In this work, a novel estimator of proton density and relaxation times in Magnetic Resonance Imaging is presented. The evaluation of the signal model together with the statistical distribution of the noise have allowed the development of the Minimum Variance Unbiased (MVU) Estimator which, in case of complex decomposition of MRI data, corresponds to a Least Square (LS) one. With respect to amplitude base Maximum Likelihood Estimator, the proposed approach shows many advantages, including an higher accuracy due to the complex model and lower computational time due to the simple function. Results of real data are reported, showing the effectiveness of the method.
In this paper, a new technique for speckle noise reduction in ultra-sound images is presented. The proposed technique adds to the classical Wiener filter the possibility of taking into account, in the restoration process, the spatial correlation between image pixels. In particular, the technique is able to locally adapt the noise filtering intensity in order to combine good edges and details preservation with effective noise reduction. The method is characterized by low memory requirements and computational burden, allowing quasi real time applications. Results on a realistic simulated dataset are reported, together with a comparison with other largely adopted filtering techniques, allowing a quantitatively evaluation of the proposed approach.
In last years the interest in transportation security has increased significantly, in particular with respect to road transport safety of dangerous goods. Today, there is a growing attention to automotive sensors monitoring systems, in order to make them an effective and valuable aid in situations of danger, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in poor weather conditions (such as fog, rain) and in presence of smoke. This limitation can be overcome by using radar sensors. In particular, imaging radar are gaining interest in the framework of Driver Assistance Systems (DAS). Radar monitoring system can be effectively used for the safety dangerous goods transportation. At present most of radar focusing techniques are not able to discriminate multiple targets on the same line of sight. In this paper we propose a novel radar signal processing technique, based on Compressive Sensing (CS) theory, to perform the detection of two or more targets on the same line of sight, greatly improving the performances of a radar DAS. After a brief description of the proposed methodology, case studies are presented in order to evaluate the performances of the technique.
Ultrasound images are affected by the speckle phenomenon, a multiplicative noise that degrades image quality. Several methods for denoising have been proposed in recent years, based on different approaches. The so-called non-local mean is considered the state-of-the-art method; the idea is to find similar patches across the image and exploit them to regularize the image. The method proposed here is in the non-local family, although instead of partitioning the target image in patches, it works pixelwise. The similarity between pixels is evaluated by analyzing their statistical behavior, in particular, by measuring the Kolmogorov-Smirnov distance between their distributions. To make this possible, a stack of acquired images is required. The proposed method has been tested on both simulated and real data sets and compared with other widely adopted techniques. Performance is interesting, with quality parameters and visual inspection confirming such findings.
A novel approach for noise reduction in Magnetic Resonance Image field is proposed. The methodology adopts a Maximum A Posteriori estimator and exploits Markov Random Field theory for adapting the filter to the local nature of the image. Differently from other widely adopted filters, the proposed algorithm works in the complex domain, i.e., real and imaginary components of the acquired images are jointly processed and regularized. First results on a clinical dataset are reported, showing the interesting performances of the methodology.
The advancement of new promising techniques in the field of biomedical imaging has always been paramount for the research community. Recently, ultrasound tomography has proved to be a good candidate for non-invasive and safe diagnostics. In particular, breast cancer imaging may benefit from this approach, as frequent screening and early diagnosis require decreased system size and costs compared to conventional imaging techniques. Furthermore, a major advantage of these approaches consists in the operator-independent feature, which is very desirable compared to conventional hand-held ultrasound imaging. In this framework, the authors present some imaging results on an experimental campaign acquired via an in-house ultrasound tomographic system designed and built at the University of Naples Parthenope. Imaging performance was evaluated via different tests, showing good potentiality in structural information retrieval. This study represents a first proof of concept in order to validate the system and to consider further realistic cases in near future applications.
Abstract The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.
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.
In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.
