Breast cancer represents one of the most impactful diseases for both human health and healthcare system. The use of cutting-edge and innovative technology solutions is paramount to allow a proper and early diagnosis, which also would mitigate its impact on the society. In this framework, the proposed work explore the use of safe, non-ioninzing microwave radiation with dense deep learning models for the detection and localisation of breast tumor, providing spatial information to support medical decision and personalised medicine. After a screening step for the identification of malignant breasts, a tumor spatial probability map is estimated by means of fully -connected deep learning models. To this aim, an in-house, two-dimensional MRI-derived breast database consisting of 160,000 profiles was adopted, and the localisation performance was quantified by adopting proper quality metrics. The obtained results confirm the potentialities that deep learning and microwave technology can have as emerging solutions in the healthcare system for improving the quality of patients' life.

Magnetoencephalography represents one of the main state-of-the-art methods for brain functional analysis. It is a non-invasive device where a helmet equipped with sensors is located around the scalp in order to measure magnetic fields originated from the electrical brain activity. In many studies magnetoencephalography signals are exploited to map the brain function in order to identify sources locations and reconstruct their temporal signals. This is possible through the resolution of an inverse problem that passes from the recorded signals to the brain activity. Several reconstruction algorithms have been proposed and exploited in literature but, due to the inherent ill-posedness of the inverse problem, they offer good temporal resolution but a limited spatial resolution. Many clinical applications require precise localization of pathological tissues to provide reliable information for neurologists. Within this manuscript, an alternative inversion algorithm is proposed in order to estimate a more refined identification of source locations. The proposed one is a deep learning algorithm named deep-MEG. It demonstrates to overcome other state-of-the-art reconstruction approaches being effective in precisely locating the active brain area and in reconstructing its temporal evolution with a good level of accuracy, even in a challenging noisy scenario. Moreover, deep-Megcan reconstruct active areas belonging to the whole brain and it is not limited to cortical sources.

This paper introduces a new system for non-contact hand movement sensing, utilizing ultrasound sensors with millimeter precision. Acting akin to a sonar, the system detects pressure waves reflected from the moving hand. Due to the high spatio/temporal resolution of the system, the proposed solution has been tested for finger tapping monitoring. Finger tapping serves as a key clinical test for diagnosing neurodegenerative diseases, such as Parkinson's disease, where symptoms like bradykinesia, rigidity, and tremor are focal points of investigation. Currently, clinicians visually assess bradykinesia by observing subjects' finger tapping gestures, rating the severity qualitatively. The idea is to provide a quantitative assessment of the tapping providing a support for diagnosis. In detail, through subsequent time/frequency analysis and specialized processing, tapping peaks are identified, enabling evaluation of tapping stability by measuring interpeak distances. This feasibility study presents the prototype and initial measurement findings.

Biometrics showed their usefulness in several applications, systems with fingerprint or face identification are well accepted in several fields with possibly different constraints like, for example, forensic or smartphone unlocking. In this paper, a novel ultrasound prototype for person identification and authentication has been designed, built and tested. The system, which acts as a sonar, measures the pressure wave backscattered from a moving hand. Such signal is subsequently processed by means of time/frequency analysis and a deep learning detector is implemented in order to identify/authenticate the user based on some peculiarity in the gestures execution. The proposed solution is cheap and, due to the high adaptability, allows different security levels. Promising results are obtained after tests carried out with the help of 10 volunteers.

The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metrics are essential. However, the application of phased-based metrics for measuring brain connectivity has proved problematic so far, since they appear to be less resilient to noise as compared to amplitude-based ones. In this paper, we address the problem of designing a purely phase-based brain connectivity metric, insensitive to volume conduction and resilient to noise. The proposed metric, named phase linearity measurement (PLM), is based on the analysis of similar behaviors in the phases of the recorded signals. The PLM is tested in two simulated datasets as well as in real MEG data acquired at the Naples MEG center. Due to its intrinsic characteristics, the PLM shows considerable noise rejection properties as compared to other widely adopted connectivity metrics. We conclude that the PLM might be valuable in order to allow better estimation of phase-based brain connectivity.

Automatic Target Recognition (ATR) aims at detecting the presence and at recognizing the typology and the orientation of targets within a scenario, by using an unsupervised approach. In Syntethic Aperture Radar imaging this turns to be a difficult task due to the specific characteristics of clutter and background noise. Within this manuscript a new two-steps ATR algorithm based on Kolmogorov-Smirnov test is presented. The method has been tested on real MSTAR datasets showing interesting performances.

One of the main challenges in Interferometric Synthetic Aperture Radar (SAR) is the accurate height reconstruction of the observed scene. Recently, approaches based on Extended Kalman Filter (EKF) have been proposed. Most of them are based on the hypothesis of height profile continuity. Such condition greatly reduces their applicability, being only valid for particular scenarios. Within this paper, we present a novel Kalman-based height reconstruction approach, specifically designed to work with multichannel data related to any type of scenario, both smooth or sharp. The novelty of the technique consists in its ability in detecting and correctly handling sharp height discontinuities while regularizing smooth areas. The approach is able to maintain the high computational efficiency typical of EKF and to work in an almost unsupervised way. The methodology has been tested and validated on both simulated and real X-band (TerraSAR-X and COSMO-SkyMed) high-resolution data sets. Reported results are encouraging and interesting, showing the correctness and the validity of the proposed approach.

