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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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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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An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第18期62卷 4689-4703页

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

We propose two novel approaches for the recovery of an (approximately) sparse signal from noisy linear measurements in the case that the signal is a priori known to be non-negative and obey given linear equality constraints, such as a simplex signal. This problem arises in, e. g., hyperspectral imaging, portfolio optimization, density estimation, and certain cases of compressive imaging. Our first approach solves a linearly constrained non-negative version of LASSO using the max-sum version of the generalized approximate message passing (GAMP) algorithm, where we consider both quadratic and absolute loss, and where we propose a novel approach to tuning the LASSO regularization parameter via the expectationmaximization (EM) algorithm. Our second approach is based on the sum-product version of the GAMP algorithm, where we propose the use of a Bernoulli non-negative Gaussian-mixture signal prior and a Laplacian likelihood and propose an EM-based approach to learning the underlying statistical parameters. In both approaches, the linear equality constraints are enforced by augmenting GAMP's generalized-linear observation model with noiseless pseudo-measurements. Extensive numerical experiments demonstrate the state-of-the-art performance of our proposed approaches.

关键词： Belief propagation compressed sensing estimation expectation maximization algorithms

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EM scattering from a 2-D target above a 1-D sea surface using GPU based FDTD 4

EM scattering from a 2-D target above a 1-D sea surface usin...

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Conference on High-Performance Computing in Remote Sensing IV

作者： Guo, Lixin Jia, Chungang Yang, Pengju Xidian Univ Sch Phys & Optoelectron Engn Xian Peoples R China

ISBN: (纸本)9781628413106

This paper presents the graphics processor unit (GPU)-based finite difference time domain (FDTD) algorithm to investigate the electromagnetic (EM) scattering from a two-dimensional target above a one-dimensional rough sea surface with the Pierson-Moskowitz spectrum. The proposed method is validated by comparing the numerical results with those obtained through sequential FDTD execution on a central processing unit, as well as through method of moments. Using compute unified device architecture technology, significant speed-up ratios are achieved. Furthermore, the speedup is improved by shared memory and asynchronous transfer.

关键词： Finite-difference time-domain method Scattering Magnetism Composites expectation maximization algorithms Near field Electromagnetic scattering Detection and tracking algorithms Electromagnetism Visualization

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Community detection for fluorescent lifetime microscopy image segmentation

Community detection for fluorescent lifetime microscopy imag...

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Conference on Three-Dimensional and Multidimensional Microscopy - Image Acquisition and Processing XXI

作者： Hu, Dandan Sarder, Pinaki Ronhovde, Peter Achilefu, Samuel Nussinov, Zohar Washington Univ Dept Phys One Brookings DrCampus Box 1105 St Louis MO 63130 USA Washington Univ Sch Med Dept Radiol St Louis MO 63110 USA

ISBN: (纸本)9780819498625

Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes. 1 In the current paper, we further explore this method in FLT images of e x vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1 : 25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

关键词： Image segmentation Fluorescence lifetime imaging Near infrared 3D image processing Critical dimension metrology expectation maximization algorithms Microscopy Image processing algorithms and systems Tissues Image resolution

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Prospects and challenges in the electromagnetic follow-up of LIGO-Virgo gravitational wave transients 5

Prospects and challenges in the electromagnetic follow-up of...

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Conference on Observatory Operations - Strategies, Processes, and Systems V

作者： Vitale, Salvatore MIT LIGO Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819496171

The kilometer-scale ground based gravitational wave (GW) detectors, LIGO and Virgo, are being upgraded to their advanced configurations. We expect the two LIGO observatories to undertake a 3 month science run in 2015 with a limited sensitivity. Virgo should come online in 2016, and join LIGO for a 6 month science run. Through a sequence of science runs and commissioning periods, the final sensitivity should be reached by similar to 2019. LIGO and Virgo are expected to deliver the first direct detection of gravitational wave transients in the next few years. Most of the known sources of GWs targeted by LIGO and Virgo will likely be luminous in the electromagnetic (EM) spectrum as well. Compact binary coalescences are thought to be progenitors of short gamma-ray bursts, while long gamma-ray bursts are likely to be associated with core collapse supernova. A joint detection of gravitational and EM radiation may help confirm these associations, and expand our understanding of those astrophysical systems. Due to the transient nature, a search for the EM counterparts to GW events should be done with the shortest latency. In this paper we describe the EM follow-up program of Advanced LIGO and Virgo, from the search for GWs to the production of sky maps. Furthermore, we quantify the expected sky localization errors in the first two years of operation of the advanced detectors network.

