Global overlapping block based reconstruction using exact Legendre moments

This paper present an improved reconstruction algorithm of the multi-gray level images based on overlapping block method using exact continuous moments computation: Legendre , Zernike, Pseudo-Zernike and Gegenbauer moments . We solve the artifact issue caused by unitary block reconstruction which affects the visual image quality. This method aim to ensure high accuracy and low computation time, using only small finite number of moments. Our approaches aims to introduce these moments in the field of data compression and local feature extraction for pattern recognition. Experimental results show the superiority of our proposed approaches over the existing methods.

Magnetic particle imaging (MPI) is a relatively new method to image the spatial distribution of magnetic nanoparticle (MNP) tracers administered to the body with high spatial and temporal resolution using an inhomogeneous magnetic field. The spatial information of the MNP's is encoded using a field free point (FFP), or a field free line (FFL), in which the magnetic field vanishes at a point, or on a line, respectively. FFL scanning has the advantage of improved sensitivity compared to FFP scanning as a result of higher signal-to-noise ratio. The trajectory traversed by the FFL or FFP is an important parameter of the MPI system and should be selected to achieve the best imaging quality in minimum scan time, while considering hardware constraints and patient safety. In this study, we analyzed the image quality of different FFL trajectories for a large field of view (FOV) using simulations, to provide a baseline information for FFL scanning MPI system design. We simulated a human-sized FFL scanning MPI configuration to image a circular FOV with 160mm diameter, and compared Radial, Spiral, Uniform Spiral, Flower, and Lissajous trajectories with different trajectory densities scanned by the FFL for constant scan time. We analyzed the system matrices of the trajectories in terms of mutual coherence and homogeneity of the spatial sensitivity. We calculated the maximum electric fields induced on a homogeneous conductive body by the selection field (SF) and the focus field (FF) to compare the trajectories based on the nerve stimulation threshold. The images were obtained using the system matrix reconstruction approach with two different image reconstruction methods. In the first one, we used the conventional image reconstruction method, algebraic reconstruction technique (ART), which gives a regularized least-squares solution. In the second one, we used the state-of-the-art alternating direction method of multipliers (ADMM), which minimizes a weighted sum of the l1 -norm and the total variation (TV) of the images. The Radial and Spiral trajectories resulted in a poor imaging performance at low trajectory densities due to relatively high coherency and poor sensitivity of the measurements, respectively. For ART reconstruction, the highest image quality with the lowest trajectory density was achieved with the Uniform Spiral trajectory. Uniform Spiral, Flower, and Lissajous trajectories yielded comparable performance with ADMM reconstruction. The rotating SF induced higher electric field amplitude compared to the FF. Consequently, maximum allowable gradient at the same trajectory density was greater for the Radial trajectory compared to the other trajectories. For a large FOV coverage, the Uniform Spiral trajectory offers a good compromise between image quality and imaging time, taking safety and hardware limitations into account. The Radial trajectory, especially using l1 -norm and TV priors in the reconstruction, may be favorable in case the SF induced electric field is higher than that of the FF at the same frequency (e.g., relatively small FOV coverage). In general, ADMM reconstruction resulted in higher contrast and resolution compared to ART, leading to lighter requirements on the density of the trajectory.

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.

Microwave Imaging (MI) provides a low-cost and safe approach to the non-invasive diagnosis of biological structures. However, solving the associated Inverse Scattering Problem (ISP) in a timely manner poses several challenges. To jointly achieve high reconstruction accuracy and low computation time, an imaging strategy based on the System-by-Design (SbD) paradigm is presented. The proposed SbD approach can deal with ISPs with an inhomogeneous background and is capable of exploiting a-priori information on the electromagnetic (EM) characteristics of the biological structure under investigation. Preliminary results based on a realistic brain phantom show that a remarkable reconstruction accuracy is achieved together with a significant reduction in terms of computation times as compared to competitive state-of-the-art approaches.

Aviation promotes trade and tourism by connecting regions, people, and countries. Having a functional and efficient airport pavement network is important to improve aviation traffic and to provide safer mobility to almost 800 million passengers travelling in the U.S. per year. The Federal Aviation Administration has initiated and actively been participating in many projects to further advance pavement design and performance to meet user requirements. To accomplish that, quantitative data are needed; such data may be collected from the pavement response to gear and environment loading. In this study, responses from four instrumented taxiway concrete slabs at John F. Kennedy International Airport were analyzed. The collected data were used to develop machine-learning (ML) based prediction models to compute the temperature, curling and bending strains within pavement. The ML models were developed using the support vector machine (SVM) algorithm. The results showed that SVM based ML models can predict pavement responses with a high accuracy and low computation time. Furthermore, in the case of feeding more data from various airports, ML models have proven to be a promising technique for pavement analysis engine for future airport pavement design frameworks. This study also produces recommendations for future data collection projects to have well-designed databases for data-driven models development.

