototype has been tested with the FVC databases. Hardware system performance show high recognition rates and low execution time: FAR = 1.2% and FRR=2.6%, while each fingerprint elaboration requires 34.82 ms. Considering both FAR, FRR indexes and the high speed, the proposed system could be used for identification tasks in large fingerprint databases. To the best of our knowledge, this is the first authentication system based on singularity points.

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 and personal ubiquitous authentication. The multimodal technique uses both fingerprint micro features (minutiae) and fingerprint macro features (singularity points) for robust user authentication. To strengthen the security level of electronic payment systems, an embedded hardware prototype has been also created: acting as self-contained sensors, it performs the entire authentication process on the same device, so that all critical information (e.g. biometric data, account transactions and cryptographic keys), are managed and stored inside the sensor, without any data transmission. The sensor has been prototyped using the Celoxica RC203E board, achieving fast execution time, low working frequency, and good recognition performance.

A Self-Contained Biometric Sensor for Ubiquitous Authentication

Development and diffusion of embedded systems, directly connected to communication technologies, move people towards the era of ubiquitous computing. An ubiquitous environment needs of many self-contained authentication sensors, opportunely distributed, for users recognition and their secure access. In this work the study and the implementation of a fingerprints-based embedded biometric system for personal ubiquitous authentication is proposed. The system is a self-contained sensor since it is able to perform fingerprint acquisition and processing for user authentication, to strengthen security: the processor performs the entire elaboration steps on board, so that all critical information (i.e. biometric data and cryptographic keys), are securely managed and stored inside the sensor, without any data leaking out. Sensor has been realized on a FPGA-based platform achieving fast execution time and a good final throughput. Resources used, elaboration times and recognition rates of the sensor are finally reported.

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

BACKGROUND AND OBJECTIVES Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. METHODS A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. 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 also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. RESULTS The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearmans rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTVMRI enhanced the CTV more accurately than BTV in 25% of cases. CONCLUSIONS The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTVMRI and GTV should be considered for a comprehensive treatment planning.

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 implemented approach gives a quantitative and qualitative evaluation of the treatment providing the volume and the three-dimensional (3D) model of the treated fibroid area. Considering these characteristics, the proposed approach can be used as a tool to integrate the information used by a Medical Decision Support System (MDSS). As step in the MRgFUS treatment evaluation, the achieved results improve the current methodology based on the manual uterine fibroid ROT segmentation.

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® 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 unsupervised Fuzzy C-Means clustering technique, is proposed. The presented approach allows for the target segmentation and its volume calculation. Segmentation tests on 5 MRI series were performed, using both area-based and distance-based metrics. The following average values have been obtained: DS = 95.10, JC = 90.82, TPF = 95.86, FNF = 2.18, MAD = 0.302, MAXD = 1.260, H = 1.636.

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 planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C‐Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area‐based and distance‐based metrics, obtaining the following average values: Similarity Index = 95.59%, Jaccard Index = 91.86%, Sensitivity = 97.39%, Specificity = 94.30%, Mean Absolute Distance = 0.246[pixels], Maximum Distance = 1.050[pixels], and Hausdorff Distance = 1.365[pixels].