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Spline-regularized ordered subsets expectation-maximization algorithms for emission computed tomography

JOURNAL OF ELECTRONIC IMAGING 2003年第1期12卷 17-24页

作者： Lee, SJ Paichai Univ Dept Elect Engn Taejon 302735 South Korea

We describe ordered subsets (OS) algorithms applied to regularized expectation-maximization (EM) algorithms for emission tomography. Our reconstruction algorithms are based on a maximum a posteriori approach, which allows us to incorporate a priori information in the form of a regularizer to stabilize the unstable EM algorithm. In this work, we use two-dimensional smoothing splines as regularizers. Our motivation for using such regularizers stems from the fact that, by relaxing the requirement of imposing significant spatial discontinuities and using instead quadratic smoothing splines, solutions are easier to compute and hyperparameter calculation becomes less of a problem. To optimize our objective function, we use the method of iterated conditional modes, which is useful for obtaining convenient closed-form solutions. In this case, step sizes or line-search algorithms necessary for gradient-based descent methods are also avoided. We finally accelerate the resulting algorithm using the OS principle and propose a principled way of scaling smoothing parameters to retain the strength of smoothing for different subset numbers. Our experimental results indicate that our new methods provide quantitatively robust results as well as a considerable acceleration. (C) 2003 SPIE and IST.

关键词： expectation maximization algorithms Reconstruction algorithms Algorithm development Computed tomography Standards development Tin Tomography Mathematical modeling Sensors Positron emission tomography

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Review of identification technologies for stray animal governance 5

Review of identification technologies for stray animal gover...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Han, Puze College of Science and Engineering University of Minnesota Twin Cities Minneapolis55455 United States

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing serious threats to human health and the environment. Conventional manual methods for counting stray animals face challenges in terms of efficiency and the risk of disease transmission. With technological advancements, image recognition, and sound identification, among other techniques, have emerged as crucial tools to address this issue. Image recognition, leveraging intuitive statistics based on external features, combined with the low-power attributes of sound identification and the health assessment capabilities of thermal imaging, collectively provide comprehensive technological support for stray animal population statistics. In the realm of image algorithms, both traditional target detection algorithms and deep learning methods such as RCNN and Faster RCNN employ convolutional neural networks to accurately identify and locate stray animals. Regarding sound algorithms, traditional Gaussian mixture models and hidden Markov models, as well as deep learning techniques involving convolutional neural networks, have effectively enhanced the accuracy of stray animal sound recognition. The integration of image and audio in a hybrid method significantly enhances stray animal monitoring. Employing advanced techniques in video tracking and sound recognition, this approach offers an efficient and practical solution, crucial for wildlife ecosystem surveillance and conservation. Research indicates that the application of deep learning methods in the domains of image and sound has significantly advanced compared to traditional approaches. In terms of image processing, I utilized the YOLO algorithm to perform grid division, feature extraction, and loss computation steps to achieve stray animal detection, demonstrating outstanding performance. Through the application of t

关键词： Animals Detection and tracking algorithms Object detection Animal model studies Image processing Education and training Diseases and disorders Positron emission tomography Deep learning expectation maximization algorithms

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expectation-maximization Gaussian-Mixture Approximate Message Passing

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013年第19期61卷 4658-4672页

作者： Vila, Jeremy P. Schniter, Philip Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was a priori known, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, the distribution is unknown, motivating the use of robust algorithms like LASSO-which is nearly minimax optimal-at the cost of significantly larger MSE for non-least-favorable distributions. As an alternative, we propose an empirical-Bayesian technique that simultaneously learns the signal distribution while MMSE-recovering the signal-according to the learned distribution-using AMP. In particular, we model the non-zero distribution as a Gaussian mixture and learn its parameters through expectation maximization, using AMP to implement the expectation step. Numerical experiments on a wide range of signal classes confirm the state-of-the-art performance of our approach, in both reconstruction error and runtime, in the high-dimensional regime, for most (but not all) sensing operators.

