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检索条件"主题词=expectation maximization algorithms"

共 128 条记录，以下是101-110 订阅

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Comparison of several algorithms for blind deconvolution. Analysis of noise effects

Comparison of several algorithms for blind deconvolution. An...

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Conference on Optics in Atmospheric Propagation and Adaptive Systems

作者： Lanteri, H Barilli, M Beaumont, H Aime, C Gaucherel, P Touma, H UNIV NICE SOPHIA ANTIPOLIS DEPT ASTROPHYSURA 709 CNRSF-06108 NICE 02FRANCE

ISBN: (纸本)0819419443

The object of this communication is to compare two inversion algorithms in their application to the blind deconvolution problem. After a brief summary of the previous works in this field, we describe the Richardson-Lucy and the steepest descent algorithms and we introduce these methods in the basic error reduction algorithm of Ayers and Dainty. These algorithms are compared when used for blind deconvolution of simulated binary objects convolved by a point spread function and corrupted by a Gaussian additive noise. We consider the effects of the noise level on the reconstruction error, together with the effects of the algorithmic parameters (inner and outer iteration numbers). Particular effects occurring during the reconstruction process are also shown.

关键词： Reconstruction algorithms Deconvolution Point spread functions expectation maximization algorithms Image processing Convolution Image acquisition Computer simulations Error analysis Algorithm development

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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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STATISTICAL INTENSITY CORRECTION AND SEGMENTATION OF MRI DATA

STATISTICAL INTENSITY CORRECTION AND SEGMENTATION OF MRI DAT...

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3rd Conference on Visualization in Biomedical Computing 1994 (VBC 94)

作者： WELLS, WM GRIMSON, WEL KIKINIS, R JOLESZ, FA HARVARD UNIV SCH MEDBOSTONMA 02115

ISBN: (纸本)0819416983

Many applications of MRI are facilitated by segmenting the volume spanned by the imagery into the various tissue types that are present. Intensity-based classification of MR images has proven to be problematic, even when advanced techniques such as non- parametric multi-channel methods are used. A persistent difficulty has been accommodating the spatial intensity inhomogeneities that are due to the equipment. This paper describes a statistical method that uses knowledge of tissue properties and intensity inhomogeneities to correct for these intensity inhomogeneities. Use of the expectation-maximization algorithm leads to a method (EM segmentation) for simultaneously estimating tissue class and the correcting gain field. The algorithm iterates two components to convergence: tissue classification, and gain field estimation. The result is a powerful new technique for segmenting and correcting MR images. An implementation of the method is discussed, and results are reported for segmentation of white matter and gray matter in gradient-echo and spin-echo images. Examples are shown for axial, coronal and sagittal (surface coil) images. For a given type of acquisition, intensity variations across patients, scans, and equipment have been accommodated without manual intervention in the segmentation. In this sense, the method is fully automatic for segmenting healthy brain tissue. An accuracy assessment was made in which the method was compared to manual segmentation, and to a method based on supervised multi-variate classification, in segmenting white matter and gray matter. The method was found to be consistent with manual segmentation, and closer to manual segmentation than the supervised method.

关键词： Image segmentation Tissues expectation maximization algorithms Magnetic resonance imaging Brain Linear filtering Neuroimaging Data modeling Image classification Statistical analysis

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BAYESIAN IMAGE-RECONSTRUCTION WITH VARIABLE BALANCING PARAMETER 8

BAYESIAN IMAGE-RECONSTRUCTION WITH VARIABLE BALANCING PARAME...

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8th SPIE Conference on Instrumentation in Astronomy

作者： NUNEZ, J LLACER, J UNIV BARCELONA DEPT ASTRON & METEOROLE-08028 BARCELONASPAIN

ISBN: (纸本)081941493X

This paper describes the Bayesian image reconstruction algorithm with entropy prior with space variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of high and low signal/noise ratio, thus avoiding the large residuals present in algorithms that use a constant balancing parameter. The space variant hyperparameter determines the relative weight between the prior information and the likelihood defining the degree of smoothness of the solution. To compute the variable hyperparameter we used a segmentation technique based on artificial neural networks of the Self- Organizing Map type. Using this technique we segmented the image in 25 regions and computed a different value of the hyperparameter for each one. We applied the method to the Hubble Space Telescope Cameras and to ground based CCD data.

