Projection Filter Research Articles

Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System

This paper presents more accurate and reliable computational methods for aiding the treatment of people with coronary artery disease. New techniques are introduced for improved evaluation and distinguish cardiac disease affected patients from the healthy controls. Experiments are conducted with high level of error tolerance rate and confidence level at 95% and 99% and established the results with corrected T-tests based on comparison of various performance measures. Normal kernel density estimator is used for visual distinction of cardiac controls. A new ensemble learning method comprising of Bayesian network as classifier and Principal components method as the projection filter with ranker search is used for the relevant feature selection. Analysis of each model is performed and discusses major findings and concludes with promising results compared to the related works. Multiple Correspondence analysis is used for exploring heart disease variable's relationships. Robust machine learning algorithms used are Rotation forests, MultiBoosting, Sparse multinomial logistic regression for better performance with fine tuning of their involved parameters. The work aims at improving the software reliability and quality of diagnosis of cardiac disease with robust inference system. To the best of our knowledge, from the literature survey, experimental results presented in this work show best results with supportive statistical inference.

Eliminating beta-continuation from Heaviside projection and density filter algorithms

Projection methods and density filters based on the Heaviside step function are an effective means for producing discrete (0---1) solutions in continuum topology optimization. They naturally impose...

An Alternating Selection for Parallel Affine Projection Filters

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

Integrating the Projective Transform with Particle Filtering for Visual Tracking

This paper presents the projective particle filter, a Bayesian filtering technique integrating the projective transform, which describes the distortion of vehicle trajectories on the camera plane. The characteristics inherent to traffic monitoring, and in particular the projective transform, are integrated in the particle filtering framework in order to improve the tracking robustness and accuracy. It is shown that the projective transform can be fully described by three parameters, namely, the angle of view, the height of the camera, and the ground distance to the first point of capture. This information is integrated in the importance density so as to explore the feature spacemore accurately. By providing a fine distribution of the samples in the feature space, the projective particle filter outperforms the standard particle filter on different tracking measures. First, the resampling frequency is reduced due to a better fit of the importance density for the estimation of the posterior density. Second, the mean squared error between the feature vector estimate and the true state is reduced compared to the estimate provided by the standard particle filter. Third, the tracking rate is improved for the projective particle filter, hence decreasing track loss.

Subspace projection filters for imaging in random media

We consider the problem of selective imaging extended reflectors in cluttered media. We propose a random travel time model for simulating the array response matrix in clutter and we compare it with the full wave solution. Our simplified model captures very well the full wave random medium behavior as this is illustrated by our numerical results. The algorithm for selective array imaging uses coherent interferometry on a filtered version of the data. The filter, which is based on the singular value decomposition of the response matrix, enhances the signal reflected by the edges of the reflector. We illustrate the performance of the imaging algorithm with numerical simulations in the regime of ultrasonic non-destructive testing in concrete.

Time-Frequency Analysis and Hermite Projection Method Applied to Swallowing Accelerometry Signals

Fast Hermite projections have been often used in image-processing procedures such as image database retrieval, projection filtering, and texture analysis. In this paper, we propose an innovative approach for the analysis of one-dimensional biomedical signals that combines the Hermite projection method with time-frequency analysis. In particular, we propose a two-step approach to characterize vibrations of various origins in swallowing accelerometry signals. First, by using time-frequency analysis we obtain the energy distribution of signal frequency content in time. Second, by using fast Hermite projections we characterize whether the analyzed time-frequency regions are associated with swallowing or other phenomena (vocalization, noise, bursts, etc.). The numerical analysis of the proposed scheme clearly shows that by using a few Hermite functions, vibrations of various origins are distinguishable. These results will be the basis for further analysis of swallowing accelerometry to detect swallowing difficulties.

