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Modified student's t-hidden Markov model for pattern recognition and classification

IET SIGNAL PROCESSING 2013年第3期7卷 219-227页

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

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications;however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixture distribution as its hidden state is the Student's t-hidden Markov model (SHMM). The authors propose a novel Student's t-hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation-maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.

关键词： expectation-maximisation algorithm Gaussian processes gradient methods hidden Markov models pattern classification modified student t-hidden Markov model pattern recognition applications pattern classification applications Gaussian mixture models t-mixture distribution latent components standard SHMM scalar exponent regularization component densities emission densities optimal weight parameters gradient method expectation-maximisation algorithm synthetic data real data expectation-maximisation algorithm Gaussian processes gradient methods hidden Markov models pattern classification modified student t-hidden Markov model pattern recognition applications pattern classification applications Gaussian mixture models t-mixture distribution latent components standard SHMM scalar exponent regularization component densities emission densities optimal weight parameters gradient method expectation-maximisation algorithm synthetic data real data

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CapsNet topology to classify tumours from brain images and comparative evaluation

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IET IMAGE PROCESSING 2020年第5期14卷 882-889页

作者： Goceri, Evgin Akdeniz Univ Dept Biomed Engn Antalya Turkey

Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor recognition increases but also sampling efficiency and generalisation capability improves. Therefore, in this work, a Capsule Network (CapsNet) is used to achieve fully automated classification of tumors from brain magnetic resonance images. In this work, prevalent three types of tumors (pituitary, glioma and meningioma) have been handled. The main contributions in this paper are as follows: 1) A comprehensive review on CapsNet based methods is presented. 2) A new CapsNet topology is designed by using a Sobolev gradient-based optimisation, expectation-maximisation based dynamic routing and tumor boundary information. 3) The network topology is applied to categorise three types of brain tumors. 4) Comparative evaluations of the results obtained by other methods are performed. According to the experimental results, the proposed CapsNet based technique can achieve extraction of desired features from image data sets and provides tumor classification automatically with 92.65% accuracy.

关键词： medical image processing image classification neural nets feature extraction brain biomedical MRI gradient methods learning (artificial intelligence) expectation-maximisation algorithm tumours optimisation CapsNet topology brain images brain tissues meningioma ependymoma computer-assisted brain tumour classification techniques Capsule-based neural networks tumour recognition brain magnetic resonance images glioma CapsNet based methods expectation-maximisation based dynamic routing tumour boundary information pituitary Sobolev gradient-based optimisation network topology feature extraction learned features

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Dynamic texture classification using Gumbel mixtures in the complex wavelet domain

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IET IMAGE PROCESSING 2019年第1期13卷 9-14页

作者： Qiao, Yulong Liu, Qiufei Liu, Wenhui Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis. The probability distribution model, which has been used to analysis DT, can describe well the distribution property of signals. Here, the authors introduce the finite mixtures of Gumbel distributions (MoGD) and the corresponding parameter estimation method based on expectation-maximisation algorithm. Then, the authors propose the DT features based on MoGD model for DT classification. Specifically, after decomposing DTs with the dual-tree complex wavelet transform (DT-CWT), the median values of complex wavelet coefficient magnitudes of non-overlapping blocks in detail subbands are modelled with MoGDs. The model parameters are accumulated into a feature vector to describe DT. During the classification, a variational approximation version of the Kullback-Leibler divergence is used to measure the similarity between different DTs. The experimental evaluations on two popular benchmark DT data sets (UCLA and DynTex++) demonstrate the effectiveness of the proposed approach.

关键词： expectation-maximisation algorithm trees (mathematics) image sequences image texture wavelet transforms image classification statistical distributions approximation theory DT features MoGD model DT classification dual-tree complex wavelet DT-CWT complex wavelet coefficient magnitudes nonoverlapping blocks feature vector dynamic texture classification Gumbel mixtures complex wavelet domain image sequence analysis probability distribution model parameter estimation method expectation-maximisation algorithm signal distribution property finite mixtures-of-Gumbel distributions median values benchmark DT data sets UCLA Kullback-Leibler divergence DynTex plus

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Bayesian generalised likelihood ratio test-based multiple antenna spectrum sensing for cognitive radios

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IET COMMUNICATIONS 2013年第18期7卷 2151-2165页

作者： Sedighi, Saeid Taherpour, Abbas Monfared, Shaghayegh S. M. Imam Khomeini Int Univ Dept Elect Engn Qazvin Iran