关键词： LIGO Sensors Signal to noise ratio Electromagnetism Gamma radiation Observatories Interferometers expectation maximization algorithms Signal detection Stars

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Characterization of computer network events through simultaneous feature selection and clustering of intrusion alerts

Characterization of computer network events through simultan...

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Conference on Multisensor, Multisource Information Fusion - Architectures, algorithms, and Applications

作者： Chen, Siyue Leung, Henry Dondo, Maxwell Univ Calgary Calgary AB Canada Def R&D Canada Ottawa ON Canada

ISBN: (纸本)9781628410587

As computer network security threats increase, many organizations implement multiple Network Intrusion Detection Systems (NIDS) to maximize the likelihood of intrusion detection and provide a comprehensive understanding of intrusion activities. However, NIDS trigger a massive number of alerts on a daily basis. This can be overwhelming for computer network security analysts since it is a slow and tedious process to manually analyse each alert produced. Thus, automated and intelligent clustering of alerts is important to reveal the structural correlation of events by grouping alerts with common features. As the nature of computer network attacks, and therefore alerts, is not known in advance, unsupervised alert clustering is a promising approach to achieve this goal. We propose a joint optimization technique for feature selection and clustering to aggregate similar alerts and to reduce the number of alerts that analysts have to handle individually. More precisely, each identified feature is assigned a binary value, which reflects the feature's saliency. This value is treated as a hidden variable and incorporated into a likelihood function for clustering. Since computing the optimal solution of the likelihood function directly is analytically intractable, we use the expectation Maximisation (EM) algorithm to iteratively update the hidden variable and use it to maximize the expected likelihood. Our empirical results, using a labelled Defense Advanced Research Projects Agency (DARPA) 2000 reference dataset, show that the proposed method gives better results than the EM clustering without feature selection in terms of the clustering accuracy.

关键词： expectation maximization algorithms Feature selection Computer networks Data modeling Sensors Autoregressive models Data transmission Chlorine Network security Computer security

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Further developments in addressing depth-variant 3D fluorescence microscopy imaging

Further developments in addressing depth-variant 3D fluoresc...

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Conference on Three-Dimensional and Multidimensional Microscopy - Image Acquisition and Processing XXI

作者： Ghosh, Sreya Schaefer, Lutz Schuster, Dietwald Preza, Chrysanthe Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA Adv Imaging Methodol Consultat Kitchener ON Canada Regensburg Univ Appl Sci Regensburg Germany

ISBN: (纸本)9780819498625

Three-dimensional (3D) imaging with optical sectioning microscopy uses computational methods to obtain the true fluorescence distribution by ameliorating the effect of defocus, spherical aberration and noise. Inverse algorithms improve image quality at a fraction of the cost of implementing an optical system by accurate modeling of the imaging system. Good inverse imaging algorithms need to be accurate as well as fast. Better understanding of the image formation model is vital to obtain improved restoration through model-based algorithms. Forward imaging models based on a depth-varying point-spread function (DV-PSF) leads to a substantial improvement in the resulting images because it accounts for depth-induced aberrations present in the imaging system. PSFs at every layer can be represented using their principal components. Computation of the forward imaging model using a principal component analysis (PCA) representation of the DV-PSF requires fewer convolutions than a strata based approach investigated in the past. In this paper we present a new algorithm for maximum likelihood image restoration developed based on a PCA representation of the DV-PSF and an accelerated conjugate gradient (CG) iteration scheme. Results obtained with this PCA-CG algorithm from both simulated and experimental fluorescence microscope data are discussed and compared with results obtained from a CG iteration method based on the strata model and linear interpolation of the DV-PSF. The performance of the PCA-CG algorithms is shown to be promising for practical applications.

关键词： Point spread functions Principal component analysis 3D image processing expectation maximization algorithms 3D modeling Image restoration Algorithm development Computer simulations Luminescence Reconstruction algorithms

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Statistical Shape and Appearance Models without One-to-One Correspondences

Statistical Shape and Appearance Models without One-to-One C...

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Conference on Medical Imaging - Image Processing

作者： Ehrhardt, Jan Krueger, Julia Handels, Heinz Med Univ Lubeck Inst Med Informat Lubeck Germany

ISBN: (纸本)9780819498274

One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.(1) proposed an alternative method using correspondence probabilities instead of exact one-to-one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices.

关键词： Image segmentation Data modeling Statistical modeling Process modeling Lung Visual process modeling expectation maximization algorithms Image registration Shape analysis Optimization (mathematics)

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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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