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International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA

作者： M. Rouainia N. Doghmane LAS laboratory Skikda university Algeria LAMA Laboratory Badji Mokhtar University Annaba Algeria

The aim of our work is to perform an automated tool for brain MRI tissues quantification. The method we develop is based on MRI intensity stochastic analysis. By the use of the Gaussian mixture model for these intensities, we estimate MRI tissues parameters with a combination of the expectation-maximization algorithm and the Markov random field model witch provide contextual constraints that improve the classification of image pixels into three classes of tissue: white matter, grey matter and cerebro-spinal fluid. The automated model based algorithm is also extended to take in account an important MRI artefact: the bias field caused by electromagnetic field inhomogeneities. The resulting automated MRI analysis method simultaneously corrects from MR field inhomogeneities, estimates tissue classes distribution parameters, classifies the image and detects multiple sclerosis lesions when treated images present this pathology. We validate our method on simulated data then on real MRI scans

关键词： Stochastic resonance Image analysis Magnetic analysis Magnetic resonance Magnetic resonance imaging Context modeling Stochastic processes Parameter estimation expectation-maximization algorithms Markov random fields

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Probabilistic Image-Based Rendering with Gaussian Mixture Model

Probabilistic Image-Based Rendering with Gaussian Mixture Mo...

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International Conference on Pattern Recognition

作者： Wenfeng Li Baoxin Li Department of Computer Science & Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)0769525210

One major challenge in traditional image-based rendering is 3D scene reconstruction by estimating accurate dense depth map, which suffers from the ambiguities in textureless or periodically textured regions. Alternatively, statistical methods may be used to estimate a most likely color for each pixel for photorealistic rendering from multiple views of the same scene. Such statistical methods normally require a relatively large number of input images to achieve reasonable quality for the synthesized image, if the estimation is purely nonparametric. In this paper, based on some reasonable assumptions on the configuration of the multiple views, we propose to use a two-component Gaussian mixture model for the appearance of a given pixel in all the views so that both the problem of occlusion and the problem of noise can be considered simultaneously. Then we use the expectation-maximization algorithm to estimate the model parameters. The virtual pixel is given as a maximum likelihood estimate for one of the mixture components. Experiments shows that reasonable performance can be obtained even with only a few input images

关键词： Rendering (computer graphics) Layout Pixel Image reconstruction Statistical analysis expectation-maximization algorithms Parameter estimation Maximum likelihood estimation Information geometry Cameras

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Acoustic Model Adaptation Based on Coarse/Fine Training of Transfer Vectors Using Directional Statistics

Acoustic Model Adaptation Based on Coarse/Fine Training of T...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： S. Watanabe A. Nakamura NTT Communication Science Laboratories NTT Corporation Japan

In this paper, we reformulate an adaptation scheme of coarse/fine training (CFT) of transfer vectors in acoustic modeling by using directional statistics. In CFT, the transfer vector is decomposed into a unit direction vector and a scaling factor. By using coarse tied Gaussian class (coarse class) estimation for the unit direction vector, and by using fine tied Gaussian class (fine class) estimation for the scaling factor, we can obtain accurate transfer vectors with a small number of free parameters. Directional statistics is a method for analyzing geometric parameters (e.g. angle and unit vector) using directional data, and is suited for the analysis of the CFT representation. Using directional statistics as a basis, we construct expectation-maximization algorithms for CFT parameters analytically using the maximum likelihood and Bayesian (maximum a posteriori) approaches. In particular, with the Bayesian approach, prior and posterior distributions for unit direction vectors are represented with a von Mises distribution, a representative distribution in directional statistics. Speaker adaptation experiments show that our proposal improves the performance of large vocabulary continuous speech recognition due to the efficient coarse/fine representation of transfer vectors, compared with the conventional transfer vector adaptation

关键词： Adaptation model Statistics Statistical analysis Statistical distributions Bayesian methods expectation-maximization algorithms Algorithm design and analysis Maximum likelihood estimation Proposals Vocabulary