Measure and modeling the ambient air particles and particle bound mercury Hg(p) at a traffic sampling site

Ambient air samples were collected between Dec 9, 2008 and Jan 14, 2009 at a highway traffic site located in Sha-Lu, central Taiwan. Atmospheric particulates and particulate bound mercury Hg(p) dry deposition fluxes, concentrations and dry deposition velocities were also studied. The objective of this study was to: 1) measure the dry deposition concentrations of ambient air particles and particle bound mercury Hg(p) following one and two days of sampling events using the total suspended particulates (TSP) sampler, and dry deposition plate (DDP); 2) modeling the dry deposition fluxes of particles and particle bound mercury Hg(p) with calculated deposition velocities then to obtain a calculated dry deposition fluxes; and 3) compared the results of calculated dry deposition fluxes to measured dry deposition fluxes. All the results were presented with one and two days of sampling events with different deposition collection media (i.e., quartz filter and overhead projection film). The results indicated that the average calculated/measured dry deposition fluxes ratios for bound mercury (Hg(p)) for 24 sampling group were 2.27. The results also indicated that the one day calculated/measured dry deposition flux ratios for Hg(p) for overhead projection film and quartz filter were 2.62 and 1.92, respectively. In addition, the results indicated that the average calculated/measured dry deposition fluxes ratios for 24 sampling group were 4.03. The results also indicated that the two days of calculated/measured dry deposition flux ratios for Hg(p) for overhead projection film and quartz filter were 4.64 and 3.42, respectively. The results of particle bound mercury Hg(p) calculated dry deposition model are slightly higher than that of measured dry deposition flux in either one day or two days of sampling period for both of overhead projection and quartz filter collection medium. .Though there are no significant differences of mean values for both particulates and Hg(p) using overhead projection film or quartz filter as a collection media for the dry deposition-study (Fang et al., 2009), quartz filter exhibits better calculated/measured ratio in the prediction of particle bound mercury (Hg(p)) than that of overhead projection film for either one day of the two days of sampling periods.

Assessment of subgrid-scale models for the incompressible Navier–Stokes equations

In this paper, we assess two kinds of subgrid finite element methods for the two-dimensional (2D) incompressible Naver-Stokes equations (NSEs). These methods introduce subgrid-scale (SGS) eddy viscosity terms which do not act on the large flow structures. The eddy viscous terms consist of the fluid flow fluctuation strain rate stress tensors. The fluctuation tensor can be calculated by a elliptic projection or a simple L 2 projection (projective filter) in finite element spaces. The finite element pair P 2 / P 1 is adopted to numerically implement analysis and computation. We give a complete error analysis based on the assumptions of some regularity conditions. On the part of numerical tests, the numerical computations for the stationary flows show that the numerical results agree with some benchmark solutions and theoretical analysis very well. Furthermore, the given SGS models are applied to the non-stationary fluid flows.

Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging

We investigated suitable spatial inverse filters for cortical dipole imaging from the scalp electroencephalogram (EEG). The effects of incorporating statistical information of signal and noise into inverse procedures were examined by computer simulations and experimental studies. The parametric projection filter (PPF) and parametric Wiener filter (PWF) were applied to an inhomogeneous three-sphere volume conductor head model. The noise covariance matrix was estimated by applying independent component analysis (ICA) to scalp potentials. The present simulation results suggest that the PPF and the PWF provided excellent performance when the noise covariance was estimated from the differential noise between EEG and the separated signal using ICA and the signal covariance was estimated from the separated signal. Moreover, the spatial resolution of the cortical dipole imaging was improved while the influence of noise was suppressed by including the differential noise at the instant of the imaging and by adjusting the duration of noise sample according to the signal to noise ratio. We applied the proposed imaging technique to human experimental data of visual evoked potential and obtained reasonable results that coincide to physiological knowledge.

A source separation approach to enhancing marine mammal vocalizations

A common problem in passive acoustic based marine mammal monitoring is the contamination of vocalizations by a noise source, such as a surface vessel. The conventional approach in improving the vocalization signal to noise ratio (SNR) is to suppress the unwanted noise sources by beamforming the measurements made using an array. In this paper, an alternative approach to multi-channel underwater signal enhancement is proposed. Specifically, a blind source separation algorithm that extracts the vocalization signal from two-channel noisy measurements is derived and implemented. The proposed algorithm uses a robust decorrelation criterion to separate the vocalization from background noise, and hence is suitable for low SNR measurements. To overcome the convergence limitations resulting from temporally correlated recordings, the supervised affine projection filter update rule is adapted to the unsupervised source separation framework. The proposed method is evaluated using real West Indian manatee (Trichechus manatus latirostris) vocalizations and watercraft emitted noise measurements made within a typical manatee habitat in Florida. The results suggest that the proposed algorithm can improve the detection range of a passive acoustic detector five times on average (for input SNR between -10 and 5 dB) using only two receivers.

Understanding and controlling the effect of lossy raw data compression on CT images

The requirements for raw data transmission through a CT scanner slip ring, through the computation system, and for storage of raw CT data can be quite challenging as scanners continue to increase in speed and to collect more data per rotation. Although lossy compression greatly mitigates this problem, users must be cautious about how errors introduced manifest themselves in the reconstructed images. This paper describes two simple yet effective methods for controlling the effect of errors in raw data compression and describe the impact of each stage on the image errors. A CT system simulator (CATSIM, GE Global Research Center, Niskayuna, NY) was used to generate raw CT datasets that simulate different regions of human anatomy. The raw data are digitized by a 20-bit ADC and companded by a log compander. Lossy compression is performed by quantization and is followed by JPEG-LS (lossless), which takes advantage of the correlations between neighboring measurements in the sinogram. Error feedback, a previously proposed method that controls the spatial distribution of reconstructed image errors, and projection filtering, a newly proposed method that takes advantage of the filtered backprojection reconstruction process, are applied independently (and combined) to study their intended impact on the control and behavior of the additional noise due to the compression methods used. The log compander and the projection filtering method considerably reduce image error levels, while error feedback pushes image errors toward the periphery of the field of view. The results for the images are a compression ratio (CR) of 3 that keeps peak compression errors under 1 HU and a CR of 9 that increases image noise by only 1 HU in common CT applications. Lossy compression can substantially reduce raw CT data size at low computational cost. The proposed methods have the flexibility to operate at a wide range of compression ratios and produce predictable, object-independent, and often imperceptible image artifacts.

RKHS Bayes Discriminant: A Subspace Constrained Nonlinear Feature Projection for Signal Detection

Given the knowledge of class probability densities, a priori probabilities, and relative risk levels, Bayes classifier provides the optimal minimum-risk decision rule. Specifically, focusing on the two-class (detection) scenario, under certain symmetry assumptions, matched filters provide optimal results for the detection problem. Noticing that the Bayes classifier is in fact a nonlinear projection of the feature vector to a single-dimensional statistic, in this paper, we develop a smooth nonlinear projection filter constrained to the estimated span of class conditional distributions as does the Bayes classifier. The nonlinear projection filter is designed in a reproducing kernel Hilbert space leading to an analytical solution both for the filter and the optimal threshold. The proposed approach is tested on typical detection problems, such as neural spike detection or automatic target detection in synthetic aperture radar (SAR) imagery. Results are compared with linear and kernel discriminant analysis, as well as classification algorithms such as support vector machine, AdaBoost and LogitBoost.

Noncausal spatial prediction filtering based on an ARMA model

Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

Physical performance and image optimization of megavoltage cone‐beam CT

Megavoltage cone-beam CT (MVCBCT) is the most recent addition to the in-room CT systems developed for image-guided radiation therapy. The first generation MVCBCT system consists of a 6 MV treatment x-ray beam produced by a conventional linear accelerator equipped with a flat panel amorphous silicon detector. The objective of this study was to evaluate the physical performance of MVCBCT in order to optimize the system acquisition and reconstruction parameters for image quality. MVCBCT acquisitions were performed with the clinical system but images were reconstructed and analyzed with a separate research workstation. The geometrical stability and the positioning accuracy of the system were evaluated by comparing geometrical calibrations routinely performed over a period of 12 months. The beam output and detector intensity stability during MVCBCT acquisition were also evaluated by analyzing in-air acquisitions acquired at different exposure levels. Several system parameters were varied to quantify their impact on image quality including the exposure (2.7, 4.5, 9.0, 18.0, and 54.0 MU), the craniocaudal imaging length (2, 5, 15, and 27.4 cm), the voxel size (0.5, 1, and 2 mm), the slice thickness (1, 3, and 5 mm), and the phantom size. For the reconstruction algorithm, the study investigated the effect of binning, averaging and diffusion filtering of raw projections as well as three different projection filters. A head-sized water cylinder was used to measure and improve the uniformity of MVCBCT images. Inserts of different electron densities were placed in a water cylinder to measure the contrast-to-noise ratio (CNR). The spatial resolution was obtained by measuring the point-spread function of the system using an iterative edge blurring technique. Our results showed that the geometric stability and accuracy of MVCBCT were better than 1 mm over a period of 12 months. Beam intensity variations per projection of up to 35.4% were observed for a 2.7 MU MVCBCT acquisition. These variations did not cause noticeable reduction in the image quality. The results on uniformity suggest that the cupping artifact occurring with MVCBCT is mostly due to off-axis response of the detector and not scattered radiation. Simple uniformity correction methods were developed to nearly eliminate this cupping artifact. The spatial resolution of the baseline MVCBCT reconstruction protocol was approximately 2 mm. An optimized reconstruction protocol was developed and showed an improvement of 75% in CNR with a penalty of only 8% in spatial resolution. Using this new reconstruction protocol, large adipose and muscular structures were differentiated at an exposure of 9 MU. A reduction of 36% in CNR was observed on a larger (pelvic-sized) phantom. This study demonstrates that soft-tissue visualization with MVCBCT can be substantially improved with proper system settings. Further improvement is expected from the next generation MVCBCT system with an optimized megavoltage imaging beamline.

NPS characterization and evaluation of a cone beam CT breast imaging system

The Noise Power Spectrum (NPS) is a function that yields information about the spatial frequency composition of noise in images obtained by a system. It is evaluated by calculating the absolute value squared of the noise image and normalizing it with respect to the voxel and matrix sizes. Consequently, the NPS has been one of the physical characteristics that is commonly used to quantitatively measure the physical performance of a system. In this article, we evaluated the NPS of a Cone Beam CT Breast Imaging system by considering the following factors. First, we evaluated its symmetry around the x- and y-axis along with the influence of the cone angle and the matrix size on the NPS. Then, an analytical curve was suggested to best represent the NPS. Second, we analyzed the influence on the NPS of a set of seven parameters, namely the pixel size, exposure level, kVp value, number of projections acquired, voxel size, back projection filter, and the reconstruction algorithm employed. In addition, since the breast induced scattering in the image, we investigated the effect of the scattering-correction algorithm used in this system. Finally, we evaluated the uniformity of the NPS as a function of z with the matrix center located at {r = 0 mm}. The results demonstrate that the proposed curve is an ideal candidate that best represents the NPS. Hence, two parameters, the amplitude (A) and the width (sigma), can be used to characterize the curve. The results also demonstrate that the voxel size and the cone angle are the only two parameters investigated in this study that do not affect the NPS. On the other hand, the matrix and pixel sizes, the back-projection filter and the reconstruction algorithm, the exposure level and the scattering correction, all influence the NPS. Finally, the results of the last part of this investigation suggest that this imaging system does not have a 3D isotropic noise distribution along the z-axis; yielding less noisy images at around z = 0.00 m and z = 80 mm.

