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Robust generative asymmetric GMM for brain MR image segmentation

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 151卷 123-138页

作者： Ji, Zexuan Xia, Yong Zheng, Yuhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci CMCC Xian 710072 Shaanxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Background and objectives: Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. Method: First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. Results: To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity i

关键词： Gaussian mixture model Asymmetric distribution expectation-maximization algorithm Brain MR image segmentation Markov random fields Intensity inhomogeneity

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A hybrid EM approach to spatial clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第5期50卷 1188-1205页

作者： Hu, TM Sung, SY Natl Univ Singapore Dept Comp Sci Singapore 117543 Singapore

Spatial clustering requires consideration of spatial information and this makes expectation-maximization (EM) algorithm that maximizes likelihood alone inappropriate. Although neighborhood EM (NEM) algorithm incorporates a spatial penalty term, it needs much more iterations for E-step. To incorporate spatial information while avoiding much additional computation, we propose a hybrid EM (HEM) approach that combines EM and NEM. Early training is performed via a selective hard EM till the penalized likelihood criterion begins to decrease. Then training is turned to NEM, which runs only one iteration of E-step and plays a role of finer tuning. Thus spatial information is incorporated throughout HEM and the computational complexity is also comparable to EM. Empirical results show that a few more passes are needed in HEM to converge after switching to NEM and the final clustering quality is close to or slightly better than standard NEM. (C) 2005 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithm spatial clustering Gaussian mixture spatial penalty term

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Sparse Variational Bayesian SAGE algorithm With Application to the Estimation of Multipath Wireless Channels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第8期59卷 3609-3623页

作者： Shutin, Dmitriy Fleury, Bernard H. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Aalborg Univ Dept Elect Syst Sect Nav & Commun DK-9220 Aalborg Denmark

In this paper, we develop a sparse variational Bayesian (VB) extension of the space-alternating generalized expectation-maximization (SAGE) algorithm for the high resolution estimation of the parameters of relevant multipath components in the response of frequency and spatially selective wireless channels. The application context of the algorithm considered in this contribution is parameter estimation from channel sounding measurements for radio channel modeling purpose. The new sparse VB-SAGE algorithm extends the classical SAGE algorithm in two respects: i) by monotonically minimizing the variational free energy, distributions of the multipath component parameters can be obtained instead of parameter point estimates and ii) the estimation of the number of relevant multipath components and the estimation of the component parameters are implemented jointly. The sparsity is achieved by defining parametric sparsity priors for the weights of the multipath components. We revisit the Gaussian sparsity priors within the sparse VB-SAGE framework and extend the results by considering Laplace priors. The structure of the VB-SAGE algorithm allows for an analytical stability analysis of the update expression for the sparsity parameters. This analysis leads to fast, computationally simple, yet powerful, adaptive selection criteria applied to the single multipath component considered at each iteration. The selection criteria are adjusted on a per-component-SNR basis to better account for model mismatches, e. g., diffuse scattering, calibration and discretization errors, allowing for a robust extraction of the relevant multipath components. The performance of the sparse VB-SAGE algorithm and its advantages over conventional channel estimation methods are demonstrated in synthetic single-input-multiple-output (SIMO) time-invariant channels. The algorithm is also applied to real measurement data in a multiple-input-multiple-output (MIMO) time-invariant context.

关键词： expectation-maximization algorithm MIMO multipath channels SAGE algorithm variational Bayesian methods

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MODELING AND CLASSIFICATION OF DEATHS DUE TO COVID-19 BASED ON MACHINE LEARNING TECHNIQUE

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THERMAL SCIENCE 2023年第1期27卷 405-410页

作者： Alharbi, Randa Univ Tabuk Fac Sci Dept Stat Tabuk Saudi Arabia

Statistical classification is recently considered one of the most important and most common methods in machine learning models and consists of building mod-els that define the target of research interest. There are many classification methods that can be used to predict the value of a response. In this article, we are interested in machine learning applications to classify the new deaths due to Covid-19. Under consideration BIC criterion, the experimental results have shown that the E (Equal variance) with four is the best mixture model. The con-vergence in the algorithm of expectation-maximization is satisfied after 167 itera-tions. The World Health Organization has presented the source of data over the period of March 2, 2020 to August 5, 2020.

关键词： expectation-maximization algorithm machine learning modeling classification Gaussian mixtures model prediction

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Reducing the uncertainties in the achievable vehicle performance targets through design optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第9期228卷 1061-1083页

作者： Yuen, Tey Jing Ramli, Rahizar Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia

In this paper an alternative method is proposed for defining the suspension performance targets through the use of full-vehicle modelling consisting of a ride model and a handling model. These models are derived with the use of a non-linear damper, suspension kinematic characteristics and basic vehicle dimensions. The vehicle performances can be explored using the design-of-experiments method. The non-sorting method is then employed to sort for non-dominated solutions, where these samples represent the Pareto front of the vehicle performances in ride comfort and handling. The k-means clustering method is used to classify further the solution into different unique optimum characteristics. The expectation-maximization algorithm is developed to compute the allowable variance of design parameters required to achieve the specific optimum design targets. This method can be a very useful tool in the earliest design stages where vehicle data are inadequate. This methodology potentially reduces the uncertainty in the achievable vehicle performance targets by allowing engineers to compare the optimum limit of the suspension with those of benchmark vehicles in the early suspension design and development process

关键词： Ride model handling model k-means clustering non-sorting method design-of-experiments method expectation-maximization algorithm suspension design

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Are attitudes toward immigration changing in Europe? An analysis based on latent class IRT models

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2022年第2期16卷 235-271页

作者： Genge, Ewa Bartolucci, Francesco Univ Econ Dept Econ & Financial Anal Bogucicka 3a St PL-40228 Katowice Poland Univ Perugia Dept Econ Via A Pascoli 20 I-06123 Perugia Italy

We analyze the changing attitudes toward immigration in EU host countries in the last few years (2010-2018) on the basis of the European Social Survey data. These data are collected by the administration of a questionnaire made of items concerning different aspects related to the immigration phenomenon. For this analysis, we rely on a latent class approach considering a variety of models that allow for: (1) multidimensionality;(2) discreteness of the latent trait distribution;(3) time-constant and time-varying covariates;and (4) sample weights. Through these models we find latent classes of Europeans with similar levels of immigration acceptance and we study the effect of different socio-economic covariates on the probability of belonging to these classes for which we provide a specific interpretation. In this way we show which countries tend to be more or less positive toward immigration and we analyze the temporal dynamics of the phenomenon under study.

关键词： Discrete latent variables European Social Survey expectation-maximization algorithm Item response theory

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Segmentation of trabeculated structures using an anisotropic Markov random field: Application to the study of the optic nerve head in glaucoma

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2006年第3期25卷 245-255页

作者： Grau, V Downs, JC Burgoyne, CF Louisiana State Univ Hlth Sci Ctr LSU Eye Ctr New Orleans LA 70112 USA Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA

The study of the architecture of the optic nerve head (ONH) may provide valuable information about the development and progression of glaucoma. To this end, we have generated three-dimensional datasets from monkey eyes under controlled intraocular pressure (IOP). Segmentation of the connective tissues in this area is crucial to obtain an accurate measurement of geometrical parameters and to build mechanical models. However, this segmentation is made difficult by the complicated geometry and the artifacts introduced in the dataset building process. We present a novel segmentation algorithm, based on expectation-maximization, which incorporates an anisotropic Markov random field (MRF) to introduce prior knowledge about the geometry of the structure. The structure tensor is used to characterize the predominant structure direction and the spatial coherence at each point. The algorithm, which has been validated on an artificial validation dataset that mimics our ONH datasets, shows significant improvement over an isotropic MRF. Results on the real datasets demonstrate the ability of the new algorithm to obtain accurate, spatially consistent segmentations of this structure.