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A Probabilistic Algorithm for Meg Source Reconstruction

A Probabilistic Algorithm for Meg Source Reconstruction

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Sensor Array and Multichannel Signal Processing Workshop

作者： J.M. Zumer H.T. Attias K. Sekihara S.S. Nagarajan Biomagnetic Imaging Lab Department of Radiology University of California San Francisco CA USA Golden Metallic Inc. San Francisco CA USA Department of Systems Design and Engineering Tokyo Metropolitan University Tokyo Japan

We present a novel algorithm for source localization based on probabilistic modeling of stimulus-evoked MEG/EEG data. This algorithm localizes multiple dipoles with the computational complexity equivalent to a single dipole scan, and is therefore more efficient than traditional multidipole fitting procedures. The algorithm assumes that the activity of multiple dipolar sources can be characterized by a linear combination of known temporal basis functions with unknown coefficients. We model the sensor data as arising from activity in each voxel of interest, plus background activity. We estimate temporal basis functions from the data using a probabilistic algorithm called partitioned-factor analysis, previously developed in our lab. We model background activity outside the voxel of interest as an unknown linear mixture of unobserved background factors plus diagonal sensor noise. We use an expectation-maximization algorithm to calculate MAP estimates of unknown basis function coefficients, background mixing matrix, sensor noise covariance and the likelihood of a dipole in each voxel of interest. In simulations, the algorithm is able to accurately localize several simultaneously-active dipoles, at SNRs typical for averaged MEG data. The algorithm performs well even in configurations that include deep sources and highly correlated sources, and thus is superior to MUSIC and beamforming techniques which are sensitive to correlated sources. The algorithm also correctly localizes real somatosensory and auditory evoked fields to the postcentral sulcus and lower bank of the lateral sulcus, respectively

关键词： Brain modeling Background noise Electroencephalography Computational complexity Sensor phenomena and characterization Partitioning algorithms Algorithm design and analysis expectation-maximization algorithms Covariance matrix Multiple signal classification

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Generalized 3D Kernel Computation Method and Its Application in PET-Insert System

Generalized 3D Kernel Computation Method and Its Application...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Debashish Pal Joseph A. O'Sullivan Heyu Wu Yuan-Chuan Tai Dept of Biomedical Engineering Washington University in St. Louis USA Electrical and Systems Engineering Washington University in St. Louis Mallinckrodt Institute of Radiology Washington University in St. Louis USA Electrical and Systems Engineering Washington University in St. Louis USA

We are developing insert devices for existing PET scanners to improve the image resolution with almost the same sensitivity as current PET scanners. The insert device can be used to zoom into a particular organ of interest. Introduction of the insert inside the scanner leads to three types of coincidences: insert-insert (II), insert-scanner (IS) and scanner-scanner (SS). In typical whole-body PET scanners, coincidences recorded in the scanner are sorted into parallel-beam sinograms and images are reconstructed using linear or iterative techniques. In the PET-insert system, the coincidences of type IS have an inherent fan-beam geometry. Reconstruction using parallel-beam sinograms introduces severe streaking artifacts in the images. The coincidences sorted into fan-beam sinograms reduce the artifacts in the reconstructed images. The approach to compute the kernel was derived from CT as there exists an analogy between the PET-insert geometry and a fourth generation CT scanner geometry. In this approach, the weights in the kernel are computed using the intersection of a cone with a voxel. We previously developed two dimensional reconstruction algorithms for this novel system geometry. We extend this work to three dimensions in this paper. A maximum-likelihood expectation-maximization algorithm was used to reconstruct the data. The kernel was validated with a contrast recovery study using a digital phantom. Images reconstructed from experimental data show good quality without any visible artifacts.