A novel approach for speckle reduction in SAR images is presented. An enhanced version of the Wiener Filter is proposed in order to locally adapt the filter characteristic to the image behavior, modelled by Markov Random Fields. First results on simulated and real data are reported.

Within this manuscript a novel Multi-channel InSAR phase unwrapping method is proposed. The approach implements an Extended Kalman Filter for jointly unwrap the phase and regularize the result. The novelty of the methodology consists in the probabilistic data association step that has been implemented in order to improve the robustness of EKF for PhU. Encouraging results on simulated dataset are reported.

In this paper the problem of despeckling Synthetic Aperture Radar images is addressed. An algorithm developed in the Bayesian estimation theory framework is presented. In particular, considering single look images, the algorithm applies an homomorphic filter followed by an Iterative Wiener filter to reduce the speckle. The proposed approach is tested on simulated and real X-band datasets showing interesting noise reduction capabilities.

Phase Unwrapping (PhU) is the operation needed in order to generate 3-Dimensional height profile starting from Synthetic Aperture Radar (SAR) data acquired in the interferometric configuration. Due to the presence of height discontinuities (buildings) and due to the presence of noise, PhU becomes a difficult task to face in urban scenarios. In this paper we propose a new methodology especially thought for generating 3D height profiles of urban areas when multiple interferograms are available. The technique is based on the use of Kalman Filter in its Extended form (Extended Kalman Filter - EKF). The main peculiarity of the technique is the introduction of a specific step in the PhU procedure able to identify the possible height discontinuities and to consequently adapt the EKF behaviour. The algorithm is validated on both simulated and real cases.

A novel Phase Unwrapping (PhU) technique for InSAR interferometric stacks based on statistical estimation theory is presented. The approach is intended for exploiting both amplitude and phase of the acquired data in order to express the multi-baseline likelihood function without the assumption of independence among channels, i.e. by considering the full mutual correlation matrix. Moreover, the contextual information is adopted for regularizing the solution, obtaining a Maximum A Posteriori (MAP) estimator. First results on real datasets related to an urban scenario are presented, showing the interesting performances of the proposed method in terms of Digital Elevation Model (DEM) reconstruction.

Within this manuscript a novel technique for joint Digital Elevation Model (DEM) reconstruction and deformation estimation is presented. In particular, a Maximum A Posteriori (MAP) estimator that makes use of Gaussian Markov Random Fields (MRF) is proposed. The advantage of the approach, with respect to classical Permanent Scatterers (PS) based techniques, consists of its ability to evaluate the height and the deformation for all resolution cell across the scene, instead of only strong scatterers. Thus, the method is able to work also in natural scenarios, or in general when few PS are available. First results are presented on a simulated dataset with COSMO-SkyMed acquisition parameters.

In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of using the amplitude image, as in classical change detection approaches, the proposed technique uses the full complex image in a Markovian framework. The complex data are modeled using Markov random field hyperparameters, which are particular local parameters that take into account the spatial correlation between pixels. Starting from two data sets, the pre- and the postevent ones, the proposed algorithm, first, estimates the two hyperparameter maps and, then, compares the similarity between them. If a change occurs between the pre- and the postevent acquisitions, the statistical distribution of the hyperparameter maps will change. The maximum distance between the two obtained statistical distributions provides an index of changes. This sort of spatial correlation maps is computed using statistical estimation techniques, while the similarity comparison is computed using the two-step Kolmogorov-Smirnov statistic test. The algorithm is validated on simulated data and tested on real COSMO-SkyMed data acquired on the area of Naples, showing interesting and promising results.

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method.

In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.

Within this manuscript, a method able to extract lake shore from SAR complex data is proposed. Radar images have many advantages over optical images, such as no cloud coverage or solar illumination problem. The method models the observed scene as a Local Gaussian Markov Random Field and estimates in a Bayesian framework the so called hyperparameters. By proper thresholding such hyperparameters we can retrieve the lake shores. Exploiting SAR data from today available high resolution sensors, we can monitor lake shores with sub-meter precision. In the following, applications to two real data sets, showing the effectiveness of the method, are reported.

Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.

In this work an edge detector for man made structures within Spotlight Synthetic Aperture Radar (SAR) Images is proposed. The algorithm processes both real and imaginary parts of the data and so it is able to fully exploit the acquired image, being optimal from the information theory point of view. The detector has been tested on Cosmo-SKYMED (CSK) Spotlight image and compared to other single image edge detectors, showing interesting and promising results.