关键词： Compressed sensing belief propagation expectation maximization algorithms Gaussian mixture model

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IMAGE IDENTIFICATION AND RESTORATION BASED ON THE expectation-maximization ALGORITHM

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OPTICAL ENGINEERING 1990年第5期29卷 436-445页

作者： LAY, KT KATSAGGELOS, AK Northwestern Univ. (USA) Consultant (Taiwan)

Abstract not available

关键词： expectation maximization algorithms Radiometric corrections

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Spatially Aware expectation maximization (SpAEM): Application to Prostate TRUS Segmentation

Spatially Aware Expectation Maximization (SpAEM): Applicatio...

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Conference on Medical Imaging - Image Processing

作者： Orooji, Mahdi Sparks, Rachel Bloch, B. Nicolas Feleppa, Ernest Barratt, Dean Madabhushi, Anant Case Western Reserve Univ Ctr Computat Imaging & Personalized Diagnost Cleveland OH 44106 USA UCL Ctr Med Image Comp London England Boston Uni Boston Med Ctr Dept Radiol Boston MA USA Lizzi Ctr Biomed Engn Riverside Res Riverside CA USA

ISBN: (纸本)9780819498274

In this paper we introduce Spatially Aware expectation maximization (SpAEM), a new parameter estimation method which incorporates information pertaining to spatial prior probability into the traditional expectation-maximization framework. For estimating the parameters of a given class, the spatial prior probability allows us to weight the contribution of any pixel based on the probability of that pixel belonging to the class of interest. In this paper we evaluate SpAEM for the problem of prostate capsule segmentation in transrectal ultrasound (TRUS) images. In cohort of 6 patients, SpAEM qualitatively and quantitatively outperforms traditional FM in distinguishing the foreground (prostate) from background (non-prostate) regions by around 45% in terms of the Sorensen Dice overlap measure, when compared against expert annotations. The variance of the estimated parameters measured via Cramer-Rao Lower Bound suggests that SpAEM yields unbiased estimates. Finally, on a synthetic TRUS image, the Cramer-Von Mises (CVM) criteria shows that SpAEM improves the estimation accuracy by around 51% and 88% for prostate and background, respectively, as compared to traditional EM.

关键词： Prostate Image segmentation expectation maximization algorithms Ultrasonography Feature extraction Image filtering Image processing Statistical analysis Cancer Medical imaging

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Attraction-repulsion expectation-maximization algorithm for image processing and sensor field networks

Attraction-repulsion expectation-maximization algorithm for ...

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Conference on Visual Communications and Image Processing 2008

作者： Hong, Hunsop Schonfeld, Dan Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9780819469946

An attraction-repulsion expectation-maximization (AREM) algorithm for density estimation is proposed in this paper. We introduce a Gibbs distribution function for attraction and inverse Gibbs distribution for repulsion as an augmented penalty function in order to determine equilibrium between over-smoothing and over-fitting. The logarithm of the likelihood function augmented the Gibbs density mixture is solved under expectation-maximization (EM) method. We demonstrate the application of the proposed attraction-repulsion expectation-maximization algorithm to image reconstruction and sensor field estimation problem using computer simulation. We show, that the proposed algorithm improves the performance considerably.

关键词： expectation maximization algorithms Statistical analysis Sensors Reconstruction algorithms Autoregressive models Image restoration Signal to noise ratio Image sensors Image processing Image analysis

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BLUR IDENTIFICATION AND IMAGE-RESTORATION WITH THE expectation maximization ALGORITHM 4

BLUR IDENTIFICATION AND IMAGE-RESTORATION WITH THE EXPECTATI...

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CONF ON PROPAGATION ENGINEERING

作者： DURACK, DL U.S. Army Atmospheric Sciences Lab. and New Mexico State Univ. (United States)

ISBN: (纸本)0819405965

The iterative expectation-maximization (EM) algorithm for identifying unknown blur, noise, and image parameters under Gaussian modeling assumptions is described. A new form of the equations updating parameter estimates is given, from which convergence conditions and symmetry properties of the parameter estimates are derived. The frequency domain resolution defined by the digital image is not appropriate for accurate parameter estimation. Instead, a version of the EM algorithm with frequency resolution appropriate for the blur point spread function (PSF) is proposed. Results are presented from a test of the reduced resolution algorithm, in which the importance of the initial PSF is studied.

