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Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

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PATTERN RECOGNITION 2013年第10期46卷 2685-2699页

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

In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOE) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Low depth-of-field Difference of Gaussian method Ensemble clustering expectation-maximization algorithm Region-of-interest extraction

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An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第23期74卷 10605-10619页

作者： Masmoudi, Atef Puech, William Masmoudi, Afif Univ Sfax Sfax Preparatory Engn Inst Sfax Tunisia Univ Montpellier 2 LIRMM UMR CNRS 5506 F-34392 Montpellier 05 France Univ Sfax Fac Sci Sfax Lab Stat & Probabil Sfax Tunisia

In this paper, we propose a new approach for a block-based lossless image compression using finite mixture models and adaptive arithmetic coding. Conventional arithmetic encoders encode and decode images sample-by-sample in raster scan order. In addition, conventional arithmetic coding models provide the probability distribution for whole source symbols to be compressed or transmitted, including static and adaptive models. However, in the proposed scheme, an image is divided into non-overlapping blocks and then each block is encoded separately by using arithmetic coding. The proposed model provides a probability distribution for each block which is modeled by a mixture of non-parametric distributions by exploiting the high correlation between neighboring blocks. The expectation-maximization algorithm is used to find the maximum likelihood mixture parameters in order to maximize the arithmetic coding compression efficiency. The results of comparative experiments show that we provide significant improvements over the state-of-the-art lossless image compression standards and algorithms. In addition, experimental results show that the proposed compression algorithm beats JPEG-LS by 9.7 % when switching between pixel and prediction error domains.

关键词： Arithmetic coding Lossless compression Image compression Finite mixture model expectation-maximization algorithm Kullback-Leibler distance

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Road sign classification using Laplace kernel classifier

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PATTERN RECOGNITION LETTERS 2000年第13-14期21卷 1165-1173页

作者： Paclík, P Novovicová, J Pudil, P Somol, P Czech Tech Univ Fac Transportat Sci Prague 11000 Czech Republic Acad Sci Czech Republ Inst Informat Theory & Automat Dept Pattern Recognit CR-18208 Prague 8 Czech Republic Univ Econ Acad Sci Joint Lab Jindrichuv Hradec Czech Republic Univ Econ Fac Management Jindrichuv Hradec Czech Republic

Driver support systems (DSS) of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method. To maximize the pseudo-likelihood function, an expectation-maximization algorithm is used. The algorithm has been tested on a dataset with more than 4900 noisy images. A comparison to other classification methods is also given. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： road sign recognition kernel density estimation expectation-maximization algorithm

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Prediction analysis for microbiome sequencing data

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BIOMETRICS 2019年第3期75卷 875-884页

作者： Wang, Tao Yang, Can Zhao, Hongyu Shanghai Jiao Tong Univ Dept Bioinformat & Biostat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Jiao Tong Univ SJTU Yale Joint Ctr Biostat & Data Sci Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Math Kowloon Hong Kong Peoples R China Yale Univ Dept Biostat New Haven CT USA

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension-reduction structure in the model. An efficient Monte Carlo expectation-maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.

关键词： expectation-maximization algorithm log ratios metagenomic data model-based dimension reduction multinomial-logit regression

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A semiparametric isotonic regression model for skewed distributions with application to DNA-RNA-protein analysis

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BIOMETRICS 2022年第4期78卷 1464-1474页

作者： Wang, Chenguang Yuan, Ao Cope, Leslie Qin, Jing Johns Hopkins Univ Sidney Kimmel Comprehens Canc Ctr Baltimore MD USA Georgetown Univ Dept Biostat Bioinformat & Biomath Washington DC USA NIAID Bethesda MD 20852 USA

In this paper, we propose a semiparametric regression model that is built upon an isotonic regression model with the assumption that the random error follows a skewed distribution. We develop an expectation-maximization algorithm for obtaining the maximum likelihood estimates of the model parameters, examine the asymptotic properties of the estimators, conduct simulation studies to explore the performance of the proposed model, and apply the method to evaluate the DNA-RNA-protein relationship and identify genes that are key factors in tumor progression.

关键词： expectation-maximization algorithm isotonic regression maximum likelihood estimation skew normal

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Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 69卷 173-183页

作者： Fu, Liyong Wang, Mingliang Lei, Yuancai Tang, Shouzheng Chinese Acad Forestry Res Inst Forest Resource Informat Tech Beijing 100091 Peoples R China Univ Georgia Warnell Sch Forestry & Nat Resources Athens GA 30602 USA

Multi-level nonlinear mixed effects (ML-NLME) models have received a great deal of attention in recent years because of the flexibility they offer in handling the repeated-measures data arising from various disciplines. In this study, we propose both maximum likelihood and restricted maximum likelihood estimations of ML-NLME models with two-level random effects, using first order conditional expansion (FOCE) and the expectation-maximization (EM) algorithm. The FOCE EM algorithm was compared with the most popular Lindstrom and Bates (LB) method in terms of computational and statistical properties. Basal area growth series data measured from Chinese fir (Cunninghamia lanceolata) experimental stands and simulated data were used for evaluation. The FOCE EM and LB algorithms given the same parameter estimates and fit statistics for models that converged by both. However, FOCE EM converged for all the models, while LB did not, especially for the models in which two-level random effects are simultaneously considered in several base parameters to account for between-group variation. We recommend the use of FOCE EM in ML-NLME models, particularly when convergence is a concern in model selection. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Cunninghamia lanceolata expectation-maximization algorithm First order conditional expansion Lindstrom and Bates algorithm Simulated data Two-level nonlinear mixed effects models

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ExomeHMM: A Hidden Markov Model for Detecting Copy Number Variation Using Whole-Exome Sequencing Data

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CURRENT BIOINFORMATICS 2017年第2期12卷 147-155页

作者： Guo, Cheng Yu, Zhenhua Wang, Minghui Li, Ao Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Res Ctr Biomed Engn Hefei Anhui Peoples R China

Background: Copy number variations (CNVs), including amplification and deletion, are alterations of DNA copy number compared to a reference genome. CNVs play a crucial role in tumourigenesis and progression, including amplification of oncogenes and deletion of tumor suppressor genes that may significantly increase the risk of cancer. CNVs are also reported to be closely related with non-cancer diseases, such as Down syndrome, Parkinson disease, and Alzheimer disease. Objective: Whole-exome sequencing (WES) has been successfully applied to the discovery of gene mutations as well as clinical diagnosis. But it is quite challenging to evaluate the copy number using WES data due to read depth bias, exons' distribution pattern and normal cell contamination. Our aim is develop an efficient method to overcome these challenges and detect CNVs using WES data. Method: In this study, we present ExomeHMM, a hidden Markov model (HMM) based CNV detecting algorithm. ExomeHMM exploits relative read depth, a ratio based signal, to mitigate read depth distortion and employs exponential attenuated transition matrix to handle sparsely and non-uniformly distributed exons. expectation-maximization algorithm is used to optimize parameters for the proposed model. Finally, we use standard Viterbi algorithm to infer the copy number of exons. Results: Using previously identified CNVs in 1000 Genome Project data as golden standard, ExomeHMM achieves the highest F-score among the four methods compared in this study. When applied to triple-negative breast cancer data, ExomeHMM is capable to find abnormal genes that are significantly associated with breast cancer. Conclusion: In conclusion, ExomeHMM is a suitable tool for CNV detections in both healthy samples as well as clinic tumor samples on whole-exome sequencing data.

关键词： Copy number variation expectation-maximization algorithm hidden Markov model next generation sequencing viterbi algorithm whole-exome sequencing

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Stomped-t: A novel probability distribution for rough-probabilistic clustering

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INFORMATION SCIENCES 2017年 421卷 104-125页

作者： Banerjee, Abhirup Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India

Rough clustering is one of the principal research areas in data mining, machine learning, pattern recognition, and bioinformatics. Among different variants of rough clustering, rough-probabilistic clustering is a new concept introduced recently. In rough-probabilistic clustering, a class is defined as the union of two disjoint regions, namely, a crisp lower approximation region and a probabilistic boundary region. In this regard, stomped normal (SN) distribution provides a statistical modeling of the data set in rough-probabilistic clustering framework. The SN distribution models the central tendency, dispersion, and width of the lower approximation region of each class using its mean, variance, and width parameter, respectively. However, it does not take into consideration the property of kurtosis of the class distribution, which controls the concentration of values around mean and shape of the tail of data distribution. In this background, a novel probability distribution, named stomped-t (St) distribution, is introduced in the paper for rough-probabilistic clustering. The proposed probability distribution incorporates the property of kurtosis into the SN framework. The proposed St probability distribution is then integrated within the rough-probabilistic clustering framework for precise and robust clustering of the data. The efficacy of the proposed clustering algorithm is demonstrated for unsupervised data clustering and image segmentation problems, along with a comparative performance analysis with related algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Rough-probabilistic clustering Stomped normal distribution Image segmentation Hidden Markov random field model expectation-maximization algorithm

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Semiparametric time-to-event modeling in the presence of a latent progression event

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BIOMETRICS 2017年第2期73卷 463-472页

作者： Rice, John D. Tsodikov, Alex Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method;its use in practice is demonstrated in the analysis of a prostate cancer data set.

关键词： expectation-maximization algorithm Semiparametric methods Survival analysis

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Item selection by latent class-based methods: an application to nursing home evaluation

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2016年第2期10卷 245-262页

作者： Bartolucci, Francesco Montanari, Giorgio E. Pandolfi, Silvia Univ Perugia Dept Econ I-06100 Perugia Italy Univ Perugia Dept Polit Sci I-06100 Perugia Italy

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items to their patients. In such a context, the latent class model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to a dataset collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in Italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis, with respect to different specifications of the initial subset of items, and of a resampling procedure.

关键词： Bayesian information criterion expectation-maximization algorithm Polytomous items Quality-of-life ULISSE project

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