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Region of interest evaluation of SPECT image reconstruction methods using a realistic brain phantom

IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1997年第3期44卷 1336-1341页

作者： Xia, WS Glick, SJ Pan, TS Soares, EJ Luo, DS Laboratory of Chromatography DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico

A realistic numerical brain phantom, developed by Zubal et al, was used for a region-of-interest evaluation of the accuracy and noise variance of the following SPECT reconstruction methods: 1) Maximum-Likelihood reconstruction using the expectation-Maximization (ML-EM) algorithm;2) an EM algorithm using ordered-subsets (OS-EM);3) a re-scaled block iterative EM algorithm (RBI-EM);and 4) a filtered backprojection algorithm that uses a combination of the Bellini method for attenuation compensation and an iterative spatial blurring correction method using the frequency-distance principle (FDP). The Zubal phantom was made from segmented MRI slices of the brain, so that neuro-anatomical structures are well defined and indexed. Small regions-of-interest (ROIs) from the white matter, grey matter in the center of the brain and grey matter from the peripheral area of the brain were selected for the evaluation. Photon attenuation and distance-dependent collimator blurring were modeled. Multiple independent noise realizations were generated for two different count levels. The simulation study showed that the ROI bias measured for the EM-based algorithms decreased as the iteration number increased, and that the OS-EM and RBI-EM algorithms (16 and 64 subsets were used) achieved the equivalent accuracy of the ML-EM algorithm at about the same noise variance, with much fewer number of iterations. The Bellini-FDP restoration algorithm converged fast and required less computation per iteration. The ML-EM algorithm had a slightly better ROI bias vs. variance trade-off than the other algorithms.

关键词： expectation-maximisation algorithm peripheral region Gray Matter Cerebrum algorithms image reconstruction iterative methods single photon emission computed tomography region of interest

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EM algorithm with Split and Merge Operations for Mixture Models

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Systems and Computers in Japan 2000年第4-6期31卷 1-11页

作者： Ueda, Naonori Nakano, Ryohei NTT Commun. Science Laboratories Kyoto 619-0237 Japan

The maximum-likelihood estimate of a mixture model is usually found by using the EM algorithm. However, the EM algorithm suffers from the local-optimum problem and therefore we cannot obtain the potential performance of mixture models in practice. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split and merge operations using a new criterion for efficiently selecting the split and merge candidates. We apply the proposed algorithm to the training of Gaussian mixtures and the dimensionality reduction based on a mixture of factor analyzers using synthetic and real data and show the effectiveness of using the split and merge operations to improve the likelihood both of the training data and of reserved test data. © 2000 Scripta Technica.

关键词： EM algorithm split and merge operations mixture models mixtures split and merge expectation-maximisation algorithm Mixture models

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Interval censoring

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STATISTICAL METHODS IN MEDICAL RESEARCH 2010年第1期19卷 53-70页

作者： Zhang, Zhigang Sun, Jianguo Univ Missouri Dept Stat Columbia MO 65211 USA Mem Sloan Kettering Canc Ctr Dept Epidemiol & Biostat New York NY 10065 USA

Interval-censored failure time data occur in many medical investigations as well as other studies such as demographical and sociological studies. They include the usual right-censored failure time data as a special case but provide much more complex structure and less relevant information than the right-censored data. This article reviews some basic concepts, issues and the corresponding statistical approaches related to the analysis of interval-censored data as well as recent advances. In particular, we discuss estimation of a survival function, comparison of several treatments and regression analysis as well as competing risks analysis and truncation in the presence of interval censoring. A well-known example of interval-censored data is described and analysed to illustrate some of the statistical procedures discussed..

关键词： FAILURE TIME DATA PROPORTIONAL HAZARDS MODEL MAXIMUM-LIKELIHOOD ESTIMATOR LINEAR TRANSFORMATION MODELS SELF-CONSISTENT ESTIMATOR ODDS REGRESSION-MODEL LOG-RANK TESTS EFFICIENT ESTIMATION EM algorithm MULTIPLE IMPUTATION expectation-maximisation algorithm WELLS regression analysis maximum likelihood estimation complex structure Cutoffs fault time Proportional Hazards Models Arsenic

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Maximum Likelihood Estimation of Multivariate Regime Switching Student-t Copula Models

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INTERNATIONAL STATISTICAL REVIEW 2024年第3期92卷 327-354页

作者： Cortese, Federico P. Pennoni, Fulvia Bartolucci, Francesco Univ Milano Bicocca Dept Econ Management & Stat Via Bicocca Arcimboldi 8 I-20128 Milan Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Via Bicocca Arcimboldi 8 I-20128 Milan Italy Univ Perugia Dept Econ Via A Pascoli 20 I-60123 Perugia Italy

We propose a multivariate regime switching model based on a Student-t$$ t $$ copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two-step procedure carried out through the expectation-maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student-t$$ t $$ copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log-returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.

关键词： copula models cryptocurrencies daily log-returns expectation-maximisation algorithm latent variable models

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Asymptotic Convergence Properties of the EM algorithm for Mixture of Experts

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NEURAL COMPUTATION 2011年第8期23卷 2140-2168页

作者： Yang, Yan Ma, Jinwen Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China

Mixture of experts (ME) is a modular neural network architecture for supervised classification. The double-loop expectation-maximization (EM) algorithm has been developed for learning the parameters of the ME architecture, and the iteratively reweighted least squares (IRLS) algorithm and the Newton-Raphson algorithm are two popular schemes for learning the parameters in the inner loop or gating network. In this letter, we investigate asymptotic convergence properties of the EM algorithm for ME using either the IRLS or Newton-Raphson approach. With the help of an overlap measure for the ME model, we obtain an upper bound of the asymptotic convergence rate of the EM algorithm in each case. Moreover, we find that for the Newton approach as a specific Newton-Raphson approach to learning the parameters in the inner loop, the upper bound of asymptotic convergence rate of the EM algorithm locally around the true solution Theta* is o(e(0.5-epsilon)(Theta*)), where epsilon > 0 is an arbitrarily small number, o(x) means that it is a higher-order infinitesimal as x -> 0, and e(Theta*) is a measure of the average overlap of the ME model. That is, as the average overlap of the true ME model with large sample tends to zero, the EM algorithm with the Newton approach to learning the parameters in the inner loop tends to be asymptotically superlinear. Finally, we substantiate our theoretical results by simulation experiments.