关键词： expectation maximization algorithms Point spread functions Image restoration Turbulence Digital imaging Optical transfer functions Image resolution Statistical analysis Diffraction Signal to noise ratio

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SIGNAL-PROCESSING WITH RADIAL BASIS FUNCTION NETWORKS USING expectation maximization ALGORITHM CLUSTERING

SIGNAL-PROCESSING WITH RADIAL BASIS FUNCTION NETWORKS USING ...

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CONF ON ADAPTIVE SIGNAL PROCESSING

作者： UKRAINEC, A HAYKIN, S McMaster Univ. (Canada)

ISBN: (纸本)0819406937

Several researchers have investigated the usefulness ofradial basis function (RBF) neural networks for prediction of difficult (chaotic) time series.18 This paper demonstrates adaptive unsupervised / supervised learning procedures which improve on the RBF network performance in terms of generalization, and final mean-square error. After a description of the RBF network architecture, the learning of the network is discussed with comments on the advantages and disadvantages of various approaches. Unsupervised clustering of RBF centres has been used by researchers6'° to overcome some of the disadvantages in supervised learning, and to improve the performance of the RBF network while keeping the number of basis functions relatively low. A maximum likelihood solution for unsupervised clustering called the expectation maximization (EM) algorithm has recently been used'° to estimate the parameters of the RBF hidden layer units. The input density is modeled as a mixture of component gaussian distributions. The estimated parameters of the mixture density are then transplanted into the RBF network, after which supervised learning of the network takes place. An extension to input—output space clustering is shown to be superior. Although papers have been written on input space clustering for RBF networks, this paper examines why the extended metric clustering should be a superior methodology. Two examples, a chaotic time series predictor and a cross—polarization canceller, illustrate the performance of the algorithms. The EM algorithm with extended metric clustering method achieves superior performance on signal processing problems by creating a better hidden layer representation in areas of the input space where samples are more likely to be jointly located.

关键词： expectation maximization algorithms Signal processing Network architectures Radar Associative arrays Machine learning Detection and tracking algorithms Digital filtering Nonlinear optics Polarization

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Parallel implementation of the maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm for positron emission tomography (PET) images in a visual language

Parallel implementation of the maximum likelihood-expectatio...

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Hybrid Image and Signal Processing IV

作者： Bastiaens, Koen Lemahieu, Ignace Univ. of Gent (Belgium)

ISBN: (纸本)0819415421

Due to its iterative nature, the execution of the maximum likelihood expectation maximization (ML-EM) reconstruction algorithm requires a long computation time. To overcome this problem, multiprocessor machines could be used. In this paper, a parallel implementation of the algorithm for positron emission tomography (PET) images is presented. To cope with the difficulties involved with parallel programming a programming environment based on a visual language has been used.

关键词： Positron emission tomography Scanners Reconstruction algorithms expectation maximization algorithms Visualization Sensors Computer programming Visual programming languages Image processing Photons

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The matrix ridge approximation: algorithms and applications

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MACHINE LEARNING 2014年第3期97卷 227-258页

作者： Zhang, Zhihua Shanghai Jiao Tong Univ Dept Comp Sci & Engn MOE Microsoft Key Lab Intelligent Comp & Intellig Shanghai 200240 Peoples R China

We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods. We discuss an approximation approach that we call matrix ridge approximation. In particular, we define the matrix ridge approximation as an incomplete matrix factorization plus a ridge term. Moreover, we present probabilistic interpretations using a normal latent variable model and a Wishart model for this approximation approach. The idea behind the latent variable model in turn leads us to an efficient EM iterative method for handling the matrix ridge approximation problem. Finally, we illustrate the applications of the approximation approach in multivariate data analysis. Empirical studies in spectral clustering and Gaussian process regression show that the matrix ridge approximation with the EM iteration is potentially useful.

关键词： Positive semidefinite matrices Matrix ridge approximation Incomplete matrix factorization expectation maximization algorithms Probabilistic models

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