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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Rafiee, G. Dlay, S. S. Woo, W. L. Newcastle Univ Dept Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9781479900152

Unsupervised extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. In this paper, we propose an efficient unsupervised segmentation solution for this problem. The proposed approach which is based on ensemble clustering and graph-cut modeling aims to extract meaningful focused regions from a given image at two stages. In the first stage, a novel two-level based ensemble clustering technique is developed to classify image blocks into three constituent classes. As a result, object and background blocks are extracted. By considering certain pixels of object and background blocks as seeds, a constraint is provided for the next stage of the approach. In stage two, a minimal graph cuts is constructed by utilizing the max-flow method and using object and background seeds. Experimental results demonstrate that the proposed approach achieves an average F-measure of 91.7% and is computationally up to 2 times faster than existing unsupervised approaches.

关键词： Ensemble clustering expectation-maximization algorithm interest regions segmentation graph-cut optimization low depth-of-field

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Estimating Conditional Probabilities for the Detection of Unfavorable Copy Number Alterations in a Targeted Therapy

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第10期60卷 2933-2942页

作者： Hsu, Fang-Han Dougherty, Edward R. Chen, Yidong Serpedin, Erchin Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Translat Genom Res Inst TGEN Phoenix AZ 85004 USA Univ Texas Hlth Sci Ctr San Antonio Dept Epidemiol & Biostat San Antonio TX 78229 USA Univ Texas Hlth Sci Ctr San Antonio Greehey Childrens Canc Res Inst San Antonio TX 78229 USA

Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment;however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters M-score and R-value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation-maximization (EM) algorithm is developed for estimating CPs, M-scores, and R-values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.

关键词： Bayesian network copy number drug response expectation-maximization algorithm gene expression

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Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm

Estimation of cortical multivariate autoregressive models fo...

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5th IEEE International Symposium on Biomedical Imaging

作者： Cheung, Bing Leung Patrick Van Veen, Barry D. Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

ISBN: (纸本)9781424420025

A new method for estimating multivariate autoregressive (MWAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in terms of a hidden state equation describing MVAR cortical signal evolution and an observation equation that relates the hidden state to the surface measurements. We develop an expectation-maximization (EM) algorithm to find maximum likelihood estimates of the MVAR model parameters. Simulations show that this one-step approach performs significantly better than the conventional two-step approach at estimating the cortical signals and detecting functional connectivity between different cortical regions.

关键词： cortical connectivity multivariate autoregressive model expectation-maximization algorithm Granger causality partial directed coherence

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Gaussian mixture model clustering with completed likelihood minimum message length criterion

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Journal of Southeast University(English Edition) 2013年第1期29卷 43-47页

作者：曾洪卢伟宋爱国东南大学仪器科学与工程学院南京210096 南京农业大学工学院南京210031

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

关键词： Gaussian mixture model non-Gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion

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A NEW MULTILEVEL THRESHOLDING APPROACH BASED ON THE ANT COLONY SYSTEM AND THE EM algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第1期9卷 319-337页

作者： Liang, Yun-Chia Yin, Yueh-Chuan Yuan Ze Univ Dept Ind Engn & Management 135 Yuan Tung Rd Chungli 320 Taoyuan County Taiwan

Thresholding, an important approach for image segmentation, is the first step in the image processing for many industrial applications. The efficiency and effectiveness of the thresholding method is the key to the success of the consecutive process steps. This study proposed an optimization algorithm (named as AOE) combining parametric and non-parametric approaches. An ant colony system (ACS-Otsu) algorithm considers the non-parametric objective between-class variance while the expectation maximization (EM) algorithm focuses on the parametric objective overall fitting error of probability distributions. Since the performance of the EM method is sensitive to the initial solution, the ACS-Otsu algorithm is employed as a robust and efficient initialization strategy. Experimental results of the nine test images show that the AOE algorithm is efficient and effective in the multilevel thresholding problems. Comparisons between the AOE algorithm and the PSO+EM algorithm in the literature also verify that the AOE algorithm not only provides competitive thresholding results but also outperforms PSO+EM in computational expense.

关键词： Image segmentation Multilevel thresholding expectation-maximization algorithm Ant colony system

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MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN

MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONA...