In this study, the authors address the problem of multiple antenna spectrum sensing in cognitive radios by exploiting the prior information about unknown parameters. Specifically, under assumption that unknown parameters are random with the given proper distributions, the authors use a Bayesian generalised likelihood ratio test (B-GLRT) in order to derive the corresponding detectors for three different scenarios: (i) only the channel gains are unknown to the secondary user (SU), (ii) only the noise variance is unknown to the SU, (iii) both the channel gains and noise variance are unknown to the SU. For the first and third scenarios, the authors use the iterative expectation maximisation algorithm for estimation of unknown parameters and the authors derive their convergence rate. It is shown that the proposed B-GLRT detectors have low complexity and besides are optimal even under the finite number of samples. The simulation results demonstrate that the proposed B-GLRT detectors have an acceptable performance even under the finite number of samples and also outperform the related recently proposed detectors for multiple antenna spectrum sensing.

关键词： antenna arrays Bayes methods cognitive radio convergence of numerical methods expectation-maximisation algorithm radio spectrum management signal denoising signal detection statistical testing wireless channels Bayesian generalised likelihood ratio test cognitive radios multiple antenna spectrum sensing B-GLRT detectors channel gains secondary user noise variance iterative expectation maximisation convergence rate

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Quantitative simultaneous ^99mTc/¹²³I cardiac SPECT using MC-JOSEM

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MEDICAL PHYSICS 2009年第2期36卷 602-611页

作者： Ouyang, Jinsong Zhu, Xuping Trott, Cathryn M. El Fakhri, Georges Massachusetts Gen Hosp Div Nucl Med & Mol Imaging Boston MA 02114 USA Harvard Univ Sch Med Dept Radiol Boston MA 02115 USA

Simultaneous rest Tc-99m-Sestamibi/I-123-BMIPP cardiac SPECT imaging has the potential to replace current clinical Tc-99m-Sestamibi rest/stress imaging and therefore has great potential in the case of patients with chest pain presenting to the emergency department. Separation of images of these two radionuclides is difficult, however, because their emission energies are close. The authors previously developed a fast Monte Carlo (MC)-based joint ordered-subset expectation maximization (JOSEM) iterative reconstruction algorithm (MC-JOSEM), which simultaneously compensates for scatter and cross talk as well as detector response within the reconstruction algorithm. In this work, the authors evaluated the performance of MC-JOSEM in a realistic population of Tc-99m/I-123 studies using cardiac phantom data on a Siemens *** system using a standard cardiac protocol. The authors also compared the performance of MC-JOSEM for estimation tasks to that of two other methods: standard OSEM using photopeak energy windows without scatter correction (NSC-OSEM) and standard OSEM using a Compton-scatter energy window for scatter correction (SC-OSEM). For each radionuclide the authors separately acquired high-count projections of radioactivity in the myocardium wall, liver, and soft tissue background compartments of a water-filled torso phantom, and they generated synthetic projections of various dual-radionuclide activity distributions. Images of different combinations of myocardium wall/background activity concentration ratios for each radionuclide were reconstructed by NSC-OSEM, SC-OSEM, and MC-JOSEM. For activity estimation in the myocardium wall, MC-JOSEM always produced the best relative bias and relative standard deviation compared with NSC-OSEM and SC-OSEM for all the activity combinations. On average, the relative biases after 100 iterations were 8.1% for Tc-99m and 3.7% for I-123 with MC-JOSEM, 39.4% for Tc-99m and 23.7% for I-123 with NSC-OSEM, and 20.9% for Tc-99m with SC-O

关键词： cardiology expectation-maximisation algorithm image reconstruction iodine compounds iterative methods medical image processing Monte Carlo methods muscle organic compounds phantoms single photon emission computed tomography technetium compounds

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Soft shape registration under Lie group frame

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IET COMPUTER VISION 2013年第6期7卷 437-447页

作者： Peng, Yaxin Lin, Wei Ying, Shihui Peng, Jigen Shanghai Univ Sch Sci Dept Math Shanghai 200444 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Inst Informat & Syst Sci Xian 710049 Peoples R China

In this study, the authors address a two-dimensional (2D) shape registration problem on data with anisotropic-scale deformation and noise. First, the model is formulated under the iterative closest point (ICP) framework, which is one of the most popular methods for shape registration. To overcome the effect of noise, the expectation maximisation algorithm is used to improve the model. Then, the structure of Lie groups is adopted to parameterise the proposed model, which provides a unified framework to deal with the shape registration problems. Such representation makes it possible to introduce some suitable constraints to the model, which improves the robustness of the algorithm. Thereby, the 2D shape registration problem is turned to an optimisation problem on the matrix Lie group. Furthermore, a sequence of quadratic programming is designed to approximate the solution for the model. Finally, several comparative experiments are carried out to validate that the authors' algorithm performs well in terms of robustness, especially in the presence of outliers.