关键词： Kernel Image reconstruction Whole-body PET Computed tomography Computational geometry Positron emission tomography Image resolution Reconstruction algorithms expectation-maximization algorithms Imaging phantoms

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SNiPer 500K: A Novel SNP Genotype Calling Algorithm

SNiPer 500K: A Novel SNP Genotype Calling Algorithm

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IEEE/NLM Life Science Systems and Applications Workshop

作者： Jianping Hua David W. Craig Marcel Brun Jennifer Webster Victoria Zismann Keta Joshipura Matthew Huentelman Edward R. Dougherty Dietrich A. Stephan Translational Genomics Research Institute Phoenix Phoenix AZ USA

ISBN: (纸本)1424402778

The fast development in high-throughput single nucleotide polymorphism (SNP) genotyping technology provides unprecedented power for genome-wide SNP association studies. However, inaccurate genotyping can lead to false association between markers and phenotype. We introduce a new SNP genotyping scheme, SNiPer 500K, for Affymetrix 500K GeneChip. SNiPer 500K designs a parametric classifier for each SNP, by using expectation-maximization algorithm to estimate the parameters. A quality index provided by SNiPer 500K shows that it can reliably genotype about 65% of all SNPs, with superior call rate, better Hardy-Weinberg equilibrium and reproducibility. SNiPer 500K provides a good balance between coverage and accuracy for high-throughput SNP genotyping and would prove to be an invaluable tool for genome-wide association study

关键词： Genomics Bioinformatics Parameter estimation Algorithm design and analysis Probes Gaussian distribution Covariance matrix expectation-maximization algorithms Reproducibility of results Signal design

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The EM Algorithm and Related Statistical Models

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Technometrics 2006年第1期48卷 148页

作者： Singh, Ashok K University of Nevada-Las Vegas

Singh reviews The EM Algorithm and Related Statistical Models edited by Michiko Watanabe and Kazunori Yamaguchi.

关键词： expectation-maximization algorithms Nonfiction

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EM-ML algorithm for track initialization using possibly noninformative data

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2005年第3期41卷 1030-1048页

作者： Cai, J Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an F1 Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the, ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the IMM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection,,and computational load are illustrated using simulated scenarios as well.

关键词： algorithms ESTIMATION theory FIGHTER planes FALSE alarms expectation-maximization algorithms REFLECTION (Optics)

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Blind deblurring of spiral CT images Based on ENR and Wiener filter

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2005年第1期13卷 49-60页

作者： Wang, Jing Wang, Ge Jiang, Ming Peking Univ Sch Math LMAM Beijing 100871 Peoples R China Univ Iowa Dept Radiol CT Micro Ct Lab Iowa City IA 52242 USA

In previous studies, we developed a blind deblurring algorithm based on the edge-to-noise ratio (ENR) and EM algorithm to improve the quality of spiral CT images. In this work, we apply the Wiener filter in place of the EM algorithm to improve the timing performance. The algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea image is substantially improved. The new ENR-Wiener blind deblurring approach significantly improves the computation speed with comparable image quality in previous studies.

关键词： expectation-maximization algorithms SPIRAL computed tomography COCHLEA IMAGING systems -- Image quality COCHLEAR implants

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Latent variable models for misclassified polytomous outcome variables

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2005年第2期58卷 359-375页

作者： Eickhoff, JC Amemiya, Y Univ Wisconsin Dept Biostat & Med Informat Madison WI 53726 USA IBM Corp Div Res TJ Watson Res Ctr Yorktown Hts NY 10598 USA

The article analyzes latent variable models with misclassified polytomous outcome variables. Modeling misclassification patterns imposes identification difficulties. The assumption of monotone misclassification is reasonable in many social and behavioral science studies where data are based on interview questions. Modifications of the proposed monotone misclassification pattern could be considered. The maximum likelihood estimation is performed using an EM algorithm where the E-step is computed via simple Monte Carlo integration. The M-step is computed using an iterative procedure.

关键词： expectation-maximization algorithms MONTE Carlo method MULTIVARIATE analysis SAMPLING (Statistics) VARIABLES (Mathematics) NUMERICAL analysis

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