关键词： Image segmentation Reconstruction algorithms Image processing algorithms and systems Stars expectation maximization algorithms Image restoration Algorithm development Point spread functions Artificial neural networks Cameras

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ADAPTIVE EDGE-PRESERVING REGULARIZATION FOR PET IMAGE-RECONSTRUCTION

ADAPTIVE EDGE-PRESERVING REGULARIZATION FOR PET IMAGE-RECONS...

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Conference on Image Processing

作者： FANG, M KAO, CM SINGH, A SIEMENS CORP RES INC PRINCETONNJ 08536

ISBN: (纸本)081941462X

We describe an adaptive regularization scheme and show how to incorporate it into either the Algebraic Reconstruction Technique (ART) or Maximum Likelihood-expectation maximization (ML-EM) based algorithms for reconstruction of Positron Emission Tomography (PET) images. We demonstrate through qualitative and quantitative experiments that the adaptive regularization technique effectively reduces the noise level in the image, while preserving the fine details of the edge structures in the image. The technique does not introduce any visible artifacts during reconstruction.

关键词： Reconstruction algorithms Image filtering Image processing Positron emission tomography Digital filtering expectation maximization algorithms Head Image restoration Nonlinear filtering Image quality

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MAXIMUM-LIKELIHOOD METHOD FOR PROBABILISTIC MULTI-HYPOTHESIS TRACKING

MAXIMUM-LIKELIHOOD METHOD FOR PROBABILISTIC MULTI-HYPOTHESIS...

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Conference on Signal and Data Processing of Small Targets 1994

作者： STREIT, RL LUGINBUHL, TE USN CTR UNDERSEA WARFARE CTRDIV NEWPORTNEWPORTRI 02841

ISBN: (纸本)0819415391

In a multi-target multi-measurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; instead, the probability that each measurement belongs to each track is estimated using a maximum likelihood algorithm derived by the method of expectation-maximization. These measurement-to-track probability estimates are intrinsic to the multi-target tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. Unlike MHT algorithms, the PMHT algorithm does not maintain explicit hypothesis lists. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning.

关键词： Detection and tracking algorithms Silicon expectation maximization algorithms Algorithm development Error analysis Process modeling Filtering (signal processing) algorithms Environmental sensing Information operations

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LEARNING THE IDENTITY WITH BASIC MORPHOLOGICAL OPERATORS (POTATO PEELER ALGORITHM) 5

LEARNING THE IDENTITY WITH BASIC MORPHOLOGICAL OPERATORS (PO...

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Conference on Image Algebra and Morphological Image Processing V

作者： MATTIOLI, J SCHMITT, M THOMSON CSF LCRF-91404 ORSAYFRANCE

Let f denote an image and F a morphological operator depending on a set of parameters {mj}. The purpose of this paper is to find a set of parameters which satisfies the equation F(f,m) equals f where F is one of the t... 详细信息

ISBN: (纸本)081941624X

Let f denote an image and F a morphological operator depending on a set of parameters {m_j}. The purpose of this paper is to find a set of parameters which satisfies the equation F(f,m) equals f where F is one of the two basic morphological operator: erosion or dilation. It is obvious that there exists a trivial solution m of f equals F(f,m), which is given by m(x) equals 0 if x equals 0 and m(x) equals -(infinity) otherwise. Our approach is to consider this problem as an optimization problem: `find the best set of parameters which minimizes the error between the desired image f and the output filtered image F(f,m)'. Among various possible approaches, we have chosen a specific one introduced by Ph. Salembier for adaptative structuring elements. In our problem, we prove that Salembier algorithms always converge toward a solution distinct from m.

关键词： Chemical elements Image filtering Americium expectation maximization algorithms Fourier transforms Lithium Neodymium On-screen displays Plutonium Stochastic processes

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ESTIMATION OF SHAPE MIXTURE BY GRANULOMETRIC METHODS 5

ESTIMATION OF SHAPE MIXTURE BY GRANULOMETRIC METHODS

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Conference on Image Algebra and Morphological Image Processing V