Markov Random Fields (MRF) are a powerful and wide adopted tool in image processing. MRFS together with Bayesian Estimation Theory can be used for detecting edges of man made structures in Synthetic Aperture Radar images. In this paper we present a method developed in the Markovian-Bayesian framework which is particularly suited to work in case of high coherence SAR interferometric images pairs, such as TanDEM-X Mission data. A real case study, consisting of a pair of complex TerraSAR-X images, is presented, showing the performances of the algorithm.

The aim of building edge detection is to obtain a map of man-made structure edges of the investigated scene. Different detectors have been developed exploiting synthetic aperture radar (SAR) data, based on the use of the reflectivity difference (working with SAR amplitude images) or of the phase difference (working with SAR interferometric images) between neighboring pixels. In this letter, a novel approach using jointly both the amplitudes and the interferometric phase of two complex SAR images is proposed, based on the hypothesis that information related to building edges can be retrieved in the two data domains. The technique is based on stochastic estimation theory, exploiting, in particular, Markov random fields. Compared to classical amplitude-based edge detectors and to phase-based ones, the proposed method shows an improvement in terms of detection accuracy, false alarm rate, and building shape recovery. The algorithm has been tested and analyzed using simulated data and validated on L-band and X-band real data sets.

Interferometric synthetic aperture radar (SAR) (InSAR) systems allow 3-D reconstruction of observed scene. In this paper, an innovative approach for phase unwrapping and digital elevation model (DEM) generation using multichannel InSAR data is presented. The proposed algorithm, exploiting both the amplitude and phase of the available complex data, is able to unwrap and simultaneously regularize the observed data. In particular, the exploitation of amplitude data within the unwrapping chain helps in preserving sharp discontinuities typical of urban areas. As a result, the technique provides accurate DEM reconstructions. For this aim, a Markovian approach, together with a new graph-cut-based optimization algorithm, has been considered. The method has been developed specifically to work in urban areas with very high resolution InSAR image stacks, being able to automatically compensate possible phase offsets. Results on both simulated and real case studies are reported, showing the effectiveness of the method.

In this work, a novel building edge detector which jointly exploits the full complex SAR image is proposed. The algorithm, starting from both amplitude and phase signals, detects building edges by estimating the spatial correlation between neighboring pixels. The observed scene is modeled using Markov Random Field theory. The proposed procedure is able to correctly identify the building and recover their shapes while maintaining a low false alarm rate, overcoming the limitation of existing techniques.

In this paper a comparison between two techniques developed to recover layover solution in SAR images is presented. SAR Statistical Tomography and Compressive Sensing techniques are described and analyzed in order to provide a set of instruments for 3D SAR imaging able to tackle different scattering mechanisms in layover areas and to recover height reconstruction of an observed scene. The performances of the two techniques are compared on simulated data and some conclusions are drawn.

In this paper a methodology for the reconstruction of height profile of earth surface starting from layover affected Synthetic Apertuire Radar data is presented. The proposed approach is based on classical statistical estimation techniques, in particular using Maximum Likelihood Estimator, together with a Gaussian model for the point target response. Multi-channel configuration has been exploited in order to solve the solution ambiguity and to increase the reconstruction accuracy. The performances of the proposed estimator have been evaluated in comparison with the Cramer Rao Lower Bounds for the considered model, showing the effectiveness of the method. The height reconstruction procedure has been tested on a simulated realistic scenario, providing interesting and promising results.

In this letter, a statistical-based approach to recover layover solution in synthetic aperture radar (SAR) images is proposed. The aim of this letter is to develop a methodology in order to separate different scattering contributions collapsed in a single SAR image pixel. After a brief discussion about layover, the proposed model is presented, followed by a discussion about achievable performances using Cramer-Rao lower bounds. In the final part of this letter, the performances of a maximum likelihood estimator are evaluated in a simulated data scenario, showing the effectiveness of the method.

In this letter, a methodology to overcome the layover problem and obtain the 3-D reconstruction of urban areas will be discussed. Interferometric synthetic aperture radar (SAR) (InSAR) systems allow the estimation of height profiles of the Earth surface, but in the case of urban scenarios, estimation becomes a hard task due to the presence of SAR geometrical distortions, with layover above all. First, the layover signal in InSAR images is investigated; then, a procedure to specifically manage layover areas is presented. The proposed method consists of introducing an auxiliary data exploitation, optical data or SAR shadowing, in the maximum a posteriori statistical estimation technique to improve the digital elevation model reconstruction, particularly on phase discontinuities. We test the method on simulated data, showing its effectiveness.

Interferometric SAR (InSAR) systems are able to estimate height profiles of the Earth surface. For the involved estimation problem, Maximum A Posteriori (MAP) statistical technique and Markov Random Field image models have been used, showing to be effective in case of multiple interferograms, obtained via different baselines/frequencies. In this paper, we are interested in the application of such estimation procedure in urban areas, where due to SAR geometry, many geometrical distortions appear. In particular, we focus on the layover problem. We present a procedure to manage layover areas, based on the optical and SAR data fusion. Moreover, we exploit the optical data to improve the a priori term of the MAP approach. We test the method on simulated data, showing the effectiveness of the method.