Efficient Legendre moment computation for grey level images

We propose a fast calculation to characterise images by Legendre moments. We first propose the recursive calculation of 1D Legendre moments. The fast algorithm is then generalized to 2D cases. With our algorithm, the computational complexity to calculate the Legendre moments is much reduced, and it is independent of the window size.

This paper presents a fast and novel method of image reconstruction using Adaptive R-tree based segmentation and Linear Bivariate Splines. We have used our own Significant Pixel Selection Algorithm [8] to represent the strong edges which are then stored in an adaptive R-tree to enhance and improve image reconstruction. The image set can be encapsulated in a bounding box which contains the connected parts of the edges found using edge-detection techniques. Image reconstruction is done based on the approximation of image regarded as a function, by a linear spline over adapted Delaunay triangulation. The proposed method is compared with some of the existing image reconstructions.

Discrete Chebyshev moments (due to discrete polynomial basis) do not have the discretization errors that continuous-domain Legendre and Zernike moments contain. Calculation of polynomial basis coefficients of discrete moments is generally performed using recurrence relationships. Such recurrence equations cause numerical error accumulation especially for calculation of higher-order moments and for larger image sizes, causing significant degradation of image reconstruction from these moments. A method for better image reconstruction from high orders of discrete Chebyshev moments is demonstrated. This is accomplished by calculating Chebyshev polynomial coefficients directly from their definition formulas using arbitrary precision arithmetic and by forming lookup tables from these coefficients.

An Efficient Method for Computation of Legendre Moments

This is the first report on a new fast statistical iterative reconstruction algorithm for conebeam with a circular source trajectory, accelerated by InstaRecon's fast <i>O</i>(<i>N³</i>log<i>N</i>) hierarchical cone beam backprojection1 and reprojection algorithms. We report on the results of image quality and run-time comparisons with iterative algorithms based on conventional backprojection and reprojection. We demonstrate that the iterative algorithm introduced here can provide image quality indistinguishable from an iterative algorithm using conventional BP/RP operators, while providing almost a 10x speedup in reconstruction rates. Combining the 10x algorithmic acceleration with additional hardware acceleration by FPGA, Cell, or GPU implementation, this work indicates the feasibility of iterative reconstruction algorithms for dose reduction and image quality improvement in routine CT practice, at competitive speeds and affordable cost.

Dynamic right ventricular evaluation in pulmonary arterial hypertension using novel ultra-fast cardiac magnetic resonance imaging acquisition

Simple and fast computation of moments

BackgroundMolecular imaging using magnetic nanoparticles (MNPs)—magnetic particle imaging (MPI)—has attracted interest for the early diagnosis of cancer and cardiovascular disease. However, because a steep local magnetic field distribution is required to obtain a defined image, sophisticated hardware is required. Therefore, it is desirable to realize excellent image quality even with low-performance hardware. In this study, the spatial resolution of MPI was evaluated using an image reconstruction method based on the correlation information of the magnetization signal in a time domain and by applying MNP samples made from biocompatible ferucarbotran that have adjusted particle diameters.MethodsThe magnetization characteristics and particle diameters of four types of MNP samples made from ferucarbotran were evaluated. A numerical analysis based on our proposed method that calculates the image intensity from correlation information between the magnetization signal generated from MNPs and the system function was attempted, and the obtained image quality was compared with that using the prototype in terms of image resolution and image artifacts.ResultsMNP samples obtained by adjusting ferucarbotran showed superior properties to conventional ferucarbotran samples, and numerical analysis showed that the same image quality could be obtained using a gradient magnetic field generator with 0.6 times the performance. However, because image blurring was included theoretically by the proposed method, an algorithm will be required to improve performance.ConclusionsMNP samples obtained by adjusting ferucarbotran showed magnetizing properties superior to conventional ferucarbotran samples, and by using such samples, comparable image quality (spatial resolution) could be obtained with a lower gradient magnetic field intensity.

An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation