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Multimodal image registration based on the expectation-maximisation methodology

IET IMAGE PROCESSING 2017年第12期11卷 1246-1253页

作者： Arce-Santana, Edgar R. Campos-Delgado, Daniel U. Reducindo, Isnardo Mejia-Rodriguez, Aldo R. Univ Autonoma San Luis Potosi Fac Ciencias San Luis Potosi SLP Mexico Univ Autonoma San Luis Potosi Fac Ciencias Informac San Luis Potosi SLP Mexico

In this study, a new framework for multimodal image registration is proposed based on the expectation-maximisation (EM) methodology. This framework allows to address simultaneously parametric and elastic registrations independently on the modality of the target and source images without making any assumptions about their intensity relationship. The EM formulation for the image registration problem leads to a regularised quadratic optimisation scheme to compute the displacement vector field (DVF) that aligns the images and depends on their joint intensity distribution. At the first stage, a parametric transformation is assumed for the DVF, where the resulting quadratic optimisation is computed recursively to calculate its optimal parameters. Next, a general unknown deformation models the elastic part of the DVF, which is represented by an additive structure. The resulting optimisation process by the EM formulation results in a cost function that involves data and regularisation terms, which is also solved recursively. A comprehensive evaluation of the parametric and elastic proposals is carried out by comparing to state-of-the-art algorithms and images from different application fields, where an advantage is visualised by the authors' proposal in terms of a compromise between accuracy and robustness.

关键词： image registration expectation-maximisation algorithm quadratic programming recursive estimation multimodal image registration expectation-maximisation methodology EM methodology elastic registration parametric registration source image target image quadratic optimisation scheme displacement vector field joint intensity distribution parametric transformation DVF optimal parameter calculation general unknown deformation model additive structure cost function

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Robust distribution system state estimation with hybrid measurements

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第16期14卷 3250-3259页

作者： Kumar, C. Santhosh Rajawat, Ketan Chakrabarti, Saikat Pal, Bikash C. Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India Imperial Coll London Dept Elect Elect Engn London England

With growing connection of distributed energy resources, availability of demand side response technologies, deployment of smart meters, the distribution system needs advanced network automation for running the system efficiently. State estimation is the core driver of network automation. While the output from SMs will make the state estimation more accurate, advanced metering infrastructures come with several challenges such as noisy, erroneous measurement including lost or missed measurements, exposure to cyber attack and so on. This study proposes a three-phase unbalanced distribution system state estimation which is robust against noisy distribution system measurements, bad data attacks and missing or delayed measurements. This method considers measurement from hybrid sources such as SCADA, micro-phasor measurement units (mu PMUs) and SMs. Kalman smoother is used to fill the missing measurements and expectation-maximisation based forecasting is used to interpolate the hybrid measurements to a common timestamp and compensate for the delay in SM measurements. Extensive numerical comparisons are made on IEEE 13, 37 and 123 bus systems to test the robustness of the proposed DSSE against delayed SM measurements and bad or noisy data. An IEEE 24 bus system is modelled and real-time measurement devices are interfaced to it in Hypersim. The data from the hybrid measurement devices of IIT Kanpur smart grid is also used to test the robustness of the proposed method.

关键词： power system state estimation phasor measurement smart power grids Kalman filters distributed power generation expectation-maximisation algorithm demand side management smart meters interpolation compensation robust distribution system state estimation distributed energy resources demand side response technologies smart meters SMs advanced network automation advanced metering infrastructures three-phase unbalanced distribution system state estimation noisy distribution system measurements bad data attacks delayed measurements hybrid sources microphasor measurement units delayed SM measurements noisy data IEEE 24 bus system real-time measurement devices hybrid measurement devices network automation cyber attack DSSE SCADA mu PMUs Kalman smoother expectation-maximisation based forecasting delay compensation common timestamp IIT Kanpur smart grid IEEE 13 bus systems IEEE 123 bus systems IEEE 37 bus systems Hypersim

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EFFICIENT ESTIMATION OF CLASS-A NOISE PARAMETERS VIA THE EM algorithm

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IEEE TRANSACTIONS ON INFORMATION THEORY 1991年第1期37卷 60-72页

作者： ZABIN, SM POOR, HV PRINCETON UNIV DEPT ELECT ENGNPRINCETONNJ 08544

The Class A Middleton noise model is a commonly used statistical-physical, parametric model for non-Gaussian interference superimposed on a Gaussian background. In this study, the problem of efficient estimation of the Class A parameters for small sample sizes is considered. The proposed estimator is based on the EM algorithm, a two-step iterative estimation technique that is ideally suited for the Class A estimation problem since the observations can be readily treated as incomplete data. For the single-parameter estimation problem, a closed-form expression for the estimator is obtained. Furthermore, for the single-parameter estimation problem, it is shown that the sequence of estimates obtained via the EM algorithm converges, and a characterization of the point to which the sequence converges is given. In particular, it is shown that if the limit point of this convergent sequence is an interior point of the parameter set of interest, then it must be a stationary point of the traditional likelihood function. In addition, for both the single-parameter and two-parameter estimation problems, the small-sample-size performance of the proposed EM algorithm is examined via an extensive simulation study.

关键词： Likelihood Functions Estimation Techniques Noise parameters expectation-maximisation algorithm Convergent Sequence Parameter estimation Noise models equilibrium point class A

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Values below detection limit in compositional chemical data

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ANALYTICA CHIMICA ACTA 2013年 764卷 32-43页

作者： Palarea-Albaladejo, J. Martin-Fernandez, J. A. JCMB Biomath & Stat Scotland Edinburgh EH9 3JZ Midlothian Scotland Univ Girona Dept Comp Sci & Appl Math E-17071 Girona Spain

Samples representing part of a whole, usually called compositional data in statistics, are commonplace in analytical chemistry say chemical data in percentage, ppm, or mu g g(-1). Their distinctive feature is that there is an inherent relationship between all the analytes constituting a chemical sample as they only convey relative information. Some compositional data analysis principles and the log-ratio based methodology are outlined here in practical terms. Besides, one often finds that some analytes are not present in sufficient concentration in a sample to allow the measuring instruments to effectively detect them. These non-detects are usually labelled as "

关键词： Compositional data Detection limits expectation-maximisation algorithm Log-normal distribution Log-ratio approach Imputation methods

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Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions - application: AEB system

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IET INTELLIGENT TRANSPORT SYSTEMS 2018年第9期12卷 1148-1156页

作者： Chelbi, Nacer Eddine Gingras, Denis Sauvageau, Claude Univ Sherbrooke LIV 2500 Boul Univ Sherbrooke PQ Canada PMG Technol Inc 100 Rue Landais Blainville PQ Canada

This study presents a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions. The approach identifies the worst-case scenarios for a given advanced driver assistance function, AEB system in this study, based on field operational tests (FOT) [safety pilot model deployment (SPMD), in this study]. The authors begin with a description of the studied AEB system and a synthesis of the most relevant tests scenarios. Then, they model the distribution of each test parameter retrieved from the SPMD database by applying two estimation methods (kernel method and expectation-maximisation algorithm). A comparison was made between the two methods to choose the best one. These distributions are then sampled using the proposed sampling strategy based on Metropolis-Hastings algorithm. Then, the idea is to take the samples of each parameter retrieved with this sampler, simulate them on a vehicular software simulator (PreScan) and to get their simulation results. For each test and in case of impact, a proportional score to the speed of impact reduction is attributed. Finally, a risk classification is done based on the scoring results which allows to recover high and very high-risk cases to build a set of worst-case scenarios.

关键词： Markov processes sampling methods driver information systems road vehicles expectation-maximisation algorithm Bayes methods Monte Carlo methods virtual evaluation approach preventive safety applications advanced driver assistance functions worst-case scenarios given advanced driver assistance function field operational tests [safety pilot model deployment studied AEB system relevant tests scenarios test parameter kernel method expectation-maximisation algorithm

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The expectation-maximization algorithm

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IEEE SIGNAL PROCESSING MAGAZINE 1996年第6期13卷 47-60页

作者： Moon, TK UTAH STATE UNIV CTR SELF ORGANIZING INTELLIGENT SYSTLOGANUT

A common task in signal processing is the estimation of the parameters of a probability distribution function. Perhaps the most frequently encountered estimation problem is the estimation of the mean of a signal in noise. In many parameter estimation problems the situation is more complicated because direct access to the data necessary to estimate the parameters is impossible, or some of the data are missing. Such difficulties arise when an outcome is a result of an accumulation of simpler outcomes, or when outcomes are clumped together, for example, in a binning or histogram operation. There may also be data dropouts or clustering in such a way that the number of underlying data points is unknown (censoring and/or truncation). The EM (expectation-maximization) algorithm is ideally suited to problems of this sort, in that it produces maximum-likelihood (ML) estimates of parameters when there is a many-to-one mapping from an underlying distribution to the distribution governing the observation. The EM algorithm is presented at a level suitable for signal processing practitioners who have had some exposure to estimation theory.

关键词： expectation-maximisation algorithm estimation theory Parameter estimation signal processing

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On-board SPECT for localizing functional targets: A simulation study

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MEDICAL PHYSICS 2009年第5期36卷 1727-1735页

作者： Roper, Justin Bowsher, James Yin, Fang-Fang Duke Univ Med Ctr Dept Radiat Oncol Med Phys Grad Program Durham NC 27710 USA

Single photon emission computed tomography (SPECT) was investigated for imaging on-board radiation therapy machines in order to localize functional and molecular targets. A computer-simulated female NCAT phantom was positioned supine on a flat-top treatment couch. Twenty tumor locations were defined in the upper torso. The eight lung tumors were subject to the effects of respiratory motion. Tumor diameters of 10.8, 14.4, and 21.6 mm were simulated for tumor-to-background ratios of 3:1 and 6:1 that are characteristic of the radiotracer (99m)Tc-sestamibi. Projection images representing scan times of 4, 8, and 20 min were simulated for an anterior, half-circular trajectory. Images were reconstructed with attenuation correction by ordered-subsets expectation maximization (OSEM) using six subsets and five iterations. Contrast-to-noise ratios (CNRs) were calculated from ensembles of 25 images. Cross correlation with a noise-free tumor template was used to select the most suspicious tumor location within a 14.4-mm-radius search volume surrounding each tumor, with only that one tumor in each search volume. Localization accuracy was assessed by calculating average distances between measured and true tumor locations. Localization accuracy and CNRs were strongly affected by tumor location relative to the detector trajectory. For example, CNR values near the chest wall were greater by a factor of 3.5 than for tumors near the spine and posterior ribs, a much greater effect than the factor of 1.6 difference in CNR between 6:1 and 3:1 tumor uptakes. Typically, tumors of 6:1 uptake were localized as accurately with 4 min of scan time as tumors of 3:1 uptake that had been imaged for 20 min. Using 4 min scans, 14.4 and 21.6 mm anterior tumors of 6:1 uptake were localized within 2 mm. These results suggest that SPECT, on-board radiation therapy machines, may be a viable modality for localizing certain functional and molecular targets using relatively short scan times.

关键词： expectation-maximisation algorithm image reconstruction lung medical image processing object detection phantoms radiation therapy single photon emission computed tomography tumours

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Maximum a posteriori-based texture estimation by despeckling of SAR clutter amplitude data

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IET IMAGE PROCESSING 2017年第8期11卷 656-666页

作者： Mahapatra, Dheeren Ku Ray, Soumya Shubhra Roy, Lakshi Prosad Natl Inst Technol Dept Elect & Commun Engn Rourkela India

Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signaldependent granular noise known as speckle. For accurate texture estimation, the maximum a posteriori (MAP) approach is found to be efficient among multitude of despeckling approaches reported in the available literature. However, the formulations of MAP estimator for texture are available in 'intensity format'. The authors formulate G-MAP and beta-MAP estimators for texture in 'amplitude format', considering clutter to be K-1/2 and W-1/2 distributed. The parameters associated with K1/2 and W1/2 distributions are estimated using Mellin transform and second-kind statistics. Furthermore, an MAP-based filter is proposed here for mixture of Cauchy-Exponential compound Gaussian (CE-CG) distributed clutter data where the texture is modelled using CE mixture model. For estimation of CE model parameters, they employ expectation-maximisation (EM) algorithm. Finally, the G-MAP, beta-MAP, and CE-MAP estimation of texture both in 'amplitude format' and 'intensity format' are performed on single look real SAR clutter data and also on multilook synthetic data. Experimental results illustrate the superiority of CE-MAP filter and moreover that the MAP estimation in 'amplitude format' achieves better mean preservation and speckle suppression compared with the same in 'intensity format'.

关键词： synthetic aperture radar radar imaging image texture maximum likelihood estimation radar signal processing expectation-maximisation algorithm texture estimation maximum a posteriori SAR clutter amplitude data synthetic aperture radar SAR images signal-dependent granular noise MAP amplitude format' Mellin transform second-kind statistics Cauchy-Exponential compound Gaussian CE-CG distributed clutter data expectation-maximisation EM algorithm intensity format'

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APPLICATION OF expectation-MAXIMIZATION algorithm TO THE DETECTION OF A DIRECT-SEQUENCE SIGNAL IN PULSED NOISE JAMMING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1993年第8期41卷 1151-1154页

作者： ANSARI, A VISWANATHAN, R Department of Electrical Engineering Southem Illinois University Carbondale IL USA

We consider the detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming environment. The expectation-maximization algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated likelihood functions. The probability of error performance of the algorithm is simulated for a repeat code and a (7,4) block code. Simulation results show that at low signal-to-thermal noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradation as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector.

关键词： binary signal Limiter Device Component expectation-maximisation algorithm signal spectrum jammers error performance low SNR

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Factor analysed hidden Markov models for speech recognition

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COMPUTER SPEECH AND LANGUAGE 2004年第2期18卷 181-200页

作者： Rosti, AVI Gales, MJF Univ Cambridge Dept Engn Cambridge CB2 1PZ England

Recently various techniques to improve the correlation model of feature vector elements in speech recognition systems have been proposed. Such techniques include semi-tied covariance hidden Markov models (HMMs) and systems based on factor analysis. All these schemes have been shown to improve the speech recognition performance without dramatically increasing the number of model parameters compared to standard diagonal covariance Gaussian mixture HMMs. This paper introduces a general form of acoustic model, the factor analysed HMM. A variety of configurations of this model and parameter sharing schemes, some of which correspond to standard systems, were examined. An EM algorithm for the parameter optimisation is presented along with a number of methods to increase the efficiency of training. The performance of FAHMMs on medium to large vocabulary continuous speech recognition tasks was investigated. The experiments show that without elaborate complexity control an equivalent or better performance compared to a standard diagonal covariance Gaussian mixture HMM system can be achieved with considerably fewer parameters. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： model parameters speech recognition system Parameter optimization expectation-maximisation algorithm Eigenvector Speech recognition Covariance Hidden Markov models Diagonal

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