关键词： expectation-maximisation algorithm image registration Lie groups quadratic programming quadratic programming matrix Lie group optimisation problem expectation maximisation algorithm ICP framework iterative closest point noise anisotropic-scale deformation 2D shape registration problem Lie group frame soft shape registration

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Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2010年第1期2010卷 1-8页

作者： Boudlal, Abdenaceur Nsiri, Benayad Aboutajdine, Driss Univ Hassan II Ain Chock LPMMAT Fac Sci Ain Chock Casablanca 20100 Morocco Univ Mohammed V Agdal Fac Sci LRIT Lab Associated CNRST Rabat 10080 Morocco

This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.

关键词： expectation-maximisation algorithm Gauss block matching Current Pick Videotapes Peak signal-to-noise ratio Video sequences motion estimation Modeling

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A latent variable regression model for capture-recapture data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第7期53卷 2740-2746页

作者： Thandrayen, Joanne Wang, Yan Univ S Australia Sch Math & Stat Adelaide SA 5000 Australia RMIT Univ Sch Math & Geospatial Sci Melbourne Vic 3000 Australia

Capture-recapture methods are used to estimate the prevalence of diseases in the field of epidemiology. The information used for estimation purposes are available from multiple lists, whereby giving rise to the problems of list dependence and heterogeneity. In this paper, modelling is focused on the heterogeneity part. We present a new binomial latent class model which takes into account both the observed and unobserved heterogeneity within capture-recapture data. We adopt the conditional likelihood approach and perform estimation via the EM algorithm. We also derive the mathematical expressions for the computation of the standard error of the unknown population size. An application to data on diabetes patients in a town in northern Italy is discussed. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Standard error Built environment population size expectation-maximisation algorithm maladjustment Mathematical Formulas Binomial

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Synthetic aperture radar image segmentation with quantum annealing

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IET RADAR SONAR AND NAVIGATION 2024年第5期18卷 812-824页

作者： Presles, Timothe Enderli, Cyrille Burel, Gilles Baghious, El Houssain Thales Def Mission Syst Elancourt France Univ Brest CNRS UMR6285 STICCCyber IOT ChairLab Brest France

In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state-of-the-art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP-hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors' approach on the D-Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems. Among state-of-the-art methods, Markov random fields (MRF) can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a MRF appears to be NP-hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar ***

关键词： expectation-maximisation algorithm image segmentation synthetic aperture radar

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Effects of shortened acquisition time on accuracy and precision of quantitative estimates of organ activity

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MEDICAL PHYSICS 2010年第4期37卷 1807-1815页

作者： He, Bin Frey, Eric C. Cornell Univ Dept Radiol Div Nucl Med New York Presbyterian HospWeill Med Coll New York NY 10021 USA Johns Hopkins Med Inst Russell H Morgan Dept Radiol & Radiol Sci Baltimore MD 21287 USA

Methods: To test this hypothesis, the authors compared the accuracy and variation in accuracy of organ activity estimates obtained from planar and SPECT scans at various count levels. A simulated phantom population with realistic variations in anatomy and biodistribution was used to model variability in a patient population. Planar and SPECT projections were simulated using previously validated Monte Carlo simulation tools. The authors simulated the projections at count levels approximately corresponding to 1.5-30 min of total acquisition time. The projections were processed using previously described quantitative SPECT (QSPECT) and planar (QPlanar) methods. The QSPECT method was based on the OS-EM algorithm with compensations for attenuation, scatter, and collimator-detector response. The QPlanar method is based on the ML-EM algorithm using the same model-based compensation for all the image degrading effects as the QSPECT method. The volumes of interests (VOIs) were defined based on the true organ configuration in the phantoms. The errors in organ activity estimates from different count levels and processing methods were compared in terms of mean and standard deviation over the simulated phantom population. Results: There was little degradation in quantitative reliability when the acquisition time was reduced by half for the QSPECT method (the mean error changed by < 1%, e.g., 0.9%-0.3%=0.6% for the spleen). The magnitude of the errors and variations in errors for large organ with high uptake were still acceptable for 1.5 min scans, even though the errors were slightly larger than those for the 30 min scans (i.e., < 2% for liver, < 3% for heart). The errors over the ranges of scan times studied for the QPlanar method were all within 0.3% for all organs. Conclusions: These data indicate that, for the purposes of organ activity estimation, acquisition times could be reduced at least by a factor of 2 for the QSPECT and QPlanar methods with little effect on the errors

关键词： biomedical measurement expectation-maximisation algorithm Monte Carlo methods phantoms radiation therapy radioisotopes single photon emission computed tomography

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