Grid filter models for large‐eddy simulation

AbstractA new interpretation of approximate deconvolution models (ADM) when used with implicit filtering as a way to approximate the projective grid filter is given. Consequently, a new category of subgrid models, the grid filter models (GFM) is defined. ADM appear as a particular case of GFM since only approximate deconvolution is achieved. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Investigating the 1/z 2-filtering in tomography algorithms

The filtering of projections in tomography algorithms, which is based on the regularization of distribution 1/z2, is investigated. Numerical simulations validate that changing the filter support will substantially improve the tomograms. Comparison of the results of reconstruction by the 1/z2-filtering algorithm and the Shepp-Logan algorithm shows that the reconstruction accuracy of the latter is worse when the number of angles of observation is small or a high noise is present in the projection data.

1205 円筒シェルの逆解析手法としての可変パラメトリック射影フィルタについて(J01-1 逆問題解析手法の開発と最新応用(1),J01 逆問題解析手法の開発と最新応用)

Variable parametric projection filter (VPPF) is proposed as inverse analysis procedure to identify the system rigidity and water level of cylindrical shell structure. Parametric projection filter has known as effective filter in compare with Wiener filter and projection filter. As the characteristics of the parametric projection filter, the regular parameter to assure the stable filtering process is including. The regular parameter has been given as a voluntary value corresponding to each inverse problem in the existing analysis concerned with inverse problem using filtering algorithm based on parametric projection filter. In this study, the procedure to give the regular parameter computationally in each filtering step is shown instead of a voluntary value. Effectiveness of the VPPF is shown though some numerical calculations based on the previous problem.

Cortical Dipole Imaging of Movement-related Potentials by Means of Parametric Inverse Filters Incorporating with Signal and Noise Covariance

The objective of this study is to explore suitable spatial filters for inverse estimation of cortical equivalent dipole layer imaging from the scalp electroencephalogram. We utilize cortical dipole source imaging to locate the possible generators of scalp-measured movement-related potentials (MRPs) in human. The effects of incorporating signal and noise covariance into inverse procedures were examined by computer simulations and experimental study. The parametric projection filter (PPF) and parametric Weiner filter (PWF) were applied to an inhomogeneous three-sphere head model under various noise conditions. The present simulation results suggest that the PWF incorporating signal information provides better cortical dipole layer imaging results than the PPF and Tikhonov regularization under the condition of moderate and high correlation between signal and noise distributions. On the other hand, the PPF has better performance than other inverse filters under the condition of low correlation between signal and noise distributions. The proposed methods were applied to self-paced MRPs in order to identify the anatomic substrate locations of neural generators. The dipole layer distributions estimated by means of PPF are well-localized as compared with blurred scalp potential maps and dipole layer distribution estimated by Tikhonov regularization. The proposed methods demonstrated that the contralateral premotor cortex was preponderantly activated in relation to movement performance. In cortical dipole source imaging, the PWF has better performance especially when the correlation between the signal and noise is high. The proposed inverse method was applicable to human experiments of MRPs if the signal and noise covariances were obtained.

Subspace Projection Filters for Real-Time Brain Electromagnetic Imaging

An increasing number of neuroimaging laboratories are becoming interested in real-time investigations of the human brain. The opportunities offered by real-time applications are inversely proportional to the latency of the brain activity response and to the computational delay of brain activity estimation. Electromagnetic tomographies, based on electroencephalography (EEG) or magnetoencephalography (MEG), feature immediacy of brain activity response and excellent time resolution, hence they are natural candidates. However their spatial resolution and signal-to-noise ratio are poor. In this paper, we develop data-independent and data-dependent subspace projection filters for the standardized low-resolution electromagnetic tomography (sLORETA), a weighted minimum norm inverse solution for EEG/MEG. The filters are designed for extracting time-series of source activity in any given region of interest. The data-independent filter is shown to reduce interference of sources originating in neighboring regions, whereas the data-dependent filter is shown to suppress sensor measurement noise. An effective and straightforward way to combine them is demonstrated. The result is a dual subspace projection allowing both noise suppression and interference reduction.