关键词： Converge internal recycling expectation-maximisation algorithm asymptotic

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Blind detection of severely blurred 1D Barcode

Blind detection of severely blurred 1D Barcode

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Dridi, Noura Delignon, Yves Sawaya, Wadih Septier, Francois Univ Lille Nord France Inst TELECOM TELECOM Lille1 LAGISFRE 3303CNRS F-59653 Villeneuve Dascq France

ISBN: (纸本)9781424456383

In this paper, we present a joint blind channel estimation and symbol detection for decoding a blurred and noisy 1D barcode captured image. From an information transmission point of view, we show that the channel impulse response, the noise power and the symbols can be efficiently estimated by taking into account the signal structure such as the cyclostationary property of the hidden Markov process to estimate. Based on the expectation-maximisation method, we show that the new algorithm offers significative performance gain compared to classical ones pushing back the frontiers of the barcode technology.

关键词： blind detection channel estimation channel impulse response expectation-maximisation algorithm expectation-maximisation method hidden Markov models hidden Markov process image denoising image restoration joint blind channel estimation noisy 1D barcode cap

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Dimensionality Reduction for Probabilistic Movement Primitives 14

Dimensionality Reduction for Probabilistic Movement Primitiv...

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14th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Colome, Adria Neumann, Gerhard Peters, Jan Torras, Carme UPC CSIC Inst Robot Inf Ind Barcelona Spain TUD Intelligent Autonomous Syst Lab Darmstadt Germany Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781479971749

Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learning algorithms. However, it is well known that the intrinsic dimensionality of many human movements is small in comparison to the number of employed DoFs, and hence, the movements can be represented by a small number of synergies encoding the couplings between DoFs. In this paper, we want to apply Dimensionality Reduction (DR) to a recent movement representation used in robotics, called Probabilistic Movement Primitives (ProMP). While ProMP have been shown to have many benefits, they suffer with the high-dimensionality of a robotic system as the number of parameters of a ProMP scales quadratically with the dimensionality. We use probablistic dimensionality reduction techniques based on expectation maximization to extract the unknown synergies from a given set of demonstrations. The ProMP representation is now estimated in the low-dimensional space of the synergies. We show that our dimensionality reduction is more efficient both for encoding a trajectory from data and for applying Reinforcement Learning with Relative Entropy Policy Search (REPS).

关键词： entropy expectation-maximisation algorithm humanoid robots learning (artificial intelligence) mobile robots DR ProMP REPS dimensionality reduction expectation maximization humanoid robot probabilistic movement primitives reinforcement learning relative entropy policy search Context Principal component analysis Probabilistic logic Robot kinematics Trajectory Vectors Humanoid robots dimensionality reduction expectation-maximisation algorithm renewable energy power generation Robot kinematics Probabilistic logic learning (artificial intelligence) trajectory Mobile robots DR principal components analysis entropy Movements

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RETRACTED: Neural network based identification of meat spoilage using Fourier-transform infrared spectra (Retracted article. See vol. 272, 2020)

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JOURNAL OF FOOD ENGINEERING 2014年 142卷 118-131页

作者： Kodogiannis, Vassilis S. Kontogianni, Eva Lygouras, John N. Univ Westminster Fac Sci & Technol Computat Intelligence Grp London W1W 6UW England Reg Unit Herakl Gen Directorate Dev GR-71201 Iraklion Greece Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece

To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy (FTIR) with the help of an intelligent-based identification system was attempted in this work. The objective of this study is to associate simultaneously spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Brochothrix thermosphacta, Lactic Acid Bacteria and Enterobacteriaceae. The dual purpose of the proposed modelling scheme is not only to classify meat samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. An Extended Normalised Radial Basis Function neural network has been implemented, and the Bayesian Ying-Yang expectation maximisation algorithm has been utilised together with novel splitting operations to determine network's size and parameter set. The dimensionality reduction of spectral data has been addressed by the implementation of a fuzzy principal component algorithm. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that spectral information obtained by FTIR spectroscopy during beef spoilage, in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Neural networks expectation-maximisation algorithm FTIR Fuzzy principal components analysis Meat spoilage Pattern recognition Modelling

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Image segmentation by a new weighted Student's t-mixture model

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IET IMAGE PROCESSING 2013年第3期7卷 240-251页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

In this study, the authors introduce a new weighted Student's t-mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t-distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t-mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well-known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model-based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.

关键词： expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images

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Parameter estimation of G0 distribution based on improved recursive expectation-maximisation method for clutter modelling

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6759-6762页

作者： Lu, Jiaxin Sun, Yuze Zhuo, Bangsheng Yang, Xiaopeng Beijing Inst Technol Sch Informat & Elect Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation-maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample.

关键词： recursive estimation radar signal processing expectation-maximisation algorithm radar clutter maximum likelihood estimation parameter estimation improved recursive expectation–maximisation method clutter modelling radar signal processing ground clutter radar echoes modelling accuracy model parameter estimation actual data sample expectation step maximisation step recursive method maximum likelihood estimation

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