1,721,051 research outputs found
Ultrasound Image Despeckling Based on Statistical Similarity
No full textUltrasound 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
Unsupervised Coastal Line Extraction From SAR Images
No full textHistorically, 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
Cyclostationary analysis for heart rate variability
No full textBackground: During the last years, cyclostationarity has emerged as a new approach for the analysis of a certain type of non-stationary signals. This theoretical tool allows us to identify periodicity in signals which cannot be identified easily but also to separate useful signals for other interfering contributions that overlap in the spectral support. Objective: The aim of this work is the exploitation of cyclostationary theory to enhance standard methodologies for the study of heart rate variability. In this framework, a preliminary analysis on healthy patients is proposed to be extended further on pathological patients with the perspective to improve (hopefully) the diagnostic power of some cardiac dysfunctions due to the more complete set of information provided by this analysis. Methods: The proposed approach involves an initial band-pass filtering step in the range 0.5 - 40 Hz of the recorded ECG signal, followed by a first-order derivative filter to reduce the effects of P and T waves and to emphasise the QRS contribution. After that, the autocorrelation function is evaluated and the Cyclic Power Spectrum (CPS) is computed. From this two-dimensional information, a onedimensional plot is derived via the evaluation of a folded-projected CPS to be compared with standard Lomb-Scargle spectrum. Results: The proposed analysis has been tested on both numerical simulations as well as for the processing of real data which are available online in the Physionet database. Conclusion: The proposed cyclostationary analysis has shown a good agreement with the results provided by the classical Lomb-Scargle spectrum in the processing of real data, underlining some contributions in the high-frequency bandwidth which are not visible by means of standard processing
Modified Dixon technique for MRI water-fat separation using jointly amplitude and phase
No full textBackground: Three Point Dixon technique is a methodology able to separate water and fat components within magnetic resonance images, exploiting the phase of the complex data. It works exploiting the phase differences between three MRI images acquired with different Echo Times. Within the procedure, the most problematic step is the so called phase unwrapping operation.
Methods: We propose a new approach for unwrapping the acquired image phases. The presented technique works directly on each single complex image instead of phase differences, allowing reconstructions with high accuracy. The methodology jointly exploits the amplitude and the phase information. For assuring high computational efficiency and fast convergence to the global optimal solution, a graph cuts based optimization approach has been implemented.
Results: The proposed technique has been first applied to a phantom simulating a magnetic resonance image of the human head. Subsequently, the algorithm has been tested on a real data set consisting of three head images acquired in axial position. In both cases, results have been compared with the ones obtained using classical unregularized version of three point Dixon technique.
Conclusions: A novel, computationally fast and effective algorithm for phase unwrapping in three point Dixon water and fat separation is presented. The methodology provides regularized fat and water component estimation with a higher accuracy compared to the classical approach
Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images
No full textIn 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
A Bayesian Approach for Relaxation Times Estimation in MRI
No full textRelaxation time estimation in MRI field could help clinical diagnosis. T1 and T2 changes can be related to tissues modification, being an effective tool for detecting the presence of several pathologies and measure their development, thus their estimation is a useful research field. Currently, most techniques work pixel-wise, and transfer the noise reduction task to post processing filters. A novel method for estimating spin-spin and spin-lattice relaxation times is proposed. The approach exploits Markov Random Field theory for modeling the unknown data and implements an a posteriori estimator in the Bayesian framework. The effect is the joint parameters estimation and noise reduction. Proposed methodology, with respect to already existing techniques is able to provide effective results while preserving details also in case of few acquisition or severe signal to noise ratio. The algorithm has been tested on both simulated and real datasets
A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times
Get PDFBrain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets
Statistical Edge Detection in Urban Areas Exploiting SAR Complex Data
No full textThe 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
Statistical approaches for multichannel phase unwrapping
No full textSynthetic Aperture Radar Interferometry allows the generation of Digital Elevation Model of an observed scene exploiting the phase signal. In order to provide the 3D reconstruction, a phase unwrapping procedure is required, which is an ill-posed problem. Multichannel datasets are able to solve the ambiguity providing a global solution. Within this manuscript two recently proposed statistical multichannel phase unwrapping methods are considered and compared. The first one is developed in the Bayesian-Markovian framework, while the second one is based on Kalman filtering. Results and comparisons on a simulated data set are reported, showing interesting results
- …