作者： SAND, F DOUGHERTY, ER ROCHESTER INST TECHNOL CTR IMAGING SCIROCHESTERNY 14623

ISBN: (纸本)081941624X

It is known that if a binary random image is composed of a disjoint union of translates of i.i.d. randomly scaled homothetics of an arbitrary compact set, then, for any convex, compact granulometric generator, the granulometric moments are asymptotically normal and there exist asymptotic representations of the moments of the granulometric moments. The present paper extends the asymptotic theory to a random image composed of a disjoint union of translates of scaled homothetics of a finite collection of compact primitives (shapes) under the condition that the mixture proportions of the shapes are known and fixed. Grain sizing is independent, with the sizings for each primitive being identically distributed. Based on this new granulometric structure theorem for mixed grain images, an estimation method is proposed that estimates the mixture proportions from estimates of the granulometric-moment means derived from running the granulometry on realizations of the mixed process. The granulometric mixture estimation is compared to maximum-likelihood estimation.

关键词： expectation maximization algorithms Image processing Statistical analysis Binary data Image analysis Image classification Americium Computer science Estimation theory Mathematics

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COMPARISON BETWEEN ML-EM AND MODIFIED NEWTON algorithms FOR SPECT IMAGE-RECONSTRUCTION

COMPARISON BETWEEN ML-EM AND MODIFIED NEWTON ALGORITHMS FOR ...

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CONF ON PHYSIOLOGICAL IMAGING, SPECTROSCOPY, AND EARLY-DETECTION DIAGNOSTIC METHODS

作者： NOUMEIR, R MAILLOUX, GE MALLOUCHE, H LEMIEUX, R Ecole Polytechnique de Montreal (Canada) Hopital du Sacre-Coeur de Montreal (Canada)

ISBN: (纸本)0819411140

The expectation maximization method for maximum likelihood image reconstruction (ML- EM) is one of the most popular algorithms used in SPECT and PET, because it is based on the realistic assumption that photon emission and counts follow a Poisson process. Moreover, this method retains two important theoretical and practical properties namely nonnegativity and self-normalization of the reconstructed image. This latter property means that the number of emitted photons is equal to the number of counts. However, the major disadvantage of this method is the large amount of computation that is required, due to its slow rate of convergence. In this paper, we demonstrate that the ML-EM algorithm is a special case of the modified Newton method and can thus be accelerated by multiplying at each iteration the changes to the image, as calculated by the standard algorithm, by an overrelaxation parameter. This accelerated ML-EM algorithm can further be optimally accelerated, and converges to a good maximum likelihood estimator.

关键词： expectation maximization algorithms Reconstruction algorithms Single photon emission computed tomography Image restoration Image processing Photon counting Tomography Photodetectors Positron emission tomography Stochastic processes

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IMAGE-RECONSTRUCTION FOR 3-D LIGHT-MICROSCOPY WITH A REGULARIZED LINEAR METHOD INCORPORATING A SMOOTHNESS PRIOR

IMAGE-RECONSTRUCTION FOR 3-D LIGHT-MICROSCOPY WITH A REGULAR...

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CONF ON BIOMEDICAL IMAGE PROCESSING AND BIOMEDICAL VISUALIZATION

作者： PREZA, C MILLER, MI CONCHELLO, JA Washington Univ. (United States)

ISBN: (纸本)0819411388

We have shown that the linear least-squares (LLS) estimate of the intensities of a 3-D object obtained from a set of optical sections is unstable due to the inversion of small and zero-valued eigenvalues of the point-spread function (PSF) operator. The LLS solution was regularized by constraining it to lie in a subspace spanned by the eigenvectors corresponding to a selected number of the largest eigenvalues. In this paper we extend the regularized LLS solution to a maximum a posteriori (MAP) solution induced by a prior formed from a 'Good's like' smoothness penalty. This approach also yields a regularized linear estimator which reduces noise as well as edge artifacts in the reconstruction. The advantage of the linear MAP (LMAP) estimate over the current regularized LLS (RLLS) is its ability to regularize the inverse problem by smoothly penalizing components in the image associated with small eigenvalues. Computer simulations were performed using a theoretical PSF and a simple phantom to compare the two regularization techniques. It is shown that the reconstructions using the smoothness prior, give superior variance and bias results compared to the RLLS reconstructions. Encouraging reconstructions obtained with the LMAP method from real microscopical images of a 10 micrometers fluorescent bead, and a four-cell Volvox embryo are shown.

关键词： Point spread functions 3D image processing Microscopy Inverse optics expectation maximization algorithms Signal to noise ratio Computer simulations Fourier transforms Image processing Image restoration

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