关键词： expectation-maximization algorithm hidden Markov models image segmentation optic nerve head

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Automatic detection of wakefulness and rest intervals in actigraphic signals: A data-driven approach

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MEDICAL ENGINEERING & PHYSICS 2014年第12期36卷 1585-1592页

作者： Martin-Martinez, D. Casaseca-de-la-Higuera, P. Andres-de-Llano, J. M. Garmendia-Leiza, J. R. Alberola-Lopez, S. Alberola-Lopez, C. Univ Valladolid Lab Proc Imagen Valladolid Spain Univ West Scotland Audio Visual Commun & Networking Res Ctr Paisley Renfrew Scotland

Actigraphy is an useful tool for evaluating the activity pattern of a subject;activity registries are usually processed by first splitting the signal into its wakefulness and rest intervals and then analyzing each one in isolation. Consequently, a preprocessing stage for such a splitting is needed. Several methods have been reported to this end but they rely on parameters and thresholds which are manually set based on previous knowledge of the signals or learned from training. This compromises the general applicability of this methods. In this paper we propose a new method in which thresholds are automatically set based solely on the specific registry to be analyzed. The method consists of two stages: (1) estimation of an initial classification mask by means of the expectation maximization algorithm and (2) estimation of a final refined mask through an iterative method which re-estimates both the mask and the classifier parameters at each iteration step. Results on real data show that our methodology outperforms those so far proposed and can be more effectively used to obtain derived sleep quality parameters from actigraphy registries. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Actigraphy Wakefulness/Rest detection Probability density function expectation-maximization algorithm

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A numerical method for resolving finite mixtures of three-dimensional inter-site distance distributions measured by single-molecule localization microscopy

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MICROSCOPY 2023年第1期72卷 43-48页

作者： Tu, Zhiwen Song, Congwei Qin, Xianan Zhejiang Sci Tech Univ Natl Engn Lab Textile Fiber Mat & Proc Technol Hangzhou 310018 Peoples R China Yanqi Lake Beijing Inst Math Sci & Applicat BIMSA Beijing 101408 Peoples R China Zhejiang Sci Tech Univ Sch Mat Sci & Engn Hangzhou 310018 Peoples R China Zhejiang Prov Innovat Ctr Adv Text Technol Shaoxing 312000 Peoples R China

The three-dimensional (3D) inter-site distance can be measured by single-molecule localization microscopy. Existing theories and analysis tools for 3D inter-site distance measurement only consider the simplest case where all measured distances are from an identical 3D Rician distribution. There are many problems where the 3D inter-site distance measurement result is made up of multiple components, for example, the measurement of intramolecular distances of deoxyribonucleic acid with multiple possible conformations. In these cases, the overall distance distributions become finite mixtures of 3D Rician distributions (or 3D Rician mixtures). Here, we provide a numerical method using the 3D Rician mixture model to resolve the finite 3D inter-site distance mixtures, which is based on the expectation-maximization algorithm. The proposed method has been tested on simulation data of finite 3D inter-site distance mixtures. The result using the Gaussian mixture model in the developed method is also discussed for comparison.

关键词： distance measurement inter-site distance finite mixture model expectation-maximization algorithm single-molecule localization microscopy

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Multiphase learning for an interval-based hybrid dynamical system

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2005年第11期E88A卷 3022-3035页

作者： Kawashima, H Matsuyama, T Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

This paper addresses the parameter estimation problem of an interval-based hybrid dynamical system (interval system). The interval system has a two-layer architecture that comprises a finite state automaton and multiple linear dynamical systems. The automaton controls the activation timing of the dynamical systems based on a stochastic transition model between intervals. Thus, the interval system can generate and analyze complex multivariate sequences that consist of temporal regimes of dynamic primitives. Although the interval system is a powerful model to represent human behaviors such as gestures and facial expressions, the learning process has a paradoxical nature: temporal segmentation of primitives and identification of constituent dynamical systems need to be solved simultaneously. To overcome this problem, we propose a multiphase parameter estimation method that consists of a bottom-up clustering phase of linear dynamical systems and a refinement phase of all the system parameters. Experimental results show the method can organize hidden dynamical systems behind the training data and refine the system parameters successfully.

关键词： hybrid dynamical system interval transition system identification clustering of dynamical systems expectation-maximization algorithm

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