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European Signal Processing Conference

作者： Roland Badeau Alexey Ozerov Institut Mines-Telecom Telecom ParisTech CNRS LTCI Technicolor Research & Innovation

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for representing mixtures of non-stationary signals in the time-frequency domain, and we highlighted its capability to both reach a high spectral resolution and reconstruct high quality audio signals. An expectation-maximization (EM) algorithm was also proposed for estimating its parameters. In this paper, we replace the maximization step by multiplicative update rules (MUR), in order to improve the convergence rate. We also introduce general MUR that are not limited to nonnegative parameters, and we propose a new insight into the EM algorithm, which shows that MUR and EM actually belong to the same family. We thus introduce a continuum of algorithms between them. Experiments confirm that the proposed approach permits to overcome the convergence rate of the EM algorithm.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Multiplicative update rules

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A Generic Statistical Approach for Emission Computed Tomography Reconstruction

A Generic Statistical Approach for Emission Computed Tomogra...

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IEEE International Conference on Intelligent Computer Communication and Processing

作者： Anca Ciurte Sergiu Nedevschi Ioan Rasa Department of Computer Science Technical University of Cluj-Napoca Department of Mathematics Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

Nowadays, nuclear imaging is increasingly used for non-invasive diagnosis. The image modalities in nuclear imaging suffer of worse statistics, in comparison with computed tomography, since they are based on emission transition tomography. Thus, precise reconstruction methods that can deal with incomplete or missing measurements are needed in order to improve the quality of nuclear images. In this paper we present a generalization of the state of the art EMML and ISRA algorithms for emission computed tomography reconstruction. The proposed method was tested and validated in comparison with the mentioned state of the art methods on a set of synthetic data. Better results (in terms of speed of convergence) were obtained for certain parameter settings.

关键词： expectation-maximization algorithm Image reconstruction Positron emission tomography Kullback- Leibler distances Log-likelihood functions Least-squares Linear systems

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VARIATIONAL BAYESIAN EM algorithm FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

VARIATIONAL BAYESIAN EM ALGORITHM FOR MODELING MIXTURES OF N...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Roland Badeau Angelique Dremeau Institut Mines-Telecom Telecom ParisTech CNRS LTCI

ISBN: (纸本)9781479903573

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Variational inference expectation-maximisation algorithm Variational Inference matrix decomposition audio signals signals mixtures Signal timing Modeling Source separation Spectral resolution model parameters Audio nonstationary random signal

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Application of time-frequency analysis for automatic hidden corrosion detection in a multilayer aluminum structure using pulsed eddy current

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NDT & E INTERNATIONAL 2012年 47卷 70-79页

作者： Hosseini, Saleh Lakis, Aouni A. Ecole Polytech Sect Appl Mech Dept Mech Engn Montreal PQ H3T1J4 Canada

Pulsed eddy current (PEC) is a non-destructive testing method used to detect corrosion and cracks in multilayer aluminum structures which are typically found in aircraft applications. Corrosion and metal loss in thin multi-layer structures are complex and variable phenomena that diminish the reliability of pulsed eddy current measurements. In this article, pulsed eddy current signals are processed to improve the accuracy and reliably of these measurements. PEC's results (time domain data) are converted by time-frequency analysis (Rihaczek distribution) to represent data in three dimensions. The time-frequency approach generates a large amount of data. Principal component analysis is applied as feature extraction to reduce redundant data to provide new features for classifiers. K-means clustering and expectation-maximization are applied to classify data and automatically determine corrosion distribution in each layer. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multi-layer aluminum structures Pulsed eddy current Time-frequency analysis Feature extraction K-means clustering expectation-maximization algorithm

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Accelerated Life Tests of a Series System With Masked Interval Data Under Exponential Lifetime Distributions

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IEEE TRANSACTIONS ON RELIABILITY 2012年第3期61卷 798-808页

作者： Fan, Tsai-Hung Hsu, Tsung-Ming Natl Cent Univ Grad Inst Stat Jhongli 32001 Taiwan

We will discuss the reliability analysis of a series system under accelerated life tests when interval data are observed, while the components are assumed to have statistically independent exponential lifetime distributions. In a series system, the system fails if any of the components fails. It is common to include masked data in which the component that causes failure of the system is not observed. First, we apply the maximum likelihood approach via the expectation-maximization algorithm, and use the parametric bootstrap method for the standard error estimation. When the proportion of the masking data is high, the maximum likelihood approach fails due to lack of information. A Bayesian approach is an appropriate alternative in such a case. Hence, we also study the Bayesian approach incorporated with a subjective prior distribution with the aid of the Markov chain Monte Carlo method. We derive statistical inference on the model parameters, as well as the mean lifetimes, and the reliability functions of the system and components. The proposed method is illustrated through a numerical example simulated from the underlying model under various masking levels.

关键词： Accelerated life tests expectation-maximization algorithm interval data Markov chain Monte Carlo masked data parametric bootstrap series system

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